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langchain.prompts import PromptTemplate\r\n\r\nfrom .config import Config\r\nfrom .extensions import llm\r\nfrom .templates import get_function_prompt_template, get_class_prompt_template\r\n\r\n\r\ndef generate_function_docstring(function_code: str, config: Config) -> str:\r\n prompt_formatted_str: str = get_function_prompt_template(\r\n function_code=function_code, config=config\r\n )\r\n function_and_docstring = llm.invoke(prompt_formatted_str)\r\n return function_and_docstring\r\n\r\n\r\ndef generate_class_docstring(class_code: str, config: Config) -> str:\r\n prompt_formatted_str: str = get_class_prompt_template(\r\n class_code=class_code, config=config\r\n )\r\n class_and_docstring = llm.invoke(prompt_formatted_str)\r\n return class_and_docstring\r\n\r\n\r\ndef get_class_docstring(class_and_docstring: str) -> str:\r\n \"\"\"Get the class docstring.\"\"\"\r\n class_tree = ast.parse(class_and_docstring)\r\n for node in class_tree.body:\r\n if isinstance(node, ClassDef):\r\n cls_docstring: str = ast.get_docstring(node)\r\n return cls_docstring\r\n"} {"file_name": "e286ef37-43ae-4ea0-a320-f3c0db52aa93.png", "code": "\r\n\r\ndef get_class_methods_docstrings(class_and_docstring: str) -> dict[str, str]:\r\n \"\"\"Get a class methods docstrings.\"\"\"\r\n class_methods: dict[str, str] = {}\r\n class_tree = ast.parse(class_and_docstring)\r\n for node in class_tree.body:\r\n if isinstance(node, ClassDef):\r\n for class_node in node.body:\r\n if isinstance(class_node, FunctionDef):\r\n class_methods[class_node.name] = ast.get_docstring(class_node)\r\n return class_methods\r\n\r\n\r\ndef make_docstring_node(docstr: str):\r\n constant_str: Constant = Constant(docstr)\r\n return Expr(value=constant_str)\r\n\r\n\r\ndef get_function_docstring(function_and_docstring: str) -> str:\r\n \"\"\"Get the function docstring.\"\"\"\r\n function_tree = ast.parse(function_and_docstring)\r\n for node in function_tree.body:\r\n if isinstance(node, (FunctionDef, AsyncFunctionDef)):\r\n function_docstring: str = ast.get_docstring(node)\r\n return function_docstring\r\n\r\n\r\ndef get_module_source_code(module_path: str) -> str:\r\n \"\"\"Get the source code for a given module.\"\"\"\r\n with open(module_path, 'r') as f:\r\n return f.read()\r\n\r\n\r\ndef add_module_code_to_queue(module_path: str, module_source_queue: Queue):\r\n module_src: str = ''\r\n if module_path:\r\n module_src = get_module_source_code(module_path)\r\n module_source_queue.put((module_path, module_src))\r\n\r\n"} {"file_name": "d366abcb-6eb7-4687-b945-d4f1dc31550c.png", "code": "\r\ndef add_module_to_queue(module_path: str, module_path_queue: Queue):\r\n module_path_queue.put(module_path)\r\n\r\n\r\ndef get_node_source(node, module_src: str) -> str:\r\n return ast.get_source_segment(source=module_src, node=node)\r\n\r\n\r\ndef get_functions_source(module_path: str) -> list[str]:\r\n functions_src: list[str] = []\r\n module_src = get_module_source_code(module_path)\r\n module_tree = ast.parse(module_src)\r\n for node in module_tree.body:\r\n if isinstance(node, FunctionDef):\r\n function_src: str = get_node_source(node=node, module_src=module_src)\r\n functions_src.append((node.name, function_src))\r\n return functions_src\r\n\r\n\r\ndef get_class_source(module_path: str) -> None:\r\n class_src: list[str] = []\r\n module_src = get_module_source_code(module_path)\r\n module_tree = ast.parse(module_src)\r\n for node in module_tree.body:\r\n if isinstance(node, ClassDef):\r\n classsrc: str = get_node_source(node=node, module_src=module_src)\r\n class_src.append((node.name, classsrc))\r\n return class_src\r\n\r\n\r\nclass DirectoryIterator:\r\n def __init__(self, config: Config):\r\n self.config: Config = config\r\n self.queue: deque[str] = deque(self.config.path)\r\n\r\n def __iter__(self) -> Iterator:\r\n return self\r\n\r\n def __next__(self) -> list[str]:\r\n"} +{"file_name": "3b833383-0903-4dcc-803d-a9544e6f9419.png", "code": "from dotenv import load_dotenv\r\nload_dotenv()\r\nimport chainlit as cl\r\nfrom farm_agent.agents import agent\r\nfrom farm_agent.utils import load_model, evaluate_image\r\nfrom PIL import Image\r\nimport io\r\n\r\n\r\nuser_location: str = None\r\nuser_name: str = None\r\nwelcome_text: str = \"\"\"\r\nHello there. This is an application that helps farmers monitor the health level of their crops. \r\nStart by giving me your name and location, then upload an image of your crops. I will analyze it to \r\ndetermine the diasease or pest that affects it and then tell you how to deal with the pest or \r\ndisease and where to purchase pesticides or fungicides.\r\n\"\"\"\r\n\r\n@cl.on_chat_start\r\nasync def start():\r\n cl.user_session.set(\"agent\", agent)\r\n await cl.Message(content=welcome_text).send()\r\n user_name = await cl.AskUserMessage(content=\"What is your name?\", timeout=120).send()\r\n user_location = await cl.AskUserMessage(content=\"Where are you from?\", timeout=120).send()\r\n res = await cl.AskActionMessage(\r\n content=\"Would you like to determine if your crops are infected by a disease or by pests?\",\r\n actions=[\r\n cl.Action(name=\"Check for diseases\", value=\"diseases\", label=\"\u2705 Check for diseases\"),\r\n cl.Action(name=\"Check for Pests\", value=\"pests\", label=\"\u274c Check for Pests\")\r\n ]\r\n ).send()\r\n if res and res.get(\"value\") == \"diseases\":\r\n files = None\r\n # Wait for the user to upload a file\r\n while files == None:\r\n files = await cl.AskFileMessage(\r\n content=f\"{user_name['content']}, start by uploading an image of your crop.\", \r\n accept=[\"image/jpeg\", \"image/png\", \"image/jpg\"]\r\n ).send()\r\n # Decode the file\r\n"} +{"file_name": "526fcd80-8089-4971-a1fe-bd631e6b2c0d.png", "code": " image_file = files[0]\r\n image_data = image_file.content # byte values of the image\r\n image = Image.open(io.BytesIO(image_data))\r\n model = load_model()\r\n predicted_label, predictions = evaluate_image(image, model)\r\n analysis_text: str = f\"\"\"\r\n After analyzing the image you uploaded, here is what I found:\r\n Maize Leaf Rust probability: {predictions['Maize Leaf Rust']}%\r\n Northern Leaf Blight probability: {predictions['Northern Leaf Blight']}%\r\n Healthy probability: {predictions['Healthy']}%\r\n Gray Leaf Spot probability: {predictions['Gray Leaf Spot']}%\r\n Your plant is most likely infected with {predicted_label}.\r\n \"\"\"\r\n elements = [\r\n cl.Image(\r\n name=\"image2\", display=\"inline\", content=image_data\r\n ), \r\n cl.Text(name=\"simple_text\", content=analysis_text, display=\"inline\", size='large')\r\n ]\r\n await cl.Message(content=f\"Maize image with {predicted_label}!\", elements=elements).send()\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.run('Tell me some facts about the maize disease leaf rust especially in relation to kenya.')\r\n await msg.update()\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.run(f'Tell me some facts about the maize disease {predicted_label} especially in relation to kenya.')\r\n await msg.update()\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.run(f'Get me aggrovets in {user_location}, Kenya')\r\n await msg.update()\r\n await cl.Message(content='Feel free to ask me more questions about maize plant diseases and how to deal with them.').send()\r\n else:\r\n await cl.Message(content='Currently cannot detect pests. Still working on that model.').send()\r\n \r\n\r\n@cl.on_message\r\nasync def main(message: cl.Message):\r\n"} +{"file_name": "f833d9f4-af90-45e3-9b4d-6b43e5961859.png", "code": " agent = cl.user_session.get(\"agent\")\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.invoke({\"input\": message.content})[\"output\"]\r\n await msg.update()"} +{"file_name": "7e9c2245-8acb-4b72-b3e4-70ece66a164e.png", "code": "from dotenv import load_dotenv\r\nload_dotenv()\r\nimport chainlit as cl\r\n\r\nfrom calendar_assistant.usecases.agent import agent_executor\r\n\r\n\r\n@cl.on_message\r\nasync def main(message: cl.Message):\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent_executor.invoke({\"input\": message.content})['output']\r\n await msg.update()"} +{"file_name": "3c9d983f-7c0a-4244-857a-c14647cc8432.png", "code": "from dotenv import load_dotenv\r\nload_dotenv()\r\nimport chainlit as cl\r\nfrom farm_agent.agents import agent\r\nfrom farm_agent.utils import load_model, evaluate_image\r\nfrom PIL import Image\r\nimport io\r\n\r\n\r\nuser_location: str = None\r\nuser_name: str = None\r\nwelcome_text: str = \"\"\"\r\nHello there. This is an application that helps farmers monitor the health level of their crops. \r\nStart by giving me your name and location, then upload an image of your crops. I will analyze it to \r\ndetermine the diasease or pest that affects it and then tell you how to deal with the pest or \r\ndisease and where to purchase pesticides or fungicides.\r\n\"\"\"\r\n\r\n@cl.on_chat_start\r\nasync def start():\r\n cl.user_session.set(\"agent\", agent)\r\n await cl.Message(content=welcome_text).send()\r\n user_name = await cl.AskUserMessage(content=\"What is your name?\", timeout=120).send()\r\n user_location = await cl.AskUserMessage(content=\"Where are you from?\", timeout=120).send()\r\n res = await cl.AskActionMessage(\r\n content=\"Would you like to determine if your crops are infected by a disease or by pests?\",\r\n actions=[\r\n cl.Action(name=\"Check for diseases\", value=\"diseases\", label=\"\u2705 Check for diseases\"),\r\n cl.Action(name=\"Check for Pests\", value=\"pests\", label=\"\u274c Check for Pests\")\r\n ]\r\n ).send()\r\n if res and res.get(\"value\") == \"diseases\":\r\n files = None\r\n # Wait for the user to upload a file\r\n while files == None:\r\n files = await cl.AskFileMessage(\r\n content=f\"{user_name['content']}, start by uploading an image of your crop.\", \r\n accept=[\"image/jpeg\", \"image/png\", \"image/jpg\"]\r\n ).send()\r\n # Decode the file\r\n"} +{"file_name": "7f04f1a1-6d7b-446c-a338-62ae505e1216.png", "code": " image_file = files[0]\r\n image_data = image_file.content # byte values of the image\r\n image = Image.open(io.BytesIO(image_data))\r\n model = load_model()\r\n predicted_label, predictions = evaluate_image(image, model)\r\n analysis_text: str = f\"\"\"\r\n After analyzing the image you uploaded, here is what I found:\r\n Maize Leaf Rust probability: {predictions['Maize Leaf Rust']}%\r\n Northern Leaf Blight probability: {predictions['Northern Leaf Blight']}%\r\n Healthy probability: {predictions['Healthy']}%\r\n Gray Leaf Spot probability: {predictions['Gray Leaf Spot']}%\r\n Your plant is most likely infected with {predicted_label}.\r\n \"\"\"\r\n elements = [\r\n cl.Image(\r\n name=\"image2\", display=\"inline\", content=image_data\r\n ), \r\n cl.Text(name=\"simple_text\", content=analysis_text, display=\"inline\", size='large')\r\n ]\r\n await cl.Message(content=f\"Maize image with {predicted_label}!\", elements=elements).send()\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.run(f'Tell me some facts about the maize disease {predicted_label} especially in relation to kenya.')\r\n await msg.update()\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.run(f'What fungicides or pesticides can be used to deal with the maize disease {predicted_label}?')\r\n await msg.update()\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.run(f'Get me aggrovets in {user_location}, Kenya')\r\n await msg.update()\r\n await cl.Message(content='Feel free to ask me more questions about maize plant diseases and how to deal with them.').send()\r\n else:\r\n await cl.Message(content='Currently cannot detect pests. Still working on that model.').send()\r\n \r\n\r\n@cl.on_message\r\nasync def main(message: cl.Message):\r\n"} +{"file_name": "728caeb2-d484-45c8-85a0-b8e9611b212c.png", "code": " agent = cl.user_session.get(\"agent\")\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.invoke({\"input\": message.content})[\"output\"]\r\n await msg.update()"} +{"file_name": "26f94642-8611-458b-b3f6-fe63662d7083.png", "code": "import chainlit as cl\r\nfrom assistant.utils.assistant_utils import welcome_user\r\nfrom assistant.agent import get_agent_executor\r\n\r\n\r\n@cl.on_chat_start\r\nasync def start():\r\n res = await cl.AskUserMessage(content=\"What is your name?\", timeout=30).send()\r\n if res:\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n msg.content = welcome_user(user_name=res['content'])\r\n await msg.update()\r\n \r\n\r\n@cl.on_message\r\nasync def main(message: cl.Message):\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n query: str = message.content\r\n agent_executor = get_agent_executor(query)\r\n msg.content = agent_executor.invoke({\"input\": query})['output']\r\n await msg.update()"} +{"file_name": "423c7826-9f81-48a4-8185-f1a596c773ef.png", "code": "from dotenv import load_dotenv\r\nload_dotenv()\r\n# from assistant.utils.channel_utils import get_channel_latest_video, get_favorite_channels_latest_videos\r\n# from assistant.utils.playlist_utils import add_video_to_youtube_playlist\r\nfrom assistant.agent import get_agent_executor\r\n# from assistant.tools.playlist.helpers import list_playlist_videos\r\n# from assistant.tools.comment.helpers import list_video_comments\r\nfrom assistant.agent import get_tools\r\nfrom assistant.tools.comment.helpers import (\r\n list_video_comments, find_my_comments, find_author_comments, list_comment_replies\r\n)\r\nfrom assistant.tools.channel.helpers import find_my_youtube_username\r\n\r\ntitle: str = 'Real Engineering'\r\n# print(get_favorite_channels_latest_videos())\r\n# title: str = 'How Nebula Works from Real Engineering'\r\n# playlist: str = 'Daily Videos'\r\n# add_video_to_youtube_playlist(title, playlist)\r\n# query = 'When was the youtube channel Ark Invest created?'\r\n# print(agent_executor.invoke({\"input\": query})['output'])\r\n# print(list_playlist_videos(title, title))\r\n#PLx7ERghZ6LoOKkmL4oeLoqWousfkKpdM_\r\n# print(list_video_comments('How Nebula Works', max_results=10))\r\n# query = 'When was my youtube channel created?'\r\n# agent_executor = get_agent_executor()\r\n# print(agent_executor.invoke({\"input\": query})['output'])\r\n# tools = get_tools(query)\r\n# print(len(tools))\r\n# t = [tool.description for tool in tools]\r\n# print(t)\r\n# print(find_author_comments('Trapping Rain Water - Google Interview Question - Leetcode 42', '@NeetCode'))\r\n\r\nquery = \"List all the replies to the comments by neetcode on the video titled 'Trapping Rain Water - Google Interview Question - Leetcode 42'\"\r\nagent_executor = get_agent_executor()\r\nprint(agent_executor.invoke({\"input\": query})['output'])\r\n# print(list_comment_replies('neetcode', 'Trapping Rain Water - Google Interview Question - Leetcode 42'))"} +{"file_name": "68f15bc4-2817-46ee-84c2-39ad197b07da.png", "code": "from os import path\r\nimport json\r\nfrom random import choices, choice\r\nfrom langchain.docstore.document import Document\r\nimport re\r\nfrom langchain.prompts import PromptTemplate\r\nfrom langchain_openai import OpenAI\r\nfrom langchain.pydantic_v1 import BaseModel, Field\r\nfrom langchain.output_parsers import PydanticOutputParser\r\nfrom langchain_core.runnables import RunnableBranch\r\nfrom langchain_core.output_parsers import StrOutputParser\r\n\r\n\r\napi_key: str = \"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\"\r\nllm = OpenAI(temperature=0, api_key=api_key)\r\nfile_path: str = \"comments.json\"\r\n\r\n\r\ndef lower(text: str) -> str:\r\n return text.lower().strip()\r\n\r\n\r\ndef remove_urls(text: str) -> str:\r\n url_pattern = r\"https?://\\S+|www\\.\\S+\"\r\n text = re.sub(url_pattern, \"\", text)\r\n return text\r\n\r\n\r\ndef remove_punctuations(text: str) -> str:\r\n punctuation_pattern = r\"[^\\w\\s]\"\r\n cleaned = re.sub(punctuation_pattern, \"\", text)\r\n return cleaned\r\n\r\n\r\ndef clean_text(text: str) -> str:\r\n text = lower(text)\r\n text = remove_urls(text)\r\n text = remove_punctuations(text)\r\n return text\r\n\r\n"} +{"file_name": "0899545e-023d-425f-93b7-7b8b3bd40d00.png", "code": "\r\ndef is_acceptable_len(text: str, l: int = 20) -> bool:\r\n return len(text.split()) >= l\r\n\r\n\r\nwith open(file_path, \"r\", encoding=\"utf-8\") as f:\r\n all_comments: list[str] = json.load(fp=f)\r\n cleaned_comments: list[str] = list(map(clean_text, all_comments))\r\n comments: list[str] = choices(population=cleaned_comments, k=10)\r\n docs: list[Document] = [\r\n Document(page_content=comment)\r\n for comment in comments\r\n if is_acceptable_len(comment)\r\n ]\r\n comments: list[dict[str, str | int]] = [\r\n {\"doc_id\": i + 1, \"comment\": docs[i].page_content} for i in range(len(docs))\r\n ]\r\n\r\ndata_dir = \"./agent_nelly/data_analysis/data\"\r\nfeatures_dir = \"features\"\r\nsave_features_dir = path.join(data_dir, features_dir, \"features.json\")\r\n\r\nwith open(save_features_dir, 'r') as f:\r\n topics: list[str] = json.load(f)\r\n\r\ncomment: dict = choice(comments)\r\n\r\n\r\nsentiment_msg: str = \"\"\"\r\nBelow is a customer comment in JSON format with the following keys:\r\n1. doc_id - identifier of the comment\r\n2. comment - the user comment\r\n\r\nPlease analyze the comment and identify the sentiment. The sentiment can be negative, neutral or \r\npositive. Only return a single string, the sentiment.\r\n\r\nComment:\r\n```\r\n{comment}\r\n```\r\n"} +{"file_name": "b9806d4c-cdf1-438b-9bf7-94fc526d5216.png", "code": "\"\"\"\r\n\r\nsentiment_template = PromptTemplate(template=sentiment_msg, input_variables=[\"comment\"])\r\n\r\n\r\nclass PositiveComment(BaseModel):\r\n doc_id: int = Field(description=\"The doc_id from the input\")\r\n topics: list[str] = Field(\r\n description=\"List of the relevant topics for the customer review. Include only topics from the list provided.\",\r\n default_factory=list,\r\n )\r\n sentiment: str = Field(\r\n description=\"Sentiment of the topic\", enum=[\"positive\", \"neutral\", \"negative\"]\r\n )\r\n\r\n\r\nclass NegativeComment(BaseModel):\r\n doc_id: int = Field(description=\"The doc_id from the input\")\r\n topics: list[str] = Field(\r\n description=\"List of the relevant topics for the customer review. Include only topics from the list provided.\",\r\n default_factory=list,\r\n )\r\n sentiment: str = Field(\r\n description=\"Sentiment of the topic\", enum=[\"positive\", \"neutral\", \"negative\"]\r\n )\r\n\r\n\r\npositive_parser = PydanticOutputParser(pydantic_object=PositiveComment)\r\nnegative_parser = PydanticOutputParser(pydantic_object=NegativeComment)\r\n\r\ntopic_assg_msg: str = \"\"\"\r\nBelow is a customer comment in JSON format with the following keys:\r\n1. doc_id - identifier of the comment\r\n2. comment - the user comment\r\n\r\nPlease analyze the provided comments and identify the main topics and sentiment. Include only the \r\ntopics provided below:\r\nTopics with a short description: {topics}\r\n\r\nComment:\r\n"} +{"file_name": "2f4be417-f055-453f-8488-14f5425282c3.png", "code": "```\r\n{comment}\r\n```\r\n\r\n{format_instructions}\r\n\"\"\"\r\n\r\npositive_tmpl = PromptTemplate(\r\n template=topic_assg_msg,\r\n input_variables=[\"comment\", \"topics\"],\r\n partial_variables={\r\n \"format_instructions\": positive_parser.get_format_instructions()\r\n },\r\n)\r\n\r\nnegative_tmpl = PromptTemplate(\r\n template=topic_assg_msg,\r\n input_variables=[\"comment\", \"topics\"],\r\n partial_variables={\r\n \"format_instructions\": negative_parser.get_format_instructions()\r\n },\r\n)\r\n\r\nsentiment_chain = sentiment_template | llm | StrOutputParser()\r\npos_chain = positive_tmpl | llm | positive_parser\r\nneg_chain = negative_tmpl | llm | negative_parser\r\n\r\n# res = sentiment_chain.invoke({\"comment\": comment})\r\n# print(res, comment)\r\n# if 'positive' in res.lower():\r\n# res = pos_chain.invoke({\"comment\": comment, 'topics': topics})\r\n# elif 'negative' in res.lower():\r\n# res = neg_chain.invoke({\"comment\": comment, 'topics': topics})\r\n# print(res)\r\n\r\nbranch = RunnableBranch(\r\n (lambda input: 'positive' in input['sentiment'].lower(), pos_chain),\r\n neg_chain\r\n)\r\n\r\n"} +{"file_name": "e3134f00-3806-45ac-bd65-b8f4d49b9d9f.png", "code": "full_chain = {\r\n \"sentiment\": sentiment_chain,\r\n \"comment\": lambda input: input['comment'],\r\n \"topics\": lambda input: input['topics']\r\n} | branch\r\n\r\nres = full_chain.invoke({'comment': comment, \"topics\": topics})\r\nprint(comment)\r\nprint(res)\r\n"} +{"file_name": "c62d7757-e75a-4bdd-8f55-b86599a06cff.png", "code": "from langchain.chains import RetrievalQA\r\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\r\nfrom langchain.vectorstores.faiss import FAISS\r\nfrom langchain.vectorstores.chroma import Chroma\r\nfrom langchain_openai import ChatOpenAI, OpenAIEmbeddings\r\nfrom langchain.docstore.document import Document\r\nfrom langchain.prompts import PromptTemplate\r\nfrom os import path\r\nimport json\r\nfrom random import choices\r\n\r\ndata_dir = \"data\"\r\nvideo_data_dir = \"data\"\r\ntranscribed_data = \"transcriptions\"\r\nvideo_title = \"iphone_15_marques_review\"\r\nsave_video_dir = path.join(data_dir, video_data_dir, video_title)\r\nsave_transcript_dir = path.join(data_dir, transcribed_data, video_title + \".txt\")\r\npersist_directory = path.join(data_dir, \"vectore_store\")\r\n\r\napi_key: str = \"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\"\r\nfile_path: str = \"comments.json\"\r\n\r\nwith open(file_path, \"r\", encoding=\"utf-8\") as f:\r\n all_comments: list[str] = json.load(fp=f)\r\n comments: list[str] = choices(population=all_comments, k=50)\r\n comments: list[Document] = [Document(page_content=comment) for comment in all_comments]\r\n\r\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=30)\r\nsplit_docs = text_splitter.split_documents(comments)\r\n\r\nembeddings = OpenAIEmbeddings(api_key=api_key)\r\n# vectordb = FAISS.from_texts(splits, embeddings)\r\n# vectordb = FAISS.from_documents(documents=comments, embedding=embeddings)\r\n# vectordb = Chroma.from_documents(\r\n# documents=split_docs,\r\n# embedding=embeddings,\r\n# persist_directory=persist_directory\r\n# )\r\n\r\ntemplate_str: str = \"\"\"\r\n"} +{"file_name": "4aaf2f0c-fb05-4325-8983-8fdf170a3e71.png", "code": "You are given the comments by various users on the review of {product}. Use the comments to answer \r\nthe questions that follow. When answering questions, try to list out your answer, with each answer \r\non its own separate line. If you do not know the answer, just say that you do not know. DO NOT MAKE \r\nSTUFF UP.\r\n---------\r\n{context}\r\nQuestion: {question}\r\nHelpful answer: \r\n\"\"\"\r\n\r\nproduct: str = \"iphone 15 pro max\"\r\ntemplate = PromptTemplate.from_template(template_str)\r\ntemplate = template.partial(product=product)\r\n\r\nvectordb = Chroma(\r\n persist_directory=persist_directory,\r\n embedding_function=embeddings\r\n)\r\n\r\nqa_chain = RetrievalQA.from_chain_type(\r\n llm=ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0, api_key=api_key),\r\n chain_type=\"stuff\",\r\n return_source_documents=True,\r\n retriever=vectordb.as_retriever(search_kwargs={\"k\": 10}),\r\n chain_type_kwargs={\"prompt\": template}\r\n)\r\n\r\nwhile True:\r\n query = input(\"User: \")\r\n res = qa_chain.invoke(query)\r\n print(res[\"result\"])"} +{"file_name": "5f0d9373-775d-4586-9e86-7d612a1637a3.png", "code": "from langchain.output_parsers import PydanticOutputParser\r\nfrom langchain.pydantic_v1 import Field, BaseModel\r\nfrom langchain.prompts import PromptTemplate\r\nfrom langchain_openai import OpenAI\r\nimport re\r\nimport json\r\nfrom langchain.docstore.document import Document\r\nfrom random import choices\r\nfrom langchain.base_language import BaseLanguageModel\r\n\r\nfile_path: str = \"comments.json\"\r\napi_key: str = \"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\"\r\n\r\n\r\ndef lower(text: str) -> str:\r\n return text.lower().strip()\r\n\r\n\r\ndef remove_urls(text: str) -> str:\r\n url_pattern = r\"https?://\\S+|www\\.\\S+\"\r\n text = re.sub(url_pattern, \"\", text)\r\n return text\r\n\r\n\r\ndef remove_punctuations(text: str) -> str:\r\n punctuation_pattern = r\"[^\\w\\s]\"\r\n cleaned = re.sub(punctuation_pattern, \"\", text)\r\n return cleaned\r\n\r\n\r\ndef clean_text(text: str) -> str:\r\n text = lower(text)\r\n text = remove_urls(text)\r\n text = remove_punctuations(text)\r\n return text\r\n\r\n\r\ndef is_acceptable_len(text: str, l=15) -> bool:\r\n return len(text.split()) >= l\r\n\r\n"} +{"file_name": "c72f09b2-fda9-4ce2-9d11-1a1f17ed9daa.png", "code": "from dotenv import load_dotenv\r\nload_dotenv()\r\nimport chainlit as cl\r\nfrom farm_agent.agents import agent\r\nfrom farm_agent.utils import load_model, evaluate_image\r\nfrom PIL import Image\r\nimport io\r\n\r\n\r\nuser_location: str = None\r\nuser_name: str = None\r\nwelcome_text: str = \"\"\"\r\nHello there. This is an application that helps farmers monitor the health level of their crops. \r\nStart by giving me your name and location, then upload an image of your crops. I will analyze it to \r\ndetermine the diasease or pest that affects it and then tell you how to deal with the pest or \r\ndisease and where to purchase pesticides or fungicides.\r\n\"\"\"\r\n\r\n@cl.on_chat_start\r\nasync def start():\r\n cl.user_session.set(\"agent\", agent)\r\n await cl.Message(content=welcome_text).send()\r\n user_name = await cl.AskUserMessage(content=\"What is your name?\", timeout=120).send()\r\n user_location = await cl.AskUserMessage(content=\"Where are you from?\", timeout=120).send()\r\n res = await cl.AskActionMessage(\r\n content=\"Would you like to determine if your crops are infected by a disease or by pests?\",\r\n actions=[\r\n cl.Action(name=\"Check for diseases\", value=\"diseases\", label=\"\u2705 Check for diseases\"),\r\n cl.Action(name=\"Check for Pests\", value=\"pests\", label=\"\u274c Check for Pests\")\r\n ]\r\n ).send()\r\n if res and res.get(\"value\") == \"diseases\":\r\n files = None\r\n # Wait for the user to upload a file\r\n while files == None:\r\n files = await cl.AskFileMessage(\r\n content=f\"{user_name['content']}, start by uploading an image of your crop.\", \r\n accept=[\"image/jpeg\", \"image/png\", \"image/jpg\"]\r\n ).send()\r\n # Decode the file\r\n"} +{"file_name": "eb331d22-c5ef-4173-b257-aaafb6bd4b7a.png", "code": " image_file = files[0]\r\n image_data = image_file.content # byte values of the image\r\n image = Image.open(io.BytesIO(image_data))\r\n model = load_model()\r\n predicted_label, predictions = evaluate_image(image, model)\r\n analysis_text: str = f\"\"\"\r\n After analyzing the image you uploaded, here is what I found:\r\n Maize Leaf Rust probability: {predictions['Maize Leaf Rust']}%\r\n Northern Leaf Blight probability: {predictions['Northern Leaf Blight']}%\r\n Healthy probability: {predictions['Healthy']}%\r\n Gray Leaf Spot probability: {predictions['Gray Leaf Spot']}%\r\n Your plant is most likely infected with {predicted_label}.\r\n \"\"\"\r\n elements = [\r\n cl.Image(\r\n name=\"image2\", display=\"inline\", content=image_data\r\n ), \r\n cl.Text(name=\"simple_text\", content=analysis_text, display=\"inline\", size='large')\r\n ]\r\n await cl.Message(content=f\"Maize image with {predicted_label}!\", elements=elements).send()\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.run('Tell me some facts about the maize disease leaf rust especially in relation to kenya.')\r\n await msg.update()\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.run(f'Tell me some facts about the maize disease {predicted_label} especially in relation to kenya.')\r\n await msg.update()\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.run(f'Get me aggrovets in {user_location}, Kenya')\r\n await msg.update()\r\n await cl.Message(content='Feel free to ask me more questions about maize plant diseases and how to deal with them.').send()\r\n else:\r\n await cl.Message(content='Currently cannot detect pests. Still working on that model.').send()\r\n \r\n\r\n@cl.on_message\r\nasync def main(message: cl.Message):\r\n"} +{"file_name": "e48eafc0-d817-4fc1-80e7-c2a88e0a7e28.png", "code": " agent = cl.user_session.get(\"agent\")\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.invoke({\"input\": message.content})[\"output\"]\r\n await msg.update()"} +{"file_name": "a26fae57-7aad-4719-94bc-8300b0ee0fb0.png", "code": "from dotenv import load_dotenv\r\nload_dotenv()\r\nimport chainlit as cl\r\n\r\nfrom calendar_assistant.usecases.agent import agent_executor\r\n\r\n\r\n@cl.on_message\r\nasync def main(message: cl.Message):\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent_executor.invoke({\"input\": message.content})['output']\r\n await msg.update()"} +{"file_name": "cf947524-8450-4f39-8dc5-740951f939c4.png", "code": "from dotenv import load_dotenv\r\nload_dotenv()\r\nimport chainlit as cl\r\nfrom farm_agent.agents import agent\r\nfrom farm_agent.utils import load_model, evaluate_image\r\nfrom PIL import Image\r\nimport io\r\n\r\n\r\nuser_location: str = None\r\nuser_name: str = None\r\nwelcome_text: str = \"\"\"\r\nHello there. This is an application that helps farmers monitor the health level of their crops. \r\nStart by giving me your name and location, then upload an image of your crops. I will analyze it to \r\ndetermine the diasease or pest that affects it and then tell you how to deal with the pest or \r\ndisease and where to purchase pesticides or fungicides.\r\n\"\"\"\r\n\r\n@cl.on_chat_start\r\nasync def start():\r\n cl.user_session.set(\"agent\", agent)\r\n await cl.Message(content=welcome_text).send()\r\n user_name = await cl.AskUserMessage(content=\"What is your name?\", timeout=120).send()\r\n user_location = await cl.AskUserMessage(content=\"Where are you from?\", timeout=120).send()\r\n res = await cl.AskActionMessage(\r\n content=\"Would you like to determine if your crops are infected by a disease or by pests?\",\r\n actions=[\r\n cl.Action(name=\"Check for diseases\", value=\"diseases\", label=\"\u2705 Check for diseases\"),\r\n cl.Action(name=\"Check for Pests\", value=\"pests\", label=\"\u274c Check for Pests\")\r\n ]\r\n ).send()\r\n if res and res.get(\"value\") == \"diseases\":\r\n files = None\r\n # Wait for the user to upload a file\r\n while files == None:\r\n files = await cl.AskFileMessage(\r\n content=f\"{user_name['content']}, start by uploading an image of your crop.\", \r\n accept=[\"image/jpeg\", \"image/png\", \"image/jpg\"]\r\n ).send()\r\n # Decode the file\r\n"} +{"file_name": "591cd28e-6767-424d-afb0-710b9d1e651c.png", "code": " image_file = files[0]\r\n image_data = image_file.content # byte values of the image\r\n image = Image.open(io.BytesIO(image_data))\r\n model = load_model()\r\n predicted_label, predictions = evaluate_image(image, model)\r\n analysis_text: str = f\"\"\"\r\n After analyzing the image you uploaded, here is what I found:\r\n Maize Leaf Rust probability: {predictions['Maize Leaf Rust']}%\r\n Northern Leaf Blight probability: {predictions['Northern Leaf Blight']}%\r\n Healthy probability: {predictions['Healthy']}%\r\n Gray Leaf Spot probability: {predictions['Gray Leaf Spot']}%\r\n Your plant is most likely infected with {predicted_label}.\r\n \"\"\"\r\n elements = [\r\n cl.Image(\r\n name=\"image2\", display=\"inline\", content=image_data\r\n ), \r\n cl.Text(name=\"simple_text\", content=analysis_text, display=\"inline\", size='large')\r\n ]\r\n await cl.Message(content=f\"Maize image with {predicted_label}!\", elements=elements).send()\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.run(f'Tell me some facts about the maize disease {predicted_label} especially in relation to kenya.')\r\n await msg.update()\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.run(f'What fungicides or pesticides can be used to deal with the maize disease {predicted_label}?')\r\n await msg.update()\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.run(f'Get me aggrovets in {user_location}, Kenya')\r\n await msg.update()\r\n await cl.Message(content='Feel free to ask me more questions about maize plant diseases and how to deal with them.').send()\r\n else:\r\n await cl.Message(content='Currently cannot detect pests. Still working on that model.').send()\r\n \r\n\r\n@cl.on_message\r\nasync def main(message: cl.Message):\r\n"} +{"file_name": "d3f9e458-3214-46d9-ad71-b93d6e48e462.png", "code": " agent = cl.user_session.get(\"agent\")\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.invoke({\"input\": message.content})[\"output\"]\r\n await msg.update()"} +{"file_name": "b32f7d47-0bcd-4161-8c83-04229ff36ef2.png", "code": "import chainlit as cl\r\nfrom assistant.utils.assistant_utils import welcome_user\r\nfrom assistant.agent import get_agent_executor\r\n\r\n\r\n@cl.on_chat_start\r\nasync def start():\r\n res = await cl.AskUserMessage(content=\"What is your name?\", timeout=30).send()\r\n if res:\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n msg.content = welcome_user(user_name=res['content'])\r\n await msg.update()\r\n \r\n\r\n@cl.on_message\r\nasync def main(message: cl.Message):\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n query: str = message.content\r\n agent_executor = get_agent_executor(query)\r\n msg.content = agent_executor.invoke({\"input\": query})['output']\r\n await msg.update()"} +{"file_name": "912009b4-b855-4b1d-8226-aae57defa84e.png", "code": "from dotenv import load_dotenv\r\nload_dotenv()\r\n# from assistant.utils.channel_utils import get_channel_latest_video, get_favorite_channels_latest_videos\r\n# from assistant.utils.playlist_utils import add_video_to_youtube_playlist\r\nfrom assistant.agent import get_agent_executor\r\n# from assistant.tools.playlist.helpers import list_playlist_videos\r\n# from assistant.tools.comment.helpers import list_video_comments\r\nfrom assistant.agent import get_tools\r\nfrom assistant.tools.comment.helpers import (\r\n list_video_comments, find_my_comments, find_author_comments, list_comment_replies\r\n)\r\nfrom assistant.tools.channel.helpers import find_my_youtube_username\r\n\r\ntitle: str = 'Real Engineering'\r\n# print(get_favorite_channels_latest_videos())\r\n# title: str = 'How Nebula Works from Real Engineering'\r\n# playlist: str = 'Daily Videos'\r\n# add_video_to_youtube_playlist(title, playlist)\r\n# query = 'When was the youtube channel Ark Invest created?'\r\n# print(agent_executor.invoke({\"input\": query})['output'])\r\n# print(list_playlist_videos(title, title))\r\n#PLx7ERghZ6LoOKkmL4oeLoqWousfkKpdM_\r\n# print(list_video_comments('How Nebula Works', max_results=10))\r\n# query = 'When was my youtube channel created?'\r\n# agent_executor = get_agent_executor()\r\n# print(agent_executor.invoke({\"input\": query})['output'])\r\n# tools = get_tools(query)\r\n# print(len(tools))\r\n# t = [tool.description for tool in tools]\r\n# print(t)\r\n# print(find_author_comments('Trapping Rain Water - Google Interview Question - Leetcode 42', '@NeetCode'))\r\n\r\nquery = \"List all the replies to the comments by neetcode on the video titled 'Trapping Rain Water - Google Interview Question - Leetcode 42'\"\r\nagent_executor = get_agent_executor()\r\nprint(agent_executor.invoke({\"input\": query})['output'])\r\n# print(list_comment_replies('neetcode', 'Trapping Rain Water - Google Interview Question - Leetcode 42'))"} +{"file_name": "c2b6e868-fd51-4adf-b254-d3ccdb1849d4.png", "code": "from os import path\r\nimport json\r\nfrom random import choices, choice\r\nfrom langchain.docstore.document import Document\r\nimport re\r\nfrom langchain.prompts import PromptTemplate\r\nfrom langchain_openai import OpenAI\r\nfrom langchain.pydantic_v1 import BaseModel, Field\r\nfrom langchain.output_parsers import PydanticOutputParser\r\nfrom langchain_core.runnables import RunnableBranch\r\nfrom langchain_core.output_parsers import StrOutputParser\r\n\r\n\r\napi_key: str = \"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\"\r\nllm = OpenAI(temperature=0, api_key=api_key)\r\nfile_path: str = \"comments.json\"\r\n\r\n\r\ndef lower(text: str) -> str:\r\n return text.lower().strip()\r\n\r\n\r\ndef remove_urls(text: str) -> str:\r\n url_pattern = r\"https?://\\S+|www\\.\\S+\"\r\n text = re.sub(url_pattern, \"\", text)\r\n return text\r\n\r\n\r\ndef remove_punctuations(text: str) -> str:\r\n punctuation_pattern = r\"[^\\w\\s]\"\r\n cleaned = re.sub(punctuation_pattern, \"\", text)\r\n return cleaned\r\n\r\n\r\ndef clean_text(text: str) -> str:\r\n text = lower(text)\r\n text = remove_urls(text)\r\n text = remove_punctuations(text)\r\n return text\r\n\r\n"} +{"file_name": "d34aa75a-251c-4c37-b737-9fc3eb620a0c.png", "code": "\r\ndef is_acceptable_len(text: str, l: int = 20) -> bool:\r\n return len(text.split()) >= l\r\n\r\n\r\nwith open(file_path, \"r\", encoding=\"utf-8\") as f:\r\n all_comments: list[str] = json.load(fp=f)\r\n cleaned_comments: list[str] = list(map(clean_text, all_comments))\r\n comments: list[str] = choices(population=cleaned_comments, k=10)\r\n docs: list[Document] = [\r\n Document(page_content=comment)\r\n for comment in comments\r\n if is_acceptable_len(comment)\r\n ]\r\n comments: list[dict[str, str | int]] = [\r\n {\"doc_id\": i + 1, \"comment\": docs[i].page_content} for i in range(len(docs))\r\n ]\r\n\r\ndata_dir = \"./agent_nelly/data_analysis/data\"\r\nfeatures_dir = \"features\"\r\nsave_features_dir = path.join(data_dir, features_dir, \"features.json\")\r\n\r\nwith open(save_features_dir, 'r') as f:\r\n topics: list[str] = json.load(f)\r\n\r\ncomment: dict = choice(comments)\r\n\r\n\r\nsentiment_msg: str = \"\"\"\r\nBelow is a customer comment in JSON format with the following keys:\r\n1. doc_id - identifier of the comment\r\n2. comment - the user comment\r\n\r\nPlease analyze the comment and identify the sentiment. The sentiment can be negative, neutral or \r\npositive. Only return a single string, the sentiment.\r\n\r\nComment:\r\n```\r\n{comment}\r\n```\r\n"} +{"file_name": "e48cc43f-7181-4aae-9b3b-9ab68d9ac549.png", "code": "\"\"\"\r\n\r\nsentiment_template = PromptTemplate(template=sentiment_msg, input_variables=[\"comment\"])\r\n\r\n\r\nclass PositiveComment(BaseModel):\r\n doc_id: int = Field(description=\"The doc_id from the input\")\r\n topics: list[str] = Field(\r\n description=\"List of the relevant topics for the customer review. Include only topics from the list provided.\",\r\n default_factory=list,\r\n )\r\n sentiment: str = Field(\r\n description=\"Sentiment of the topic\", enum=[\"positive\", \"neutral\", \"negative\"]\r\n )\r\n\r\n\r\nclass NegativeComment(BaseModel):\r\n doc_id: int = Field(description=\"The doc_id from the input\")\r\n topics: list[str] = Field(\r\n description=\"List of the relevant topics for the customer review. Include only topics from the list provided.\",\r\n default_factory=list,\r\n )\r\n sentiment: str = Field(\r\n description=\"Sentiment of the topic\", enum=[\"positive\", \"neutral\", \"negative\"]\r\n )\r\n\r\n\r\npositive_parser = PydanticOutputParser(pydantic_object=PositiveComment)\r\nnegative_parser = PydanticOutputParser(pydantic_object=NegativeComment)\r\n\r\ntopic_assg_msg: str = \"\"\"\r\nBelow is a customer comment in JSON format with the following keys:\r\n1. doc_id - identifier of the comment\r\n2. comment - the user comment\r\n\r\nPlease analyze the provided comments and identify the main topics and sentiment. Include only the \r\ntopics provided below:\r\nTopics with a short description: {topics}\r\n\r\nComment:\r\n"} +{"file_name": "40ec5bc0-2033-49b0-9547-addf922c8414.png", "code": "```\r\n{comment}\r\n```\r\n\r\n{format_instructions}\r\n\"\"\"\r\n\r\npositive_tmpl = PromptTemplate(\r\n template=topic_assg_msg,\r\n input_variables=[\"comment\", \"topics\"],\r\n partial_variables={\r\n \"format_instructions\": positive_parser.get_format_instructions()\r\n },\r\n)\r\n\r\nnegative_tmpl = PromptTemplate(\r\n template=topic_assg_msg,\r\n input_variables=[\"comment\", \"topics\"],\r\n partial_variables={\r\n \"format_instructions\": negative_parser.get_format_instructions()\r\n },\r\n)\r\n\r\nsentiment_chain = sentiment_template | llm | StrOutputParser()\r\npos_chain = positive_tmpl | llm | positive_parser\r\nneg_chain = negative_tmpl | llm | negative_parser\r\n\r\n# res = sentiment_chain.invoke({\"comment\": comment})\r\n# print(res, comment)\r\n# if 'positive' in res.lower():\r\n# res = pos_chain.invoke({\"comment\": comment, 'topics': topics})\r\n# elif 'negative' in res.lower():\r\n# res = neg_chain.invoke({\"comment\": comment, 'topics': topics})\r\n# print(res)\r\n\r\nbranch = RunnableBranch(\r\n (lambda input: 'positive' in input['sentiment'].lower(), pos_chain),\r\n neg_chain\r\n)\r\n\r\n"} +{"file_name": "13decdaf-cc2e-478c-8dcb-f6f10fedc2b6.png", "code": "full_chain = {\r\n \"sentiment\": sentiment_chain,\r\n \"comment\": lambda input: input['comment'],\r\n \"topics\": lambda input: input['topics']\r\n} | branch\r\n\r\nres = full_chain.invoke({'comment': comment, \"topics\": topics})\r\nprint(comment)\r\nprint(res)\r\n"} +{"file_name": "5e4444e6-32d7-40e7-88db-8934ec64f734.png", "code": "from langchain.chains import RetrievalQA\r\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\r\nfrom langchain.vectorstores.faiss import FAISS\r\nfrom langchain.vectorstores.chroma import Chroma\r\nfrom langchain_openai import ChatOpenAI, OpenAIEmbeddings\r\nfrom langchain.docstore.document import Document\r\nfrom langchain.prompts import PromptTemplate\r\nfrom os import path\r\nimport json\r\nfrom random import choices\r\n\r\ndata_dir = \"data\"\r\nvideo_data_dir = \"data\"\r\ntranscribed_data = \"transcriptions\"\r\nvideo_title = \"iphone_15_marques_review\"\r\nsave_video_dir = path.join(data_dir, video_data_dir, video_title)\r\nsave_transcript_dir = path.join(data_dir, transcribed_data, video_title + \".txt\")\r\npersist_directory = path.join(data_dir, \"vectore_store\")\r\n\r\napi_key: str = \"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\"\r\nfile_path: str = \"comments.json\"\r\n\r\nwith open(file_path, \"r\", encoding=\"utf-8\") as f:\r\n all_comments: list[str] = json.load(fp=f)\r\n comments: list[str] = choices(population=all_comments, k=50)\r\n comments: list[Document] = [Document(page_content=comment) for comment in all_comments]\r\n\r\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=30)\r\nsplit_docs = text_splitter.split_documents(comments)\r\n\r\nembeddings = OpenAIEmbeddings(api_key=api_key)\r\n# vectordb = FAISS.from_texts(splits, embeddings)\r\n# vectordb = FAISS.from_documents(documents=comments, embedding=embeddings)\r\n# vectordb = Chroma.from_documents(\r\n# documents=split_docs,\r\n# embedding=embeddings,\r\n# persist_directory=persist_directory\r\n# )\r\n\r\ntemplate_str: str = \"\"\"\r\n"} +{"file_name": "8daaa664-33a1-4cdb-b60b-4edf3d6d5d12.png", "code": "You are given the comments by various users on the review of {product}. Use the comments to answer \r\nthe questions that follow. When answering questions, try to list out your answer, with each answer \r\non its own separate line. If you do not know the answer, just say that you do not know. DO NOT MAKE \r\nSTUFF UP.\r\n---------\r\n{context}\r\nQuestion: {question}\r\nHelpful answer: \r\n\"\"\"\r\n\r\nproduct: str = \"iphone 15 pro max\"\r\ntemplate = PromptTemplate.from_template(template_str)\r\ntemplate = template.partial(product=product)\r\n\r\nvectordb = Chroma(\r\n persist_directory=persist_directory,\r\n embedding_function=embeddings\r\n)\r\n\r\nqa_chain = RetrievalQA.from_chain_type(\r\n llm=ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0, api_key=api_key),\r\n chain_type=\"stuff\",\r\n return_source_documents=True,\r\n retriever=vectordb.as_retriever(search_kwargs={\"k\": 10}),\r\n chain_type_kwargs={\"prompt\": template}\r\n)\r\n\r\nwhile True:\r\n query = input(\"User: \")\r\n res = qa_chain.invoke(query)\r\n print(res[\"result\"])"} +{"file_name": "9885da74-3b2c-43ca-9c94-f8da6536009b.png", "code": "from langchain.output_parsers import PydanticOutputParser\r\nfrom langchain.pydantic_v1 import Field, BaseModel\r\nfrom langchain.prompts import PromptTemplate\r\nfrom langchain_openai import OpenAI\r\nimport re\r\nimport json\r\nfrom langchain.docstore.document import Document\r\nfrom random import choices\r\nfrom langchain.base_language import BaseLanguageModel\r\n\r\nfile_path: str = \"comments.json\"\r\napi_key: str = \"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\"\r\n\r\n\r\ndef lower(text: str) -> str:\r\n return text.lower().strip()\r\n\r\n\r\ndef remove_urls(text: str) -> str:\r\n url_pattern = r\"https?://\\S+|www\\.\\S+\"\r\n text = re.sub(url_pattern, \"\", text)\r\n return text\r\n\r\n\r\ndef remove_punctuations(text: str) -> str:\r\n punctuation_pattern = r\"[^\\w\\s]\"\r\n cleaned = re.sub(punctuation_pattern, \"\", text)\r\n return cleaned\r\n\r\n\r\ndef clean_text(text: str) -> str:\r\n text = lower(text)\r\n text = remove_urls(text)\r\n text = remove_punctuations(text)\r\n return text\r\n\r\n\r\ndef is_acceptable_len(text: str, l=15) -> bool:\r\n return len(text.split()) >= l\r\n\r\n"} +{"file_name": "5e134df0-b479-4028-8a40-7836af69b269.png", "code": "with open(file_path, \"r\", encoding=\"utf-8\") as f:\r\n all_comments: list[str] = json.load(fp=f)\r\n cleaned_comments: list[str] = list(map(clean_text, all_comments))\r\n # comments: list[str] = choices(population=cleaned_comments, k=3)\r\n comments = cleaned_comments\r\n docs: list[Document] = [\r\n Document(page_content=comment)\r\n for comment in comments\r\n if is_acceptable_len(comment)\r\n ]\r\n comments: list[dict[str, str | int]] = [\r\n {\"doc_id\": i + 1, \"comment\": docs[i].page_content} for i in range(len(docs))\r\n ]\r\n\r\ndata_dir = \"./agent_nelly/data_analysis/data\"\r\nfeatures_dir = \"features\"\r\nsave_features_dir = path.join(data_dir, features_dir, \"features.json\")\r\n\r\nwith open(save_features_dir, 'r') as f:\r\n topics: list[str] = json.load(f)\r\n\r\n\r\nclass CustomerCommentData(BaseModel):\r\n doc_id: int = Field(description=\"The doc_id from the input\")\r\n topics: list[str] = Field(\r\n description=\"List of the relevant topics for the customer review. Include only topics from the list provided.\",\r\n default_factory=list,\r\n )\r\n sentiment: str = Field(\r\n description=\"Sentiment of the topic\", enum=[\"positive\", \"neutral\", \"negative\"]\r\n )\r\n \r\n\r\nclass CommentsParser(BaseModel):\r\n comment: list[CustomerCommentData] = Field(description=\"A list of the customer comment data\", default_factory=list)\r\n\r\n\r\noutput_parser = PydanticOutputParser(pydantic_object=CommentsParser)\r\nformat_instructions = output_parser.get_format_instructions()\r\n\r\n"} +{"file_name": "0cdc51e4-5665-438a-ba9d-7b110423f5fc.png", "code": "topic_assign_msg: str = \"\"\"\r\nBelow is a list of customer comments in JSON format with the following keys:\r\n1. doc_id - identifier of the comment\r\n2. comment - the user comment\r\n\r\nPlease analyze the provided comments and identify the main topics and sentiment. Include only the \r\ntopics mentioned in the following text:\r\nText: {topics}\r\n\r\n{format_instructions}\r\n\r\nuser comments: \r\n```{comments}```\r\n\"\"\"\r\n\r\ntopic_assign_tmpl = PromptTemplate(\r\n template=topic_assign_msg,\r\n input_variables=[\"topics\", \"comments\", \"format_instructions\"],\r\n)\r\n\r\nwith open('analysis.json', 'r') as f:\r\n data = json.load(f)\r\n \r\ni = data[-1][\"comment_id\"] + 1\r\n\r\nfrom time import sleep\r\nimport json\r\nfor _ in range(10):\r\n d = comments[i: i+3]\r\n x = {}\r\n for s in d:\r\n x[s['doc_id']] = s['comment']\r\n i += 3\r\n inputs = {\r\n \"topics\": topics,\r\n \"format_instructions\": format_instructions,\r\n \"comments\": json.dumps(d),\r\n }\r\n # print(d)\r\n # print(c)\r\n"} +{"file_name": "8dbd3e8a-a61d-4cee-b9ec-93a9c1da36bc.png", "code": " chat = OpenAI(temperature=0, api_key=api_key)\r\n try:\r\n chain = topic_assign_tmpl | chat | output_parser\r\n res = chain.invoke(inputs)\r\n except Exception:\r\n pass\r\n else:\r\n print(res)\r\n res = [\r\n {\r\n \"comment_id\": c.doc_id,\r\n \"comment\": x[c.doc_id],\r\n \"sentiment\": c.sentiment,\r\n \"features\": c.topics\r\n }\r\n for c in res.comment\r\n ]\r\n print(res)\r\n for y in res:\r\n data.append(y)\r\n with open('analysis.json', 'w') as f:\r\n json.dump(data, fp=f, indent=4)\r\n sleep(5)\r\n"} +{"file_name": "b13ca38e-1d1f-41d8-aa7b-65bee8907aca.png", "code": "from langchain.chains import RetrievalQA\r\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\r\nfrom langchain.vectorstores.faiss import FAISS\r\nfrom langchain_openai import ChatOpenAI, OpenAIEmbeddings\r\nfrom os import path\r\n\r\ndata_dir = \"data\"\r\nvideo_data_dir = \"data\"\r\ntranscribed_data = \"transcriptions\"\r\nvideo_title = \"iphone_15_marques_review\"\r\nsave_video_dir = path.join(data_dir, video_data_dir, video_title)\r\nsave_transcript_dir = path.join(data_dir, transcribed_data, video_title + \".txt\")\r\n\r\napi_key: str = \"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\"\r\nwith open(save_transcript_dir, \"r\") as f:\r\n video_transcript = f.read()\r\n\r\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=2500, chunk_overlap=150)\r\nsplits = text_splitter.split_text(video_transcript)\r\n\r\nembeddings = OpenAIEmbeddings(api_key=api_key)\r\nvectordb = FAISS.from_texts(splits, embeddings)\r\n\r\nqa_chain = RetrievalQA.from_chain_type(\r\n llm=ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0, api_key=api_key),\r\n chain_type=\"stuff\",\r\n retriever=vectordb.as_retriever(),\r\n)\r\n\r\nfeatures: list[str] = [\r\n \"5G connectivity\",\r\n \"A15 Bionic chip\",\r\n \"ProMotion display\",\r\n \"Ceramic Shield front cover\",\r\n \"Triple-camera system\",\r\n \"LiDAR scanner\",\r\n \"Night mode\",\r\n \"Cinematic mode\",\r\n \"Dolby Vision HDR recording\",\r\n \"MagSafe charging\",\r\n"} +{"file_name": "fb878479-06c2-4410-b0fc-01257b8330d8.png", "code": " \"Face ID\",\r\n \"Water and dust resistance\",\r\n \"iOS 15\",\r\n \"Improved battery life\",\r\n \"Siri voice recognition\",\r\n \"Apple Pay\",\r\n \"Apple Fitness+ integration\",\r\n \"Apple Arcade subscription\",\r\n \"Apple Music\",\r\n \"iMessage\",\r\n \"App Store\",\r\n \"iCloud storage\",\r\n \"Privacy features\",\r\n]\r\n\r\nfor feature in features:\r\n print(\"#######################################################\")\r\n query = f\"What does the video cover in relation to {feature}?\"\r\n print(qa_chain.invoke(query))\r\n print(\"#######################################################\")\r\n"} +{"file_name": "ff8603f3-5096-4eb2-9ad7-d1a314559d12.png", "code": "from os import path\r\nimport json\r\nfrom random import choices\r\nfrom langchain.docstore.document import Document\r\nimport re\r\nfrom langchain.prompts import ChatPromptTemplate\r\n\r\n\r\napi_key: str = \"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\"\r\nfile_path: str = \"comments.json\"\r\n\r\n\r\ndef lower(text: str) -> str:\r\n return text.lower().strip()\r\n\r\n# def remove_tags(text: str) -> str:\r\n# pattern = r'<.*?>'\r\n# text = re.sub()\r\n\r\ndef remove_urls(text: str) -> str:\r\n url_pattern = r'https?://\\S+|www\\.\\S+'\r\n text = re.sub(url_pattern, \"\", text)\r\n return text\r\n\r\ndef remove_punctuations(text: str) -> str:\r\n punctuation_pattern = r'[^\\w\\s]'\r\n cleaned = re.sub(punctuation_pattern, \"\", text)\r\n return cleaned\r\n\r\ndef clean_text(text: str) -> str:\r\n text = lower(text)\r\n text = remove_urls(text)\r\n text = remove_punctuations(text)\r\n return text\r\n\r\ndef is_acceptable_len(text: str, l=6) -> bool:\r\n return len(text.split()) >= l\r\n\r\nwith open(file_path, \"r\", encoding=\"utf-8\") as f:\r\n all_comments: list[str] = json.load(fp=f)\r\n"} +{"file_name": "851c022a-4f93-4ff3-bbfd-33ed2e143d3f.png", "code": " cleaned_comments: list[str] = list(map(clean_text, all_comments))\r\n comments: list[str] = choices(population=cleaned_comments, k=10)\r\n comments: list[Document] = [Document(page_content=comment) for comment in comments if is_acceptable_len(comment)]\r\n \r\ndata_dir = \"./agent_nelly/data_analysis/data\"\r\nfeatures_dir = \"features\"\r\nsave_features_dir = path.join(data_dir, features_dir, \"features.json\")\r\n\r\nwith open(save_features_dir, 'r') as f:\r\n topics: list[str] = json.load(f)\r\n\r\ntopic_assignment_msg: str = \"\"\"\r\nYou are provided with a detailed summary for the review of the product {product}. You will also be \r\ngiven a comment, which is a reaction to the product review. Please analyze the \r\n\r\nList of topics: {topics}\r\nComments: {comments}\r\n\"\"\"\r\n\r\n"} +{"file_name": "c38647d3-1e4f-4de6-8b3b-c95bc703320a.png", "code": "from langchain.output_parsers import PydanticOutputParser\r\nfrom langchain.pydantic_v1 import Field, BaseModel\r\nfrom langchain.prompts import ChatPromptTemplate, PromptTemplate\r\nfrom langchain_openai import ChatOpenAI, OpenAI\r\nimport re\r\nimport json\r\nfrom langchain.docstore.document import Document\r\nfrom random import choices\r\nfrom os import path\r\n\r\n\r\nfile_path: str = \"comments.json\"\r\napi_key: str = \"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\"\r\n\r\n\r\ndef lower(text: str) -> str:\r\n return text.lower().strip()\r\n\r\n\r\ndef remove_urls(text: str) -> str:\r\n url_pattern = r\"https?://\\S+|www\\.\\S+\"\r\n text = re.sub(url_pattern, \"\", text)\r\n return text\r\n\r\n\r\ndef remove_punctuations(text: str) -> str:\r\n punctuation_pattern = r\"[^\\w\\s]\"\r\n cleaned = re.sub(punctuation_pattern, \"\", text)\r\n return cleaned\r\n\r\n\r\ndef clean_text(text: str) -> str:\r\n text = lower(text)\r\n text = remove_urls(text)\r\n text = remove_punctuations(text)\r\n return text\r\n\r\n\r\ndef is_acceptable_len(text: str, l=15) -> bool:\r\n return len(text.split()) >= l\r\n"} +{"file_name": "04adeb87-9878-4c98-aded-e5a78782694f.png", "code": "\r\n\r\nwith open(file_path, \"r\", encoding=\"utf-8\") as f:\r\n all_comments: list[str] = json.load(fp=f)\r\n cleaned_comments: list[str] = list(map(clean_text, all_comments))\r\n comments: list[str] = choices(population=cleaned_comments, k=10)\r\n docs: list[Document] = [\r\n Document(page_content=comment)\r\n for comment in comments\r\n if is_acceptable_len(comment)\r\n ]\r\n comments: list[dict[str, str | int]] = [\r\n {\"doc_id\": i + 1, \"comment\": docs[i].page_content} for i in range(len(docs))\r\n ]\r\n\r\ndata_dir = \"./agent_nelly/data_analysis/data\"\r\nfeatures_dir = \"features\"\r\nsave_features_dir = path.join(data_dir, features_dir, \"features.json\")\r\n\r\nwith open(save_features_dir, 'r') as f:\r\n topics: list[str] = json.load(f)\r\n\r\n\r\nclass CustomerCommentData(BaseModel):\r\n doc_id: int = Field(description=\"The doc_id from the input\")\r\n topics: list[str] = Field(\r\n description=\"List of the relevant topics for the customer review. Include only topics from the list provided.\",\r\n default_factory=list,\r\n )\r\n sentiment: str = Field(\r\n description=\"Sentiment of the topic\", enum=[\"positive\", \"neutral\", \"negative\"]\r\n )\r\n \r\n\r\nclass CommentsParser(BaseModel):\r\n comment: list[CustomerCommentData] = Field(description=\"A list of the customer comment data\", default_factory=list)\r\n\r\n\r\noutput_parser = PydanticOutputParser(pydantic_object=CommentsParser)\r\nformat_instructions = output_parser.get_format_instructions()\r\n"} +{"file_name": "7b64f41d-49de-436f-a8d0-6ae5a7ac6a66.png", "code": "\r\ntopic_assign_msg: str = \"\"\"\r\nBelow is a list of customer comments in JSON format with the following keys:\r\n1. doc_id - identifier of the comment\r\n2. comment - the user comment\r\n\r\nPlease analyze the provided comments and identify the main topics and sentiment. Include only the \r\ntopics mentioned in the following text:\r\nText: {topics}\r\n\r\n{format_instructions}\r\n\r\nuser comments: \r\n```{comments}```\r\n\"\"\"\r\n\r\ntopic_assign_tmpl = PromptTemplate(\r\n template=topic_assign_msg,\r\n input_variables=[\"topics\", \"comments\", \"format_instructions\"],\r\n)\r\n# topic_assign_tmpl = ChatPromptTemplate.from_messages(\r\n# [\r\n# (\"system\", \"You are a helpful assistant. Your task is to analyze user comments.\"),\r\n# (\"user\", topic_assign_msg)\r\n# ]\r\n# )\r\n\r\n# messages = topic_assign_tmpl.format(\r\n# topics=topics,\r\n# format_instructions=format_instructions,\r\n# comments=json.dumps(comments)\r\n# )\r\n\r\ninputs = {\r\n \"topics\": topics,\r\n \"format_instructions\": format_instructions,\r\n \"comments\": json.dumps(comments),\r\n}\r\n\r\nchat = OpenAI(temperature=0, api_key=api_key)\r\n"} +{"file_name": "a545f1b1-b4a1-4499-b08d-41e263c754ad.png", "code": "chain = topic_assign_tmpl | chat | output_parser\r\nres = chain.invoke(inputs)\r\nprint(res)\r\n"} +{"file_name": "90fc8b51-1848-471e-8caa-3318f21855f4.png", "code": "from langchain_community.document_loaders.generic import GenericLoader\r\nfrom langchain_community.document_loaders.parsers import OpenAIWhisperParser\r\nfrom langchain_community.document_loaders.blob_loaders.youtube_audio import (\r\n YoutubeAudioLoader,\r\n)\r\nfrom os import path\r\n\r\n# Two Karpathy lecture videos\r\nurls = [\"https://www.youtube.com/watch?v=cBpGq-vDr2Y\"]\r\n\r\n# Directory to save audio files\r\ndata_dir = \"data\"\r\nvideo_data_dir = \"data\"\r\ntranscribed_data = \"transcriptions\"\r\nvideo_title = \"iphone_15_marques_review\"\r\nsave_video_dir = path.join(data_dir, video_data_dir, video_title)\r\nsave_transcript_dir = path.join(data_dir, transcribed_data, video_title + \".txt\")\r\n\r\napi_key: str = \"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\"\r\n\r\nloader = GenericLoader(\r\n YoutubeAudioLoader(urls, save_video_dir), OpenAIWhisperParser(api_key=api_key)\r\n)\r\ndocs = loader.load()\r\n\r\nfull_transcript = \"\"\r\nfor doc in docs:\r\n full_transcript += doc.page_content\r\n\r\nwith open(save_transcript_dir, \"w\", encoding=\"utf-8\") as f:\r\n f.write(full_transcript)\r\n\r\nprint(full_transcript)\r\n"} +{"file_name": "d19a7ee3-b36d-4e7c-93ad-4da4294edc37.png", "code": "from os import path\r\n\r\nfrom langchain.chains import StuffDocumentsChain\r\nfrom langchain.chains.llm import LLMChain\r\nfrom langchain.llms.base import BaseLLM\r\nfrom langchain.prompts import PromptTemplate\r\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\r\nfrom langchain_community.document_loaders.text import TextLoader\r\nfrom langchain_openai import ChatOpenAI, OpenAI\r\nfrom langchain.docstore.document import Document\r\n\r\nwith open('analysis.json', 'r') as f:\r\n import json\r\n analy = json.load(f)\r\n\r\napi_key: str = \"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\"\r\n\r\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)\r\n\r\n\r\ntemplate_str: str = \"\"\"\r\nYou are provided with the coments of various users to the review of the {product}\r\n\r\nPlease provide a detailed summary of the users comments. Make sure to identify the features \r\nthat the users loved as well as those that they hated.\r\ncomments: {comments}\r\ndetailed summary: \r\n\"\"\"\r\n\r\nproduct: str = \"Apple vision pro\"\r\ntemplate = PromptTemplate.from_template(template_str)\r\ntemplate = template.partial(product=product)\r\n\r\napi_key: str = \"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\"\r\nchat: BaseLLM = ChatOpenAI(temperature=0, api_key=api_key)\r\nllm: BaseLLM = OpenAI(temperature=0, api_key=api_key)\r\n\r\nllm_chain = LLMChain(llm=chat, prompt=template)\r\nstuff_chain = StuffDocumentsChain(llm_chain=llm_chain, document_variable_name=\"comments\")\r\n\r\n"} +{"file_name": "37b7b11f-6415-4a18-a702-aa63959db5f0.png", "code": "# loader = TextLoader(file_path=save_transcript_dir)\r\n# docs = loader.load_and_split(text_splitter=text_splitter)\r\ndocs: list[Document] = [\r\n Document(page_content=comment['comment']) for comment in analy\r\n]\r\nres = stuff_chain.run(docs)\r\n# chain = template | llm\r\n# res = chain.invoke({\"comments\": analy})\r\nprint(res)\r\n\r\n\r\n# def save_summary(summary: str) -> None:\r\n# with open(save_transcript_dir, \"w\") as f:\r\n# f.write(summary)\r\n\r\n\r\n# def summarize_video(video_transcript: str) -> str:\r\n# with open(save_transcript_dir, \"r\") as f:\r\n# summry: str = f.read()\r\n# return summry\r\n"} +{"file_name": "757d9d70-eecb-4e7e-91bf-8f238ad1761d.png", "code": "from dotenv import load_dotenv\r\n\r\nload_dotenv()\r\nfrom langchain.agents import AgentExecutor, Tool\r\nfrom langchain.agents.format_scratchpad import format_to_openai_function_messages\r\nfrom langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser\r\nfrom langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\r\nfrom langchain.tools.render import format_tool_to_openai_function\r\nfrom typing import Optional\r\nfrom langchain.llms.base import BaseLLM\r\nfrom langchain_openai import OpenAI, ChatOpenAI\r\nfrom langchain.callbacks.manager import (\r\n AsyncCallbackManagerForToolRun,\r\n CallbackManagerForToolRun,\r\n)\r\nfrom langchain.tools import BaseTool, Tool\r\nfrom langchain_community.utilities.google_search import GoogleSearchAPIWrapper\r\n\r\n\r\nclass GoogleSearchTool(BaseTool):\r\n name = \"google_search\"\r\n description = \"\"\"\r\n useful when you need to to search for the latest information from the web\r\n \"\"\"\r\n\r\n def _run(\r\n self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None\r\n ) -> str:\r\n \"\"\"Use the tool.\"\"\"\r\n search = GoogleSearchAPIWrapper()\r\n return search.run(query=query)\r\n\r\n async def _arun(\r\n self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None\r\n ) -> str:\r\n \"\"\"Use the tool asynchronously.\"\"\"\r\n raise NotImplementedError()\r\n\r\n\r\napi_key: str = \"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\"\r\n"} +{"file_name": "fd94db7e-2b21-4a83-b397-bc3cb77de6f0.png", "code": "chatgpt: BaseLLM = ChatOpenAI(temperature=0, api_key=api_key)\r\n\r\ntools: list[Tool] = [\r\n GoogleSearchTool(),\r\n]\r\n\r\n\r\ndef get_agent_executor():\r\n \"\"\"Get the agent\"\"\"\r\n prompt = ChatPromptTemplate.from_messages(\r\n [\r\n (\r\n \"system\",\r\n \"You are a very useful assistant. Your task will be to asnswer the users question. Be very friendly and professional.\",\r\n ),\r\n (\"user\", \"{input}\"),\r\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\r\n ]\r\n )\r\n\r\n functions = [format_tool_to_openai_function(t) for t in tools]\r\n\r\n llm_with_tools = chatgpt.bind(functions=functions)\r\n\r\n agent = (\r\n {\r\n \"input\": lambda x: x[\"input\"],\r\n \"agent_scratchpad\": lambda x: format_to_openai_function_messages(\r\n x[\"intermediate_steps\"]\r\n ),\r\n }\r\n | prompt\r\n | llm_with_tools\r\n | OpenAIFunctionsAgentOutputParser()\r\n )\r\n\r\n agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)\r\n return agent_executor\r\n\r\n\r\n"} +{"file_name": "bdf75653-3a55-4274-91c9-26c57d2523df.png", "code": "agent = get_agent_executor()\r\n# query = \"What are the main features of the iphone 15?\"\r\n# query = \"What are some of the complaints about iphone 15?\"\r\n# query = \"What features of the iphone 15 do its users love most?\"\r\nquery = \"What are the pros and cons of the iphone 15?\"\r\nres = agent.invoke({\"input\": query})\r\nprint(res)\r\n"} +{"file_name": "aea4c318-667c-48d0-863f-91fc149874e2.png", "code": "from langchain.chains import StuffDocumentsChain\r\nfrom langchain.chains.llm import LLMChain\r\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\r\nfrom langchain_community.document_loaders.text import TextLoader\r\nfrom os import path\r\nfrom langchain.prompts import PromptTemplate\r\nfrom langchain.llms.base import BaseLLM\r\nfrom langchain_openai import ChatOpenAI, OpenAI\r\n\r\n\r\ndata_dir = \"./agent_nelly/data_analysis/data\"\r\nsummary_dir = \"summary\"\r\nsave_transcript_dir = path.join(data_dir, summary_dir, \"summary.txt\")\r\n\r\napi_key: str = \"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\"\r\n\r\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)\r\n\r\n\r\ntemplate_str: str = \"\"\"\r\nYou are provided with the transcript for a youtube video. The video is a review of the product {product}. \r\nReturn a JSON object with a single key ```features``` which is a list of all the {product} features mentioned.\r\nTranscript: {text} \r\n\"\"\"\r\n\r\nproduct: str = \"iphone 15 pro max\"\r\ntemplate = PromptTemplate.from_template(template_str)\r\ntemplate = template.partial(product=product)\r\n\r\napi_key: str = \"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\"\r\nchat: BaseLLM = ChatOpenAI(temperature=0, api_key=api_key)\r\nllm: BaseLLM = OpenAI(temperature=0, api_key=api_key)\r\n\r\nwith open(save_transcript_dir, 'r') as f:\r\n summry: str = f.read()\r\n \r\nchain = template | llm\r\nres = chain.invoke({\"text\": summry})\r\nprint(res)"} +{"file_name": "cb20125e-7fb3-4a42-8b05-95a5e15849b2.png", "code": "from typing import Generator, Optional, Iterator\r\nfrom pydantic import BaseModel, Field\r\nfrom youtube import YouTube\r\nfrom youtube.models import Channel, Comment, Search, Video\r\nfrom youtube.schemas import (SearchOptionalParameters, SearchPart,\r\n YouTubeListResponse, YouTubeRequest,\r\n YouTubeResponse)\r\nimport json\r\nfrom rich.table import Table\r\nfrom rich.console import Console\r\nfrom rich import box\r\nfrom rich.live import Live\r\n\r\n\r\nclient_secrets_file = \"/home/lyle/Downloads/search.json\"\r\nyoutube_client = YouTube(client_secret_file=client_secrets_file)\r\nyoutube_client_object = youtube_client.authenticate()\r\nyoutube_client.youtube_client = youtube_client_object\r\n\r\n\r\ndef get_channel_id(channel_name: str) -> str:\r\n \"\"\"Get the channel id.\"\"\"\r\n response: YouTubeResponse = youtube_client.find_channel_by_name(channel_name)\r\n search_result: Search = response.items[0]\r\n return search_result.resource_id\r\n\r\n\r\ndef search_youtube_channels(product: str, max_results: int = 5) -> list[Search]:\r\n search_part: SearchPart = SearchPart()\r\n query: str = f\"latest {product} review\"\r\n optional_params: SearchOptionalParameters = SearchOptionalParameters(\r\n q=query,\r\n maxResults=max_results,\r\n type=[\"channel\"],\r\n )\r\n search_schema: YouTubeRequest = YouTubeRequest(\r\n part=search_part, optional_parameters=optional_params\r\n )\r\n response: YouTubeResponse = youtube_client.search(search_schema)\r\n items: list[Search] = response.items\r\n"} +{"file_name": "070b1267-d218-42d8-a025-4379dfd0f8b9.png", "code": " return items\r\n\r\n\r\ndef get_channel_details(channel: Search) -> Channel:\r\n \"\"\"Get channel details\"\"\"\r\n response: YouTubeListResponse = youtube_client.find_channel_by_id(\r\n channel_id=channel.resource_id\r\n )\r\n channel: Channel = response.items[0]\r\n return channel\r\n\r\n\r\ndef parse_channel_details(channel: Channel) -> dict:\r\n return {\r\n \"title\": channel.snippet.title,\r\n \"description\": channel.snippet.description,\r\n \"date\": str(channel.snippet.published_at.date()),\r\n \"subscribers\": channel.statistics.subscribers_count,\r\n \"videos\": channel.statistics.videos_count,\r\n }\r\n \r\n \r\ndef get_channels(product: str, max_results: int = 10) -> list[dict]:\r\n channels: list[Search] = search_youtube_channels(product=product, max_results=max_results)\r\n channels: list[Channel] = map(get_channel_details, channels)\r\n channels: list[dict] = map(parse_channel_details, channels)\r\n return channels\r\n\r\ndef save_data(file_path: str, data: list) -> None:\r\n with open(file_path, 'w') as f:\r\n json.dump(data, f, indent=4)\r\n \r\ndef load_data(file_path: str) -> dict:\r\n with open(file_path, 'r') as f:\r\n data: list[dict] = json.load(f)\r\n return data\r\n\r\n\r\ndef create_channels_table(table_data: list[dict]) -> Table:\r\n table: Table = Table(row_styles=[\"dim\", \"\"],leading=1, box=box.MINIMAL_DOUBLE_HEAD,\r\n"} +{"file_name": "3fdf1272-e2f0-4d8f-a16b-35e832fca3a6.png", "code": " title=\"[bold italic gold1]Youtube channels reviewing Iphone 15 pro[/bold italic gold1]\")\r\n table.add_column(header=\"[b]Channel Title\", justify=\"left\", style=\"dark_orange\")\r\n table.add_column(header=\"Subscribers\", justify=\"left\", style=\"light_coral\")\r\n table.add_column(header=\"[b]Videos\", justify=\"left\", style=\"yellow2\")\r\n table.add_column(header=\"Date\", justify=\"center\", style=\"violet\")\r\n table.columns[0].header_style = \"bold chartreuse1\"\r\n table.columns[1].header_style = \"bold dark_goldenrod\"\r\n table.columns[2].header_style = \"bold chartreuse1\"\r\n table.columns[3].header_style = \"bold dark_goldenrod\"\r\n table.border_style = \"bright_yellow\"\r\n table.pad_edge = True\r\n for row in table_data:\r\n table.add_row(row[\"title\"], str(row[\"subscribers\"]), str(row[\"videos\"]), row[\"date\"])\r\n return table\r\n\r\n\r\ndef video_search(\r\n product: str, channel_title: str, max_results: int = 5\r\n) -> list[Search]:\r\n \"\"\"Search the given channel for the given videos.\"\"\"\r\n query: str = f\"latest {product} review\"\r\n channel_id: str = get_channel_id(channel_name=channel_title)\r\n search_part: SearchPart = SearchPart()\r\n optional_params: SearchOptionalParameters = SearchOptionalParameters(\r\n channelId=channel_id,\r\n q=query,\r\n maxResults=max_results,\r\n type=[\"video\"],\r\n )\r\n search_schema: YouTubeRequest = YouTubeRequest(\r\n part=search_part, optional_parameters=optional_params\r\n )\r\n response: YouTubeResponse = youtube_client.search(search_schema)\r\n items: list[Search] = response.items\r\n return items\r\n\r\n\r\ndef get_video_id(video_title: str) -> str:\r\n \"\"\"Get video id given the title.\"\"\"\r\n part: SearchPart = SearchPart()\r\n"} +{"file_name": "1101219b-6902-416c-8cda-6909ec670725.png", "code": " optional_parameters: SearchOptionalParameters = SearchOptionalParameters(\r\n q=video_title, maxResults=1, type=[\"video\"]\r\n )\r\n search_request: YouTubeRequest = YouTubeRequest(\r\n part=part, optional_parameters=optional_parameters\r\n )\r\n search_results: YouTubeResponse = youtube_client.search(search_request)\r\n search_result: Search = search_results.items[0]\r\n return search_result.resource_id\r\n\r\n\r\ndef get_video_details(video: Search) -> Video:\r\n \"\"\"Get video details\"\"\"\r\n response: YouTubeListResponse = youtube_client.find_video_by_id(video.resource_id)\r\n video: Video = response.items[0]\r\n return video\r\n\r\n\r\ndef parse_video_details(video: Video) -> dict:\r\n return {\r\n \"title\": video.snippet.title,\r\n \"description\": video.snippet.description,\r\n \"date\": str(video.snippet.published_at),\r\n \"views\": video.statistics.views_count,\r\n \"comments\": video.statistics.comments_count,\r\n \"likes\": video.statistics.likes_count,\r\n }\r\n \r\ndef get_videos(product: str, channel: str) -> list[dict]:\r\n videos: list[Search] = video_search(product=product, channel_title=channel)\r\n videos: list[Video] = map(get_video_details, videos)\r\n videos: list[dict] = map(parse_video_details, videos)\r\n return videos\r\n\r\n\r\ndef create_videos_table(table_data: list[dict]) -> Table:\r\n table: Table = Table(row_styles=[\"dim\", \"\"],leading=1, box=box.MINIMAL_DOUBLE_HEAD,\r\n title=\"[bold italic gold1]Youtube videos reviewing Iphone 15 pro[/bold italic gold1]\")\r\n table.add_column(header=\"[b]Video Title\", justify=\"left\", style=\"dark_orange\")\r\n table.add_column(header=\"Views\", justify=\"left\", style=\"light_coral\")\r\n"} +{"file_name": "e40282c0-8440-487d-a849-50cfae353e43.png", "code": " table.add_column(header=\"[b]Comments\", justify=\"left\", style=\"yellow2\")\r\n table.add_column(header=\"Likes\", justify=\"left\", style=\"magenta3\")\r\n table.add_column(header=\"[b]Date\", justify=\"center\", style=\"violet\")\r\n table.columns[0].header_style = \"bold chartreuse1\"\r\n table.columns[1].header_style = \"bold dark_goldenrod\"\r\n table.columns[2].header_style = \"bold chartreuse1\"\r\n table.columns[3].header_style = \"bold dark_goldenrod\"\r\n table.columns[4].header_style = \"bold chartreuse1\"\r\n table.border_style = \"bright_yellow\"\r\n table.pad_edge = True\r\n for row in table_data:\r\n table.add_row(row[\"title\"], str(row[\"views\"]), str(row[\"comments\"]), str(row[\"likes\"]), row[\"date\"])\r\n return table\r\n\r\n\r\ndef parse_comment(comment: Comment) -> dict:\r\n return {\r\n \"comment_id\": comment.id,\r\n \"comment\": comment.snippet.text_display,\r\n \"likes\": comment.snippet.like_count,\r\n \"date_published\": str(comment.snippet.published_at),\r\n }\r\n \r\n \r\nclass Data(BaseModel):\r\n id: str\r\n comment: str\r\n sentiment: Optional[str] = Field(\r\n description=\"The comment sentiment\",\r\n enum=[\"neutral\", \"positive\", \"negative\"],\r\n default=None,\r\n )\r\n features: Optional[list[str]] = Field(\r\n description=\"The features mentioned in the comment\", default_factory=list\r\n )\r\n likes: Optional[int] = Field(description=\"The number of likes\", default=None)\r\n date: Optional[str] = Field(\r\n description=\"The date when the comment was posted\", default=None\r\n )\r\n\r\n"} +{"file_name": "d25e1c09-1055-45e1-ba80-c8d2f85387ef.png", "code": "\r\ndef get_video_comments(video_id: str, max_results: Optional[int] = 10) -> Generator:\r\n \"\"\"List a given videos comments\"\"\"\r\n comment_iterator: Iterator = youtube_client.get_comments_iterator(video_id=video_id)\r\n done: bool = False\r\n comment_count: int = 0\r\n for comment_threads in comment_iterator:\r\n if done:\r\n break\r\n for comment_thread in comment_threads:\r\n comment: Comment = comment_thread.snippet.top_level_comment\r\n comment = parse_comment(comment=comment)\r\n # comment = Data(\r\n # id=comment[\"comment_id\"],\r\n # comment=comment[\"comment\"],\r\n # likes=comment[\"likes\"],\r\n # date=comment[\"date_published\"],\r\n # )\r\n yield comment\r\n comment_count += 1\r\n if comment_count > max_results:\r\n done = True\r\n break\r\n \r\n \r\ndef create_comments_table(table_data: list[dict]) -> Table:\r\n table: Table = Table(row_styles=[\"dim\", \"\"],leading=1, box=box.MINIMAL_DOUBLE_HEAD)\r\n table.add_column(header=\"[b]Comment Id\", justify=\"left\", style=\"dark_orange\")\r\n table.add_column(header=\"Comment\", justify=\"left\", style=\"light_coral\")\r\n table.add_column(header=\"[b]Likes\", justify=\"left\", style=\"yellow2\")\r\n table.add_column(header=\"Date\", justify=\"center\", style=\"violet\")\r\n table.columns[0].header_style = \"bold chartreuse1\"\r\n table.columns[1].header_style = \"bold dark_goldenrod\"\r\n table.columns[2].header_style = \"bold chartreuse1\"\r\n table.columns[3].header_style = \"bold dark_goldenrod\"\r\n table.border_style = \"bright_yellow\"\r\n table.pad_edge = True\r\n for row in table_data:\r\n table.add_row(row[\"comment_id\"], row[\"comment\"], str(row[\"likes\"]), row[\"date_published\"])\r\n return table\r\n"} +{"file_name": "c71f3566-3eec-4cf3-b090-abbe2253b146.png", "code": "\r\n\r\nchannel_file_path: str = \"channels.json\"\r\nproduct: str = \"iphone 15 pro\"\r\nchannel: str = \"Marques Brownlee\"\r\nvideo_file_path: str = \"videos.json\"\r\ncomments_file_path: str = \"comments_1.json\"\r\n# save_data(file_path=channel_file_path, data=list(get_channels(product=product)))\r\n# channels: list[dict] = load_data(file_path=channel_file_path)\r\n\r\n# videos: list[dict] = get_videos(product=product, channel=channel)\r\n# save_data(file_path=video_file_path, data=list(get_videos(product=product, channel=channel)))\r\n# videos: list[dict] = load_data(file_path=video_file_path)\r\n\r\n# save_data(file_path=comments_file_path, data=list(get_video_comments(video_id='cBpGq-vDr2Y', max_results=100)))\r\ncomments: list[dict] = load_data(file_path=comments_file_path)\r\nconsole = Console()\r\nbatch: int = 10\r\nfrom time import sleep\r\n# with Live(create_comments_table(comments[:batch])) as live:\r\n# index: int = 0\r\n# for i in range(batch, len(comments)):\r\n# live.update(create_comments_table(comments[i: i+batch]))\r\n# sleep(0.1)\r\n \r\n# from collections import deque\r\n# queue = deque(maxlen=10, iterable=comments[:batch])\r\n# with Live(create_comments_table(table_data=queue)) as live:\r\n# for data in comments[batch:]:\r\n# queue.append(data)\r\n# live.update(create_comments_table(table_data=queue))\r\n# sleep(0.5)\r\n# from collections import deque\r\n# queue = deque(maxlen=10)\r\n# iterator = get_video_comments(video_id='cBpGq-vDr2Y', max_results=100)\r\n# for _ in range(batch):\r\n# queue.append(next(iterator))\r\n# with Live(create_comments_table(table_data=queue)) as live:\r\n# for data in iterator:\r\n# queue.append(data)\r\n"} +{"file_name": "38d3cf41-80a9-464d-ab95-2269428ce647.png", "code": "# live.update(create_comments_table(table_data=queue))\r\n\r\n\r\ndef create_analyzed_comments_table(table_data: list[dict]) -> Table:\r\n table: Table = Table(row_styles=[\"dim\", \"\"],leading=1, box=box.MINIMAL_DOUBLE_HEAD)\r\n table.add_column(header=\"[b]Comment Id\", justify=\"left\", style=\"dark_orange\")\r\n table.add_column(header=\"Comment\", justify=\"left\", style=\"light_coral\")\r\n table.add_column(header=\"[b]Likes\", justify=\"left\", style=\"yellow2\")\r\n table.add_column(header=\"Sentiment\", justify=\"left\", style=\"light_coral\")\r\n table.add_column(header=\"[b]Topics\", justify=\"left\", style=\"yellow2\")\r\n table.add_column(header=\"Date\", justify=\"center\", style=\"violet\")\r\n table.columns[0].header_style = \"bold chartreuse1\"\r\n table.columns[1].header_style = \"bold dark_goldenrod\"\r\n table.columns[2].header_style = \"bold chartreuse1\"\r\n table.columns[3].header_style = \"bold dark_goldenrod\"\r\n table.columns[4].header_style = \"bold chartreuse1\"\r\n table.columns[5].header_style = \"bold dark_goldenrod\"\r\n table.border_style = \"bright_yellow\"\r\n table.pad_edge = True\r\n colors = {\r\n 'negative': 'red',\r\n 'positive': 'green',\r\n 'neutral': 'purple'\r\n }\r\n for row in table_data:\r\n color = colors[row[\"sentiment\"]]\r\n table.add_row(row[\"comment_id\"], row[\"comment\"], str(row[\"likes\"]), f\"[bold {color}]{row['sentiment']}[/bold {color}]\", \", \".join(row[\"features\"]), row[\"date_published\"])\r\n return table\r\ndef analyze_comment(comment: dict) -> dict:\r\n from random import choice, choices\r\n sentiments: list[str] = ['negative', 'positive', 'neutral']\r\n all_topics: list[str] = [\"Incremental changes in design\", \"Softer corners\", \"Slimmer bezels\", \"USB Type-C port\"]\r\n sentiment: str = choice(sentiments)\r\n topics: list[str] = choices(population=all_topics, k=3)\r\n comment['topics'] = topics\r\n comment['sentiment'] = sentiment\r\n return comment\r\n# analyzed_comments: list[dict] = list(map(analyze_comment, comments)) \r\nwith open('analysis.json', 'r') as f:\r\n analyzed_comments: list[dict] = json.load(f)\r\n"} +{"file_name": "5e82858d-6aa5-4a14-8395-d514537efb9b.png", "code": "\r\nfrom random import randint \r\ndef like(comment: dict) -> dict:\r\n comment['likes'] = randint(0,2)\r\n return comment\r\n\r\nanalyzed_comments: list[dict] = list(map(like, analyzed_comments))\r\n \r\n \r\nfrom collections import deque\r\nqueue = deque(maxlen=10, iterable=analyzed_comments[:batch])\r\nwith Live(create_analyzed_comments_table(table_data=queue)) as live:\r\n for data in analyzed_comments[batch:]:\r\n queue.append(data)\r\n live.update(create_analyzed_comments_table(table_data=queue))\r\n sleep(0.5)"} +{"file_name": "3501eb0e-a4fa-42e8-968d-d4f864700508.png", "code": "from langchain.prompts import PromptTemplate\r\nfrom os import path\r\nfrom langchain.llms.base import BaseLLM\r\nfrom langchain_openai import OpenAI\r\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\r\n\r\n\r\ndata_dir = \"data\"\r\nvideo_data_dir = \"data\"\r\ntranscribed_data = \"transcriptions\"\r\nvideo_title = \"iphone_15_marques_review\"\r\nsave_video_dir = path.join(data_dir, video_data_dir, video_title)\r\nsave_transcript_dir = path.join(data_dir, transcribed_data, video_title + \".txt\")\r\n\r\napi_key: str = \"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\"\r\napi_key: str = \"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\"\r\nchatgpt: BaseLLM = OpenAI(temperature=0, api_key=api_key)\r\n\r\nprompt: str = \"\"\"\r\nYou are given the transcript for a video that covers the review of the iphone 15 pro. Find out all the \r\nfeatures covered in the review. Only return the features of the iphine 15 pri. Return a JSON object with a single key called features.\r\nTranscript: {transcript}\r\n\"\"\"\r\n\r\nwith open(save_transcript_dir, \"r\") as f:\r\n video_transcript = f.read()\r\n\r\ntemplate = PromptTemplate(template=prompt, input_variables=[\"transcript\"])\r\n\r\nchain = template | chatgpt\r\n\r\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=2500, chunk_overlap=150)\r\nsplits = text_splitter.split_text(video_transcript)\r\ntopics = []\r\nfor doc in splits:\r\n res = chain.invoke({\"transcript\": doc})\r\n topics.append(res)\r\n print(res)\r\nprint(topics)\r\n"} +{"file_name": "523d52c0-4291-4b3f-9186-c855ffba01b6.png", "code": "from dotenv import load_dotenv\r\n\r\nload_dotenv()\r\nfrom langchain.agents import tool\r\nfrom langchain.pydantic_v1 import BaseModel, Field\r\nfrom langchain_core.utils.function_calling import convert_to_openai_function\r\nfrom langchain_community.utilities.google_search import GoogleSearchAPIWrapper\r\nimport os\r\nfrom langchain_openai import ChatOpenAI, OpenAI\r\nfrom langchain.prompts import ChatPromptTemplate, PromptTemplate\r\nfrom langchain.llms.base import BaseLLM\r\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\r\nfrom os import path\r\n\r\n\r\nOPENAI_API_KEY: str = \"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\"\r\n\r\nGOOGLE_API_KEY: str = os.environ.get(\r\n \"GOOGLE_API_KEY\", \"AIzaSyDDuuMXlQ-7iiT7QvQg6c8nbyV0mFxSAYo\"\r\n)\r\nGOOGLE_CSE_ID: str = os.environ.get(\"GOOGLE_CSE_ID\", \"a347832f863fd4f5d\")\r\n\r\nchat_model: BaseLLM = ChatOpenAI(temperature=0, api_key=OPENAI_API_KEY)\r\nllm: BaseLLM = OpenAI(temperature=0, api_key=OPENAI_API_KEY)\r\n\r\ngoogle_search = GoogleSearchAPIWrapper(\r\n google_api_key=GOOGLE_API_KEY, google_cse_id=GOOGLE_CSE_ID, k=3\r\n)\r\n\r\ndata_dir = \"data\"\r\nvideo_data_dir = \"data\"\r\ntranscribed_data = \"transcriptions\"\r\nvideo_title = \"iphone_15_marques_review\"\r\nsave_video_dir = path.join(data_dir, video_data_dir, video_title)\r\nsave_transcript_dir = path.join(data_dir, transcribed_data, video_title + \".txt\")\r\n\r\n\r\nclass UserQuery(BaseModel):\r\n query: str = Field(description=\"What the user wants to search for on the web\")\r\n result_count: int = Field(description=\"The number of results to return\", default=5)\r\n"} +{"file_name": "d2a32c7e-c984-41cb-a4f8-a195d715024d.png", "code": "\r\n\r\nclass ProductFeatures(BaseModel):\r\n display: str\r\n design: str\r\n perfomance: str\r\n camera: str\r\n\r\n\r\n@tool(args_schema=UserQuery)\r\ndef google_search_tool(query: str, result_count: int) -> str:\r\n \"\"\"Search for the latest information from the web using Google.\"\"\"\r\n google_search = GoogleSearchAPIWrapper(\r\n google_api_key=GOOGLE_API_KEY, google_cse_id=GOOGLE_CSE_ID, k=3\r\n )\r\n google_search.k = result_count\r\n return google_search.run(query=query)\r\n\r\n\r\nchat_model.bind(functions=[convert_to_openai_function(google_search_tool)])\r\nprompt = ChatPromptTemplate.from_messages(\r\n messages=[\r\n (\r\n \"system\",\r\n \"You are a very good product analyst for apple products. Your task is to find out the most accurate detailed information on various apple products.\",\r\n ),\r\n (\"human\", \"{request}\"),\r\n ]\r\n)\r\n\r\n# analyst_chain = prompt | chat_model\r\n\r\n# res = analyst_chain.invoke(\r\n# {\"request\": \"What are all the features of the iphone 13 pro max?\"}\r\n# )\r\n\r\n# print(res)\r\n\r\nproduct_features_template_str: str = \"\"\"\r\nYou will be provided with a product name. Your task will be to list all all the product,s features \r\n"} +{"file_name": "ca662e58-e478-41fe-b194-a097ad0e7a89.png", "code": "that can be used during the creation of a youtube video that reviews the product for users. Only \r\nreturn a JSON object with the key ``features``.\r\nProduct: {product}\r\n\"\"\"\r\n\r\nproduct_features_description_template: str = \"\"\"\r\nYou will be provided by a list of product features as well as a product name. Your task is to \r\nprovide a detailed description of each feature. Only return a JSON object with each feature as \r\na key.\r\nProduct Name: {product}\r\nProduct features: {features}\r\n\"\"\"\r\n\r\nfeatures_covered_template_str: str = \"\"\"\r\nYou will be provided with the transcript for a youtube video that reviews a product. The product name \r\nwill also be provided as well as the features of the given product. Your task will be to find all the \r\nfeatures of the product that are found in the feature list and are also covered in the transcript. For \r\neach feature covered in the transcript, provide a short summary of what is covered. Only consider the \r\nfeatures in the feature list. Only return a valid JSON object with each feature as a key and a short \r\nsummary.\r\nProduct: {product}\r\nFeatures: {features}\r\nTranscript: {transcript}\r\n\"\"\"\r\n\r\n\r\nfeatures_template = PromptTemplate(\r\n template=product_features_template_str, input_variables=[\"product\"]\r\n)\r\nfeatures_description_template = PromptTemplate(\r\n template=product_features_description_template,\r\n input_variables=[\"product\", \"features\"],\r\n)\r\nfeatures_covered_template = PromptTemplate(\r\n template=features_covered_template_str, input_variables=[\"product\", \"transcript\", \"features\"]\r\n)\r\n\r\nproduct: str = \"iphone 13 pro\"\r\nfeatures: list[str] = [\r\n \"5G connectivity\",\r\n"} +{"file_name": "bad54846-134f-423f-ad11-f55db1920834.png", "code": " \"A15 Bionic chip\",\r\n \"ProMotion display\",\r\n \"Ceramic Shield front cover\",\r\n \"Triple-camera system\",\r\n \"LiDAR scanner\",\r\n \"Night mode\",\r\n \"Cinematic mode\",\r\n \"Dolby Vision HDR recording\",\r\n \"MagSafe charging\",\r\n \"Face ID\",\r\n \"Water and dust resistance\",\r\n \"iOS 15\",\r\n \"Improved battery life\",\r\n \"Siri voice recognition\",\r\n \"Apple Pay\",\r\n \"Apple Fitness+ integration\",\r\n \"Apple Arcade subscription\",\r\n \"Apple Music\",\r\n \"iMessage\",\r\n \"App Store\",\r\n \"iCloud storage\",\r\n \"Privacy features\",\r\n]\r\n# start: int = 0\r\n# step: int = 3\r\n# while start < len(features):\r\n# f: list[str] = features[start: start + step]\r\n# chain = features_description_template | llm\r\n# res = chain.invoke({\"product\": \"iphone 13 pro\", \"features\": f})\r\n# print(res)\r\n# start += step\r\nwith open(save_transcript_dir, \"r\") as f:\r\n video_transcript = f.read()\r\n \r\ntext_splitter = RecursiveCharacterTextSplitter(chunk_size=4000, chunk_overlap=0)\r\nsplits = text_splitter.split_text(video_transcript)\r\nfeatures_covered = []\r\nfor doc in splits:\r\n chain = features_covered_template | llm\r\n res = chain.invoke({\"transcript\": doc, \"product\": \"iphone 13 pro\", \"features\": features})\r\n"} +{"file_name": "f0a8bed6-0efa-4e13-8011-506cd510c658.png", "code": " features_covered.append(res)\r\n print(res)\r\nprint(features_covered)\r\n# print(len(splits))\r\n# for split in splits:\r\n# print(split)\r\n# print(\"###################\")"} +{"file_name": "9ac9740a-4573-47c7-9753-c2ac3d4f2d0e.png", "code": "from collections.abc import Iterator\r\nfrom typing import Optional\r\nfrom youtube import YouTube\r\nfrom youtube.models import Search, Video\r\nfrom youtube.schemas import (\r\n SearchOptionalParameters,\r\n SearchPart,\r\n YouTubeListResponse,\r\n YouTubeRequest,\r\n YouTubeResponse,\r\n)\r\nfrom youtube.models import Comment\r\nfrom youtube.schemas import (\r\n CommentThreadFilter,\r\n CommentThreadOptionalParameters,\r\n CommentThreadPart,\r\n YouTubeRequest,\r\n)\r\nimport json\r\n\r\n\r\ndef get_video_id(video_title: str) -> str:\r\n \"\"\"Get video id given the title.\"\"\"\r\n part: SearchPart = SearchPart()\r\n optional_parameters: SearchOptionalParameters = SearchOptionalParameters(\r\n q=video_title, maxResults=1, type=[\"video\"]\r\n )\r\n search_request: YouTubeRequest = YouTubeRequest(\r\n part=part, optional_parameters=optional_parameters\r\n )\r\n search_results: YouTubeResponse = youtube_client.search(search_request)\r\n search_result: Search = search_results.items[0]\r\n return search_result.resource_id\r\n\r\n\r\ndef list_video_comments(video_id: str, max_results: Optional[int] = 2500) -> None:\r\n \"\"\"List a given videos comments\"\"\"\r\n # video_id: str = get_video_id(video_title)\r\n part: CommentThreadPart = CommentThreadPart()\r\n filter: CommentThreadFilter = CommentThreadFilter(videoId=video_id)\r\n"} +{"file_name": "0f939b6d-bebb-480c-8220-5fd807cac25f.png", "code": " optional: CommentThreadOptionalParameters = CommentThreadOptionalParameters(\r\n maxResults=25\r\n )\r\n request: YouTubeRequest = YouTubeRequest(\r\n part=part, filter=filter, optional_parameters=optional\r\n )\r\n comment_iterator: Iterator = youtube_client.get_comments_iterator(request)\r\n done: bool = False\r\n comment_count: int = 0\r\n for comment_threads in comment_iterator:\r\n comments: list[str] = []\r\n if done:\r\n break\r\n for comment_thread in comment_threads:\r\n comment: Comment = comment_thread.snippet.top_level_comment\r\n comments.append(comment.snippet.text_display)\r\n comment_count += 1\r\n if comment_count > max_results:\r\n done = True\r\n break\r\n with open(\"comments.json\", \"r\", encoding=\"utf-8\") as f:\r\n existing_comments: list[str] = json.load(f)\r\n\r\n with open(\"comments.json\", \"w\", encoding=\"utf-8\") as f:\r\n existing_comments += comments\r\n json.dump(existing_comments, fp=f, indent=2)\r\n return comment_count\r\n\r\n\r\nclient_secrets_file = \"/home/lyle/Downloads/search.json\"\r\nyoutube_client = YouTube(client_secret_file=client_secrets_file)\r\nyoutube_client_object = youtube_client.authenticate()\r\nyoutube_client.youtube_client = youtube_client_object\r\n\r\n\r\n# print(get_video_id(video_title='iPhone 15 Pro Review: The Good, The Bad, & The Ugly!'))\r\nprint(list_video_comments(video_id=\"cBpGq-vDr2Y\"))\r\n"} +{"file_name": "a87ea07b-3320-4576-bbc3-b093ae7a345d.png", "code": "from dotenv import load_dotenv\r\nload_dotenv()\r\nfrom crewai import Crew\r\nfrom textwrap import dedent\r\n\r\nfrom product_review_agents import ProductReviewAgents\r\nfrom product_review_tasks import ProductReviewTasks\r\n\r\n\r\nclass ProductReviewCrew:\r\n def __init__(self, product):\r\n self.product = product\r\n\r\n def run(self):\r\n agents = ProductReviewAgents()\r\n tasks = ProductReviewTasks()\r\n\r\n research_analyst_agent = agents.research_analyst()\r\n\r\n research_task = tasks.research(research_analyst_agent, self.product)\r\n\r\n crew = Crew(\r\n agents=[\r\n research_analyst_agent,\r\n ],\r\n tasks=[\r\n research_task,\r\n ],\r\n verbose=True\r\n )\r\n\r\n result = crew.kickoff()\r\n return result\r\n\r\nif __name__ == \"__main__\":\r\n print(\"## Welcome to Product Analysis Crew\")\r\n print('-------------------------------')\r\n company = input(\r\n dedent(\"\"\"\r\n What is the product you want to analyze?\r\n"} +{"file_name": "051cf90e-0f60-4348-8c67-229c9a0c107e.png", "code": " \"\"\"))\r\n \r\n product_crew = ProductReviewCrew(company)\r\n result = product_crew.run()\r\n print(\"\\n\\n########################\")\r\n print(\"## Here is the Report\")\r\n print(\"########################\\n\")\r\n print(result)"} +{"file_name": "0348c54f-bae9-4dfc-a06a-bb21301d0344.png", "code": "from crewai import Agent\r\nfrom tools import FindProductVideoTools, FindProductReviewTools\r\nfrom langchain.llms.openai import OpenAI\r\nfrom langchain.chat_models import ChatOpenAI\r\n\r\n\r\nclass ProductReviewAgents():\r\n def research_analyst(self):\r\n return Agent(\r\n role='Product Video Researcher',\r\n goal=\"\"\"Find the best product review videos from youtube\"\"\",\r\n backstory=\"\"\"Known for your indepth knowledge of various videos that \r\n analyze different products on youtube. Now you have to find the best video that \r\n reviews the given product.\"\"\",\r\n llm=OpenAI(temperature=0.7),\r\n verbose=True,\r\n tools=[\r\n FindProductVideoTools.find_product_video_id,\r\n FindProductReviewTools.find_product_reviews\r\n ]\r\n )"} +{"file_name": "113210ed-e2a1-49ad-a155-4d9d3058eba5.png", "code": "from crewai import Task\r\nfrom textwrap import dedent\r\n\r\nclass ProductReviewTasks():\r\n def research(self, agent, product):\r\n return Task(description=dedent(f\"\"\"\r\n Collect and summarize the most recent comments from the \r\n products review from youtube.\r\n Maje sure to capture the sentiment of each comment, \r\n what the user liked, did not like as well as other \r\n features that they wish were present.\r\n\r\n Your final answer MUST be a report that includes a\r\n comprehensive summary of the reviews, capturing \r\n the most loved features.\r\n \r\n {self.__tip_section()}\r\n\r\n Selected product by the customer: {product}\r\n \"\"\"),\r\n agent=agent\r\n )\r\n \r\n def __tip_section(self):\r\n return \"If you do your BEST WORK, I'll give you a $10,000 commision!\""} +{"file_name": "518f77c9-58f4-4c10-82db-da6c7aaeabc3.png", "code": "from langchain.agents import AgentType, Tool, initialize_agent, tool\r\nfrom langchain.llms import OpenAI\r\nfrom langchain.memory import ConversationBufferMemory\r\nfrom langchain.utilities import SerpAPIWrapper\r\nimport googlemaps\r\nimport os\r\nimport chainlit as cl\r\nfrom dotenv import load_dotenv\r\nload_dotenv()\r\n\r\n\r\n@tool\r\ndef get_agrovets(query: str) -> str:\r\n \"\"\"Useful when you need to get agrovets in a given location. Give it a query, such as agrovets in Nairobi, Kenya.\r\n \"\"\"\r\n gmaps = googlemaps.Client(key=os.environ['GOOGLE_MAPS_API_KEY'])\r\n results = gmaps.places(query=f'Get me aggrovets in {query}')\r\n aggrovet_locations: list[dict] = list()\r\n for result in results['results']:\r\n bussiness: dict = dict()\r\n bussiness['business_status'] = result['business_status']\r\n bussiness['formatted_address'] = result['formatted_address']\r\n bussiness['name'] = result['name']\r\n bussiness['opening_hours'] = result.get('opening_hours', 'NaN')\r\n aggrovet_locations.append(bussiness)\r\n return aggrovet_locations\r\n\r\n\r\n@cl.on_chat_start\r\nasync def start():\r\n tools: list[Tool] = [\r\n get_agrovets\r\n ]\r\n llm = OpenAI(temperature=0)\r\n memory = ConversationBufferMemory(memory_key=\"chat_history\")\r\n agent = initialize_agent(\r\n tools,\r\n llm,\r\n agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,\r\n verbose=True,\r\n"} +{"file_name": "2ac544c7-593a-467a-b59a-ffceb02a02fe.png", "code": " memory=memory,\r\n )\r\n cl.user_session.set(\"agent\", agent)\r\n \r\n\r\n@cl.on_message\r\nasync def main(message: cl.Message):\r\n agent = cl.user_session.get(\"agent\")\r\n msg = cl.Message(content=\"\")\r\n await msg.send()\r\n await cl.sleep(1)\r\n msg.content = agent.invoke({\"input\": message.content})[\"output\"]\r\n await msg.update()"} +{"file_name": "5629ee4e-8bc3-404e-bd7b-0526992ff195.png", "code": "from pydantic import Field, BaseModel\r\n\r\n\r\nclass Trip(BaseModel):\r\n start: str = Field(description=\"start location of trip\")\r\n end: str = Field(description=\"end location of trip\")\r\n waypoints: list[str] = Field(description=\"list of waypoints\")\r\n transit: str = Field(description=\"mode of transportation\")"} +{"file_name": "e924816b-4a1a-4156-aea4-6cd682fc60ff.png", "code": "# import openai\r\nimport logging\r\nimport time\r\n# for Palm\r\nfrom langchain.llms import GooglePalm\r\n# for OpenAI\r\nfrom langchain.chat_models import ChatGooglePalm, ChatOpenAI\r\nfrom langchain.chains import LLMChain, SequentialChain\r\nfrom prompt_templates import (\r\n ValidationTemplate, load_secets, MappingTemplate, ItineraryTemplate)\r\n\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nclass Agent(object):\r\n def __init__(\r\n self,\r\n open_ai_api_key,\r\n model=\"gpt-3.5-turbo\",\r\n temperature=0,\r\n debug=True,\r\n ):\r\n self.logger = logging.getLogger(__name__)\r\n self.logger.setLevel(logging.INFO)\r\n self._openai_key = open_ai_api_key\r\n\r\n self.chat_model = ChatOpenAI(model=model, temperature=temperature, openai_api_key=self._openai_key)\r\n self.validation_prompt = ValidationTemplate()\r\n self.itinerary_prompt = ItineraryTemplate()\r\n self.mapping_prompt = MappingTemplate()\r\n self.validation_chain = self._set_up_validation_chain(debug)\r\n self.agent_chain = self._set_up_agent_chain(debug)\r\n\r\n def _set_up_validation_chain(self, debug=True):\r\n \"\"\"\r\n\r\n Parameters\r\n ----------\r\n debug\r\n\r\n"} +{"file_name": "b2cea438-2fba-488e-8ee7-81a76186f85b.png", "code": " Returns\r\n -------\r\n\r\n \"\"\"\r\n validation_agent = LLMChain(\r\n llm=self.chat_model,\r\n prompt=self.validation_prompt.chat_prompt,\r\n output_parser=self.validation_prompt.parser,\r\n output_key=\"validation_output\",\r\n verbose=debug,\r\n )\r\n\r\n overall_chain = SequentialChain(\r\n chains=[validation_agent],\r\n input_variables=[\"query\", \"format_instructions\"],\r\n output_variables=[\"validation_output\"],\r\n verbose=debug,\r\n )\r\n\r\n return overall_chain\r\n\r\n def _set_up_agent_chain(self, debug=True):\r\n \"\"\"\r\n\r\n Parameters\r\n ----------\r\n debug\r\n\r\n Returns\r\n -------\r\n\r\n \"\"\"\r\n travel_agent = LLMChain(\r\n llm=self.chat_model,\r\n prompt=self.itinerary_prompt.chat_prompt,\r\n verbose=debug,\r\n output_key=\"agent_suggestion\",\r\n )\r\n\r\n parser = LLMChain(\r\n"} +{"file_name": "025af6bf-a6a4-4b1d-91dd-648fb50d62fd.png", "code": " llm=self.chat_model,\r\n prompt=self.mapping_prompt.chat_prompt,\r\n output_parser=self.mapping_prompt.parser,\r\n verbose=debug,\r\n output_key=\"mapping_list\",\r\n )\r\n\r\n overall_chain = SequentialChain(\r\n chains=[travel_agent, parser],\r\n input_variables=[\"query\", \"format_instructions\"],\r\n output_variables=[\"agent_suggestion\", \"mapping_list\"],\r\n verbose=debug,\r\n )\r\n\r\n return overall_chain\r\n\r\n def suggest_travel(self, query):\r\n \"\"\"\r\n\r\n Parameters\r\n ----------\r\n query\r\n\r\n Returns\r\n -------\r\n\r\n \"\"\"\r\n self.logger.info(\"Validating query\")\r\n t1 = time.time()\r\n self.logger.info(\r\n \"Calling validation (model is {}) on user input\".format(\r\n self.chat_model.model_name\r\n )\r\n )\r\n validation_result = self.validation_chain(\r\n {\r\n \"query\": query,\r\n \"format_instructions\": self.validation_prompt.parser.get_format_instructions(),\r\n }\r\n )\r\n"} +{"file_name": "f92fbad0-5450-44b3-a2e0-bb32a2065b1a.png", "code": "\r\n validation_test = validation_result[\"validation_output\"].dict()\r\n t2 = time.time()\r\n self.logger.info(\"Time to validate request: {}\".format(round(t2 - t1, 2)))\r\n\r\n if validation_test[\"plan_is_valid\"].lower() == \"no\":\r\n self.logger.warning(\"User request was not valid!\")\r\n print(\"\\n######\\n Travel plan is not valid \\n######\\n\")\r\n print(validation_test[\"updated_request\"])\r\n return None, None, validation_result\r\n\r\n else:\r\n # plan is valid\r\n self.logger.info(\"Query is valid\")\r\n self.logger.info(\"Getting travel suggestions\")\r\n t1 = time.time()\r\n\r\n self.logger.info(\r\n \"User request is valid, calling agent (model is {})\".format(\r\n self.chat_model.model_name\r\n )\r\n )\r\n\r\n agent_result = self.agent_chain(\r\n {\r\n \"query\": query,\r\n \"format_instructions\": self.mapping_prompt.parser.get_format_instructions(),\r\n }\r\n )\r\n\r\n trip_suggestion = agent_result[\"agent_suggestion\"]\r\n list_of_places = agent_result[\"mapping_list\"].dict()\r\n t2 = time.time()\r\n self.logger.info(\"Time to get suggestions: {}\".format(round(t2 - t1, 2)))\r\n\r\n return trip_suggestion, list_of_places, validation_result\r\n\r\n\r\nsecrets = load_secets() \r\n\r\n"} +{"file_name": "b8b8939e-a32a-4dc3-b2df-50935e2a0832.png", "code": "query = \"\"\"\r\n I want to do a three day trip across Kenya's Rift valley.\r\n \"\"\"\r\ntravel_agent = Agent(\r\n open_ai_api_key=secrets['OPENAI_API_KEY'],\r\n debug=True,\r\n)\r\n\r\nitinerary, list_of_places, validation = travel_agent.suggest_travel(query)\r\nprint(validation)\r\nprint(itinerary)\r\nprint(list_of_places)"} +{"file_name": "e35c3863-5628-4b76-9dc3-7962aacf7e85.png", "code": "from langchain.prompts.chat import (\r\n ChatPromptTemplate,\r\n SystemMessagePromptTemplate,\r\n HumanMessagePromptTemplate,\r\n)\r\nfrom langchain.output_parsers import PydanticOutputParser\r\nfrom pydantic import BaseModel, Field\r\nfrom dotenv import load_dotenv\r\nfrom pathlib import Path\r\nimport os\r\nfrom models import Trip\r\n\r\n\r\ndef load_secets():\r\n load_dotenv()\r\n env_path = Path(\".\") / \".env\"\r\n load_dotenv(dotenv_path=env_path)\r\n\r\n open_ai_key = os.getenv(\"OPENAI_API_KEY\")\r\n google_palm_key = os.getenv(\"GOOGLE_PALM_API_KEY\")\r\n\r\n return {\r\n \"OPENAI_API_KEY\": open_ai_key,\r\n \"GOOGLE_PALM_API_KEY\": google_palm_key,\r\n }\r\n\r\n\r\nclass Validation(BaseModel):\r\n plan_is_valid: str = Field(\r\n description=\"This field is 'yes' if the plan is feasible, 'no' otherwise\"\r\n )\r\n updated_request: str = Field(description=\"Your update to the plan\")\r\n\r\n\r\nclass ValidationTemplate(object):\r\n def __init__(self):\r\n self.system_template = \"\"\"\r\n You are a travel agent who helps users make exciting travel plans.\r\n\r\n The user's request will be denoted by four hashtags. Determine if the user's\r\n"} +{"file_name": "462c6e7d-ddd8-44e6-8878-e9744fb5e4f5.png", "code": " request is reasonable and achievable within the constraints they set.\r\n\r\n A valid request should contain the following:\r\n - A start and end location\r\n - A trip duration that is reasonable given the start and end location\r\n - Some other details, like the user's interests and/or preferred mode of transport\r\n\r\n Any request that contains potentially harmful activities is not valid, regardless of what\r\n other details are provided.\r\n\r\n If the request is not valid, set\r\n plan_is_valid = 0 and use your travel expertise to update the request to make it valid,\r\n keeping your revised request shorter than 100 words.\r\n\r\n If the request seems reasonable, then set plan_is_valid = 1 and\r\n don't revise the request.\r\n\r\n {format_instructions}\r\n \"\"\"\r\n\r\n self.human_template = \"\"\"\r\n ####{query}####\r\n \"\"\"\r\n\r\n self.parser = PydanticOutputParser(pydantic_object=Validation)\r\n\r\n self.system_message_prompt = SystemMessagePromptTemplate.from_template(\r\n self.system_template,\r\n partial_variables={\r\n \"format_instructions\": self.parser.get_format_instructions()\r\n },\r\n )\r\n self.human_message_prompt = HumanMessagePromptTemplate.from_template(\r\n self.human_template, input_variables=[\"query\"]\r\n )\r\n\r\n self.chat_prompt = ChatPromptTemplate.from_messages(\r\n [self.system_message_prompt, self.human_message_prompt]\r\n )\r\n\r\n"} +{"file_name": "4b31c15f-39d8-430f-ab97-894e8ea50615.png", "code": "\r\nclass ItineraryTemplate(object):\r\n def __init__(self):\r\n self.system_template = \"\"\"\r\n You are a travel agent who helps users make exciting travel plans.\r\n\r\n The user's request will be denoted by four hashtags. Convert the\r\n user's request into a detailed itinerary describing the places\r\n they should visit and the things they should do.\r\n\r\n Try to include the specific address of each location.\r\n\r\n Remember to take the user's preferences and timeframe into account,\r\n and give them an itinerary that would be fun and doable given their constraints.\r\n\r\n Return the itinerary as a bulleted list with clear start and end locations.\r\n Be sure to mention the type of transit for the trip.\r\n If specific start and end locations are not given, choose ones that you think are suitable and give specific addresses.\r\n Your output must be the list and nothing else.\r\n \"\"\"\r\n\r\n self.human_template = \"\"\"\r\n ####{query}####\r\n \"\"\"\r\n\r\n self.system_message_prompt = SystemMessagePromptTemplate.from_template(\r\n self.system_template,\r\n )\r\n self.human_message_prompt = HumanMessagePromptTemplate.from_template(\r\n self.human_template, input_variables=[\"query\"]\r\n )\r\n\r\n self.chat_prompt = ChatPromptTemplate.from_messages(\r\n [self.system_message_prompt, self.human_message_prompt]\r\n )\r\n\r\n\r\nclass MappingTemplate(object):\r\n def __init__(self):\r\n self.system_template = \"\"\"\r\n"} +{"file_name": "928599da-e19b-4175-aed1-e0594bb5e908.png", "code": " You an agent who converts detailed travel plans into a simple list of locations.\r\n\r\n The itinerary will be denoted by four hashtags. Convert it into\r\n list of places that they should visit. Try to include the specific address of each location.\r\n\r\n Your output should always contain the start and end point of the trip, and may also include a list\r\n of waypoints. It should also include a mode of transit. The number of waypoints cannot exceed 20.\r\n If you can't infer the mode of transit, make a best guess given the trip location.\r\n\r\n For example:\r\n\r\n ####\r\n Itinerary for a 2-day driving trip within London:\r\n - Day 1:\r\n - Start at Buckingham Palace (The Mall, London SW1A 1AA)\r\n - Visit the Tower of London (Tower Hill, London EC3N 4AB)\r\n - Explore the British Museum (Great Russell St, Bloomsbury, London WC1B 3DG)\r\n - Enjoy shopping at Oxford Street (Oxford St, London W1C 1JN)\r\n - End the day at Covent Garden (Covent Garden, London WC2E 8RF)\r\n - Day 2:\r\n - Start at Westminster Abbey (20 Deans Yd, Westminster, London SW1P 3PA)\r\n - Visit the Churchill War Rooms (Clive Steps, King Charles St, London SW1A 2AQ)\r\n - Explore the Natural History Museum (Cromwell Rd, Kensington, London SW7 5BD)\r\n - End the trip at the Tower Bridge (Tower Bridge Rd, London SE1 2UP)\r\n #####\r\n\r\n Output:\r\n Start: Buckingham Palace, The Mall, London SW1A 1AA\r\n End: Tower Bridge, Tower Bridge Rd, London SE1 2UP\r\n Waypoints: [\"Tower of London, Tower Hill, London EC3N 4AB\", \"British Museum, Great Russell St, Bloomsbury, London WC1B 3DG\", \"Oxford St, London W1C 1JN\", \"Covent Garden, London WC2E 8RF\",\"Westminster, London SW1A 0AA\", \"St. James's Park, London\", \"Natural History Museum, Cromwell Rd, Kensington, London SW7 5BD\"]\r\n Transit: driving\r\n\r\n Transit can be only one of the following options: \"driving\", \"train\", \"bus\" or \"flight\".\r\n\r\n {format_instructions}\r\n \"\"\"\r\n\r\n self.human_template = \"\"\"\r\n ####{agent_suggestion}####\r\n \"\"\"\r\n"} +{"file_name": "3829d17b-8136-428c-9d6a-a70214aa0b0c.png", "code": "\r\n self.parser = PydanticOutputParser(pydantic_object=Trip)\r\n\r\n self.system_message_prompt = SystemMessagePromptTemplate.from_template(\r\n self.system_template,\r\n partial_variables={\r\n \"format_instructions\": self.parser.get_format_instructions()\r\n },\r\n )\r\n self.human_message_prompt = HumanMessagePromptTemplate.from_template(\r\n self.human_template, input_variables=[\"agent_suggestion\"]\r\n )\r\n\r\n self.chat_prompt = ChatPromptTemplate.from_messages(\r\n [self.system_message_prompt, self.human_message_prompt]\r\n )"} +{"file_name": "50ec7b40-8cbb-49b7-8372-36fbece93d6c.png", "code": "from redis import Redis\r\n\r\nredis = Redis()\r\n\r\npage = '1'\r\nres = redis.zrevrange(f'quote_authors', 0, 10, withscores=True)\r\nprint(res)"} +{"file_name": "9a106eee-e98e-472e-93e6-0039d8b0bfcb.png", "code": "from youtube.models import (\r\n Playlist, PlaylistItem, Search\r\n)\r\nfrom api.database.models import Video\r\nfrom youtube import YouTube\r\nfrom youtube.resources.schemas import (\r\n SearchPart, SearchFilter, SearchOptionalParameters\r\n)\r\nfrom youtube.resources.schemas import (\r\n YouTubeResponse, CreateStatus, CreatePlaylistSnippet, CreatePlaylistSchema,\r\n VideoResourceId, CreatePlaylistItemSnippet, CreatePlaylistItem, YouTubeRequest\r\n)\r\nfrom redis import Redis\r\nimport logging\r\nfrom dotenv import load_dotenv\r\nfrom config import RedisSettings, Config\r\nfrom api.database.models import Channel\r\nfrom api.database.crud import get_all_channels, get_channel_by_title\r\nfrom api.database.database import get_db\r\nfrom json import loads, dumps\r\nfrom datetime import timedelta\r\n\r\n\r\nload_dotenv()\r\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S')\r\n\r\nredis_config: RedisSettings = RedisSettings()\r\nredis: Redis = Redis(\r\n host=redis_config.redis_host,\r\n port=redis_config.redis_port,\r\n decode_responses=True\r\n)\r\nconfig: Config = Config()\r\n\r\n\r\ndef get_youtube_client(client_secret_file: str) -> YouTube:\r\n logging.info('Creating the YouTube client.')\r\n youtube: YouTube = YouTube(client_secret_file=client_secret_file)\r\n logging.info('Authenticating the user.')\r\n youtube.authenticate()\r\n"} +{"file_name": "cbf39e86-865b-4949-800a-90f4dbcddec1.png", "code": " logging.info('Request authenticated.')\r\n return youtube\r\n\r\n\r\ndef create_playlist(title: str, \r\n description: str, \r\n youtube: YouTube,\r\n default_language: str = 'en', \r\n privacy_status: str = 'public') -> Playlist:\r\n snippet: CreatePlaylistSnippet = CreatePlaylistSnippet(\r\n title=title,\r\n description=description,\r\n defaultLanguage=default_language\r\n )\r\n create_schema: CreatePlaylistSchema = CreatePlaylistSchema(\r\n snippet=snippet,\r\n status=CreateStatus(privacyStatus=privacy_status)\r\n )\r\n playlist: Playlist = youtube.insert_playlist(create_schema)\r\n return playlist\r\n\r\n\r\ndef get_playlists() -> dict[str, dict]:\r\n playlists: dict[str, str] = {\r\n 'Daily Videos': {\r\n 'id': 'PL_26vmg8W_AcEEl_Bo2AhziS-93r6b8bu',\r\n 'url': 'https://www.youtube.com/playlist?list=PL_26vmg8W_AcEEl_Bo2AhziS-93r6b8bu'\r\n }\r\n }\r\n return playlists\r\n\r\n\r\ndef get_playlist_id(name: str) -> str:\r\n playlists: dict[str, dict] = get_playlists()\r\n return playlists[name]['id']\r\n \r\n \r\ndef get_playlist_url(name: str) -> str:\r\n playlists: dict[str, dict] = get_playlists()\r\n return playlists[name]['url']\r\n"} +{"file_name": "7a43d82a-546b-4287-b4f4-0f90e98ddf3f.png", "code": "\r\n\r\ndef get_channels() -> list[Channel]:\r\n logging.info('Getting the channel details from the database.')\r\n channels: list[Channel] = get_all_channels(get_db)\r\n logging.info('Fetched the channel details from the database.')\r\n return channels\r\n\r\ndef get_channel_names() -> list[str]:\r\n logging.info('Fetching the channel names.')\r\n channels: list[Channel] = get_all_channels(get_db)\r\n logging.info('Fetched the channel names.')\r\n return [channel.title for channel in channels]\r\n\r\n\r\ndef get_channel_id(name: str) -> str:\r\n channel: Channel = get_channel_by_title(name, get_db)\r\n return channel.id\r\n\r\n\r\ndef find_latest_video(channel_id: str, youtube_client: YouTube) -> Video:\r\n if redis.get(name=f'latest:{channel_id}'):\r\n logging.info('Found video in cache, retrieving it.')\r\n video_str: str = redis.get(name=f'latest:{channel_id}')\r\n video: Video = Video(**loads(video_str))\r\n logging.info('Successfully retrieved video from cache.')\r\n else:\r\n logging.info('Video missing from cache, retrieving from youtube.')\r\n part: SearchPart = SearchPart()\r\n optional_parameters: SearchOptionalParameters = SearchOptionalParameters(\r\n q='',\r\n maxResults=1,\r\n type=['video'],\r\n channelId=channel_id,\r\n order='date'\r\n )\r\n search: YouTubeRequest = YouTubeRequest(part=part, optional_parameters=optional_parameters)\r\n response: YouTubeResponse = youtube_client.search(search)\r\n logging.info('Retrieved the latest video from youtube.')\r\n search_responses: list[Search] = response.items\r\n"} +{"file_name": "d294e2e3-8a17-43d2-a748-57d7d72c5f39.png", "code": " search_resp: Search = search_responses[0]\r\n video: Video = Video(**search_resp.model_dump(exclude={'thumbnails'}))\r\n video_str: str = dumps(video.dict(), default=str)\r\n logging.info('caching the retrieved video.')\r\n redis.set(name=f'latest:{channel_id}', value=video_str)\r\n logging.info('Successfully cached the retrieved video.')\r\n logging.info('Setting an expiration time of %d for the cached video.', config.expiration_time)\r\n redis.setex(f'latest:{channel_id}', value=video_str, time=timedelta(seconds=config.expiration_time))\r\n return video\r\n\r\ndef add_video_to_playlist(video: Video, \r\n playlist_id: str, \r\n youtube: YouTube, \r\n position: int = 0) -> PlaylistItem | None:\r\n playlist_item: PlaylistItem = None\r\n if redis.setnx(name=f'{playlist_id}:{video.resource_id}', value=video.resource_id):\r\n logging.info('The video \"%s\" odes not exist in playlist, adding it.', video.title)\r\n resource_id: VideoResourceId = VideoResourceId(videoId=video.resource_id)\r\n snippet: CreatePlaylistItemSnippet = CreatePlaylistItemSnippet(\r\n playlistId=playlist_id,\r\n resourceId=resource_id,\r\n position=position\r\n )\r\n create: CreatePlaylistItem = CreatePlaylistItem(snippet=snippet)\r\n logging.info('Inserting \"%s\" into playlist.', video.title)\r\n playlist_item: PlaylistItem = youtube.insert_playlist_item(create)\r\n logging.info('Inserted \"%s\" to playlist.', video.title)\r\n logging.info('Adding \"%s\" to \"unwatched list\".', video.title)\r\n video.save()\r\n logging.info('Added \"%s\" to \"unwatched list\".', video.title)\r\n logging.info('Setting the video expiration time of %d for video.', config.expiration_time)\r\n video.expire(num_seconds=3600)\r\n return playlist_item\r\n\r\n\r\ndef delete_video_playlist(playlist_item_id: str, youtube: YouTube) -> None:\r\n youtube.delete_playlist_item(playlist_item_id)\r\n \r\n \r\ndef workflow(youtube: YouTube, channel_names: list[str], playlist_name: str = 'Daily Videos'):\r\n"} +{"file_name": "4ee1c672-c98e-474c-9a80-cc6af74e92e9.png", "code": " logging.info('Getting the playlist id for %s', playlist_name)\r\n playlist_id: str = get_playlist_id(playlist_name)\r\n logging.info('The playlist %s id is %s', playlist_name, playlist_id)\r\n playlist_items: list[str] = []\r\n for channel_name in channel_names:\r\n logging.info('Getting the channel id for \"%s\".', channel_name)\r\n channel_id: str = get_channel_id(channel_name)\r\n logging.info('The channel \"%s\" has id \"%s\".', channel_name, channel_id)\r\n logging.info('Getting the latest video for channel \"%s\".', channel_name)\r\n latest_video: Video = find_latest_video(channel_id, youtube)\r\n logging.info('Fetched the latest video for %s.', channel_name)\r\n logging.info('The latest video for channel: \"%s\" is :\"%s\"', channel_name, latest_video.title)\r\n logging.info('Creating a playlist item for video: \"%s\"', latest_video.title)\r\n playlist_item: PlaylistItem = add_video_to_playlist(latest_video, playlist_id, youtube, position=0)\r\n if playlist_item:\r\n logging.info('Added \"%s\" to \"%s\".', latest_video.title, playlist_name)\r\n playlist_items.append({\r\n 'id': playlist_item.id,\r\n 'title': playlist_item.snippet.title\r\n })\r\n else:\r\n logging.info('The video \"%s\" already exists in playlist \"%s\".', latest_video.title, playlist_name)\r\n logging.info('Video id: %s.', latest_video.resource_id)\r\n if playlist_items:\r\n logging.info('Added all the latest videos to the playlist.')\r\n else:\r\n logging.info('Did not add any videos to playlist.')\r\n return playlist_items\r\n\r\n\r\nclient_secret_file: str = 'client_secret.json'\r\nyoutube: YouTube = get_youtube_client(client_secret_file)\r\n# playlist_id: str = get_playlist_id('Daily Videos')\r\n# channel_id: str = get_channel_id('CNBC')\r\n# latest_video: Video = find_latest_video(channel_id, youtube)\r\n# playlist_item: PlaylistItem = add_video_to_playlist(latest_video.resource_id, playlist_id, youtube, position=0)\r\n# delete_video_playlist('UExfMjZ2bWc4V19BY0VFbF9CbzJBaHppUy05M3I2YjhidS41NkI0NEY2RDEwNTU3Q0M2', youtube)\r\n# ids = ['UExfMjZ2bWc4V19BY0VFbF9CbzJBaHppUy05M3I2YjhidS41NkI0NEY2RDEwNTU3Q0M2', 'UExfMjZ2bWc4V19BY0VFbF9CbzJBaHppUy05M3I2YjhidS4yODlGNEE0NkRGMEEzMEQy', 'UExfMjZ2bWc4V19BY0VFbF9CbzJBaHppUy05M3I2YjhidS4wMTcyMDhGQUE4NTIzM0Y5', 'UExfMjZ2bWc4V19BY0VFbF9CbzJBaHppUy05M3I2YjhidS41MjE1MkI0OTQ2QzJGNzNG', 'UExfMjZ2bWc4V19BY0VFbF9CbzJBaHppUy05M3I2YjhidS4wOTA3OTZBNzVEMTUzOTMy', 'UExfMjZ2bWc4V19BY0VFbF9CbzJBaHppUy05M3I2YjhidS4xMkVGQjNCMUM1N0RFNEUx', 'UExfMjZ2bWc4V19BY0VFbF9CbzJBaHppUy05M3I2YjhidS41MzJCQjBCNDIyRkJDN0VD', 'UExfMjZ2bWc4V19BY0VFbF9CbzJBaHppUy05M3I2YjhidS5DQUNERDQ2NkIzRUQxNTY1', 'UExfMjZ2bWc4V19BY0VFbF9CbzJBaHppUy05M3I2YjhidS45NDk1REZENzhEMzU5MDQz']\r\n# for id in ids:\r\n# delete_video_playlist(id, youtube)\r\n"} +{"file_name": "111de771-8552-4d99-920a-58df81c64b76.png", "code": "channel_names: list[str] = get_channel_names()\r\nplaylist_name: str = 'Daily Videos'\r\nplaylist_items: list[str] = workflow(youtube, channel_names)\r\n\r\n# print(get_channel_id('Asianometry'))\r\n# print(redis.setex(name='PL_26vmg8W_AcEEl_Bo2AhziS-93r6b8bu:DqkZCzjdtbw', time=1, value=''))\r\n# print(redis.setex(name='PL_26vmg8W_AcEEl_Bo2AhziS-93r6b8bu:VzW_BtXSw6A', time=1, value=''))\r\n# print(redis.get(name='PL_26vmg8W_AcEEl_Bo2AhziS-93r6b8bu:DqkZCzjdtbw'))\r\n# print(find_latest_video('UC1LpsuAUaKoMzzJSEt5WImw', youtube))\r\n# channels: list[Channel] = get_all_channels(get_db)\r\n# latest_videos: list[Video] = [find_latest_video(channel.id, youtube) for channel in channels]\r\n# videos: list[Video] = Video.find().all()\r\n# for channel in channels:\r\n# redis.setex(f'latest:{channel.id}', value='video_str', time=1)\r\n# for video in latest_videos:\r\n# pl_id: str = 'PL_26vmg8W_AcEEl_Bo2AhziS-93r6b8bu'\r\n# redis.setex(name=f'{pl_id}:{video.resource_id}', time=1, value='')\r\n# for video in videos:\r\n# video.expire(num_seconds=1)"} +{"file_name": "362a3566-5534-4aef-8307-37b3320beb58.png", "code": "from api import create_app\r\n\r\n\r\napp = create_app()"} +{"file_name": "a11348b1-2b7f-4140-a4e0-d915e69d2ef4.png", "code": "from datetime import datetime\r\nfrom uuid import uuid4\r\nfrom redis_om import Migrator\r\nfrom redis_om.model import NotFoundError\r\nfrom api.database.models import Video\r\nfrom youtube.models import Search\r\nfrom youtube import YouTube\r\nfrom youtube.resources.schemas import(\r\n SearchFilter, SearchPart, SearchOptionalParameters, YouTubeRequest, YouTubeResponse\r\n)\r\n\r\n\r\nclient_secret_file: str = 'client_secret.json'\r\nyoutube: YouTube = YouTube(client_secret_file=client_secret_file)\r\nyoutube.authenticate()\r\n\r\nMigrator().run()\r\n\r\n# part: SearchPart = SearchPart()\r\n# optional_parameters: SearchOptionalParameters = SearchOptionalParameters(\r\n# q='Python programming',\r\n# maxResults=2,\r\n# type=['video']\r\n# )\r\n# search: YouTubeRequest = YouTubeRequest(part=part, optional_parameters=optional_parameters)\r\n# response: YouTubeResponse = youtube.search(search)\r\n# search_resp: Search = response.items[0]\r\n\r\n# video: Video = Video(**search_resp.model_dump(exclude={'thumbnails'}))\r\n# print(video)\r\nvideos: list[Video] = Video.find().all()\r\n# for video in videos:\r\n# video.expire(num_seconds=1)\r\nprint(videos)"} +{"file_name": "e5b703bb-4bca-4037-9972-79390b3b1b00.png", "code": "from celery_app import celery_app\r\n\r\n\r\nif __name__ == '__main__':\r\n args = ['worker', '--loglevel=INFO']\r\n # celery_app.autodiscover_tasks(['tasks'])\r\n celery_app.worker_main(argv=args)"} +{"file_name": "59db80e7-e1fc-4b69-83f1-d0dcf1716cf4.png", "code": "from pydantic_settings import BaseSettings\r\nfrom dotenv import load_dotenv\r\n\r\nload_dotenv()\r\n\r\n\r\nclass PostgresSettings(BaseSettings):\r\n postgres_host: str\r\n postgres_port: int\r\n postgres_user: str\r\n postgres_password: str\r\n postgres_db: str\r\n \r\n @property\r\n def sqlalchemy_db_url(self) -> str:\r\n return f\"postgresql://{self.postgres_user}:{self.postgres_password}@{self.postgres_host}:{self.postgres_port}/{self.postgres_db}\"\r\n\r\n\r\nclass RedisSettings(BaseSettings):\r\n redis_host: str\r\n redis_port: int\r\n\r\n \r\nclass CeleryConfig(BaseSettings):\r\n celery_broker_url: str\r\n celery_result_backend: str\r\n\r\n\r\nclass Config(BaseSettings):\r\n expiration_time: int\r\n"} +{"file_name": "c9d8f4ec-c4b3-47de-86dc-0a96d9770baf.png", "code": "from youtube import YouTube\r\nfrom youtube.models import Channel\r\nfrom youtube.resources.schemas import YouTubeResponse\r\n\r\n\r\nclient_secret_file: str = 'client_secret.json'\r\nyoutube: YouTube = YouTube(client_secret_file=client_secret_file)\r\nyoutube.authenticate()\r\n\r\nchannel_name: str = 'Ticker Symbol You'\r\nsearch_response: YouTubeResponse = youtube.find_channel_by_name(channel_name)\r\nprint(search_response.items[0])"} +{"file_name": "3e7e4e27-8b14-4c7b-9116-184944d6d2d8.png", "code": "from json import load\r\nfrom typing import Any\r\nimport streamlit as st\r\nfrom youtube import YouTube\r\nfrom youtube.models import Search, Video\r\nfrom youtube.resources.schemas import (\r\n CreatePlaylistSchema, CreatePlaylistSnippet, CreateStatus, CreatePlaylistItem, CreatePlaylistItemSnippet,\r\n VideoResourceId, YouTubeRequest, SearchPart, SearchOptionalParameters, SearchFilter\r\n)\r\nfrom typing import Any\r\n\r\n\r\nclient_secret_file: str = 'client_secret.json'\r\nyoutube: YouTube = YouTube(client_secret_file=client_secret_file)\r\nyoutube.authenticate()\r\n\r\n\r\ndef load_data(file_name: str = 'live-news.json') -> dict[str, Any]:\r\n \"\"\"Load a json file.\"\"\"\r\n with open(file_name, 'r', encoding='utf-8') as f:\r\n data: dict[str, Any] = load(f)\r\n return data\r\n\r\ndef search_news(text: str) -> list[dict[str, Any]]:\r\n part: SearchPart = SearchPart()\r\n optional_parameters: SearchOptionalParameters = SearchOptionalParameters(\r\n q=text,\r\n maxResults=5,\r\n eventType='live',\r\n type=['video']\r\n )\r\n search = YouTubeRequest(part=part, optional_parameters=optional_parameters)\r\n results: list[Search] = youtube.search(search).items\r\n results: dict[str, Any] = [result.model_dump() for result in results]\r\n return results\r\n\r\ndef search_video(video_id: str) -> dict[str, Any]:\r\n return youtube.find_video_by_id(video_id).model_dump()\r\n\r\ndef get_thumbnail_url(data: dict[str, Any]) -> str:\r\n"} +{"file_name": "edeb847b-cd6b-46b3-a21e-3b880ee119e8.png", "code": " \"\"\"Get a video thumbnail.\"\"\"\r\n thumbnail_key: dict[str, Any] = {}\r\n if data.get('high'):\r\n thumbnail_key = data.get('high')\r\n elif data.get('medium'):\r\n thumbnail_key = data.get('high')\r\n elif data.get('standard'):\r\n thumbnail_key = data.get('high')\r\n else:\r\n thumbnail_key = data.get('high')\r\n return thumbnail_key.get('url')\r\n\r\ndef get_caption(data: dict[str, Any]) -> str:\r\n return data.get('title', 'Caption')\r\n\r\ndef get_description(data: dict[str, Any]) -> str:\r\n return data.get('description', 'Description')\r\n\r\nst.title(\"Live News Bot.\")\r\n\r\n# React to user input\r\nif prompt := st.chat_input(\"What is up?\"):\r\n # Display user message in chat message container\r\n with st.chat_message('user'):\r\n st.markdown(f'Finding latest news videos mentioning \"{prompt}\"')\r\n # Display assistant response in chat message container\r\n with st.spinner('Searching for the latest live news coverage...'):\r\n search_results: list[dict[str, Any]] = search_news(prompt)\r\n # search_results: list[dict[str, Any]] = load_data()\r\n for search_result in search_results: \r\n video_id: str = search_result.get('resource_id')\r\n with st.spinner('Loading the news piece...'):\r\n video: dict[str, Any] = search_video(video_id)\r\n with st.chat_message(\"assistant\"):\r\n url: str = f'https://www.youtube.com/watch?v={video_id}'\r\n st.video(url)\r\n st.markdown(get_description(video['snippet']))\r\n\r\n# videos: list[dict[str, Any]] = load_data()\r\n# video: dict[str, Any] = search_video(videos[0].get('resource_id'))\r\n"} +{"file_name": "75f99cac-24cd-4f67-9263-005f0e547883.png", "code": "# print(video)"} +{"file_name": "942295ee-5887-4c9b-95e7-c63f0308af0b.png", "code": "from langchain.agents import AgentType, initialize_agent\r\nfrom langchain.chat_models import ChatOpenAI\r\nfrom langchain.tools import BaseTool, StructuredTool, Tool, tool\r\nfrom dotenv import load_dotenv\r\nfrom pydantic.v1 import BaseModel, Field\r\nfrom typing import Optional, Type\r\nfrom pydantic_settings import BaseSettings\r\nfrom langchain.callbacks.manager import (\r\n AsyncCallbackManagerForToolRun,\r\n CallbackManagerForToolRun,\r\n)\r\nfrom youtube import YouTube\r\nfrom youtube.models import Search\r\nfrom youtube.resources.schemas import (\r\n CreatePlaylistSchema, CreatePlaylistSnippet, CreateStatus, CreatePlaylistItem, CreatePlaylistItemSnippet,\r\n VideoResourceId, YouTubeRequest, SearchPart, SearchOptionalParameters, SearchFilter, \r\n YouTubeResponse\r\n)\r\n\r\n\r\nload_dotenv()\r\n\r\nclass Config(BaseSettings):\r\n open_ai_token: str\r\n\r\nconfig: Config = Config()\r\nclient_secret_file: str = 'client_secret.json'\r\nyoutube: YouTube = YouTube(client_secret_file=client_secret_file)\r\nyoutube.authenticate()\r\n\r\nclass YouTubeChannelTitleSearch(BaseModel):\r\n title: str\r\n\r\n\r\nclass YouTubeChannelTitleSearchTool(BaseTool):\r\n name = \"youtube_title_channel_search\"\r\n description = \"\"\"\r\n useful when you need to find information about a channel when provided with the title. \r\n To use this tool you must provide the channel title.\r\n \"\"\"\r\n"} +{"file_name": "502a49fb-3a1a-4f1c-bcb1-45cef1cb9269.png", "code": " args_schema: Type[BaseModel] = YouTubeChannelTitleSearch\r\n\r\n def _run(\r\n self, title: str, \r\n run_manager: Optional[CallbackManagerForToolRun] = None\r\n ) -> str:\r\n \"\"\"Use the tool.\"\"\"\r\n youtube_response: YouTubeResponse = youtube.find_channel_by_name(title)\r\n search_results: list[Search] = youtube_response.items\r\n return search_results[0]\r\n\r\n async def _arun(\r\n self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None\r\n ) -> str:\r\n \"\"\"Use the tool asynchronously.\"\"\"\r\n raise NotImplementedError(\"Calculator does not support async\")\r\n \r\n\r\nclass YouTubeChannelSearch(BaseModel):\r\n id: str\r\n\r\n\r\nclass YouTubeChannelSearchTool(BaseTool):\r\n name = \"youtube_channel_search\"\r\n description = \"\"\"\r\n useful when you ned to find information about a channel when provided with the channel id. \r\n To use this tool you must provide the channel id.\r\n \"\"\"\r\n args_schema: Type[BaseModel] = YouTubeChannelSearch\r\n\r\n def _run(\r\n self, \r\n id: str, \r\n run_manager: Optional[CallbackManagerForToolRun] = None\r\n ) -> str:\r\n \"\"\"Use the tool.\"\"\"\r\n youtube_channel: YouTubeResponse = youtube.find_channel_by_id(id)\r\n return youtube_channel\r\n\r\n async def _arun(\r\n"} +{"file_name": "1d97e630-c984-4ec8-a93b-7b27223c8164.png", "code": " self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None\r\n ) -> str:\r\n \"\"\"Use the tool asynchronously.\"\"\"\r\n raise NotImplementedError(\"Calculator does not support async\")\r\n\r\n\r\nclass YouTubeChannelVideoSearchTool(BaseTool):\r\n name = \"youtube_channel_video_search\"\r\n description = \"useful for when you need to answer questions about videos for a youtube channel\"\r\n args_schema: Type[BaseModel] = YouTubeChannelSearch\r\n\r\n def _run(\r\n self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None\r\n ) -> str:\r\n \"\"\"Use the tool.\"\"\"\r\n return ''\r\n\r\n async def _arun(\r\n self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None\r\n ) -> str:\r\n \"\"\"Use the tool asynchronously.\"\"\"\r\n raise NotImplementedError(\"Calculator does not support async\")\r\n\r\nllm = ChatOpenAI(\r\n temperature=0, \r\n openai_api_key=config.open_ai_token,\r\n )\r\n\r\ntools = [\r\n YouTubeChannelTitleSearchTool(), \r\n YouTubeChannelVideoSearchTool(),\r\n YouTubeChannelSearchTool()\r\n ]\r\nagent = initialize_agent(\r\n tools, \r\n llm, \r\n agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, \r\n verbose=True,\r\n handle_parsing_errors=True\r\n)\r\n"} +{"file_name": "a54f424e-7477-404c-afe1-eb0d8c150f53.png", "code": "\r\nquery: str = \"\"\"\r\nFind the id of the youtube channel \"Ark Invest\", then using the id, find the number of subcribers \r\nfor the channel.\r\n\"\"\"\r\n\r\nres = agent.run(query)\r\nprint(res)"} +{"file_name": "160f3890-b702-4246-80c2-cc1ba57f7122.png", "code": "from celery import Celery\r\nfrom config import CeleryConfig\r\n\r\n\r\ncelery_app: Celery = Celery(__name__)\r\ncelery_app.config_from_object(CeleryConfig)\r\ncelery_app.conf.beat_schedule = {\r\n 'clear-daily-playlist': {\r\n 'task': 'tasks.clear_daily_playlist',\r\n 'schedule': 10\r\n }\r\n }\r\ncelery_app.autodiscover_tasks(['tasks'])\r\n"} +{"file_name": "8748dc4c-a5fa-434d-a839-f6f8a2dde6c5.png", "code": "from celery_app import celery_app\r\nimport logging\r\nfrom play_list import get_youtube_client, get_channel_names, workflow\r\nfrom youtube import YouTube\r\n\r\n\r\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S')\r\n\r\n\r\n@celery_app.task\r\ndef create_daily_playlist() -> None:\r\n client_secret_file: str = 'client_secret.json'\r\n logging.info('Getting the youtube client.')\r\n youtube: YouTube = get_youtube_client(client_secret_file)\r\n logging.info('Getting the channel names.')\r\n channel_names: list[str] = get_channel_names()[:2]\r\n logging.info('Fetched the channel names: ')\r\n logging.info(channel_names)\r\n playlist_name: str = 'Daily Videos'\r\n logging.info('Fetching and adding latest videos to the playlist \"%s\".', playlist_name)\r\n playlist_items: list[str] = workflow(youtube, channel_names, playlist_name)\r\n if playlist_items:\r\n logging.info('Added the following playlist items: ')\r\n logging.info(playlist_items)\r\n logging.info('Notifying via email.')\r\n return playlist_items\r\n\r\n\r\n@celery_app.task\r\ndef clear_daily_playlist() -> None:\r\n logging.info('Clearing daily playlist.')"} +{"file_name": "54eb166c-f4cf-4608-9fe5-680688f2cac5.png", "code": "from celery import Celery\r\nfrom config import CeleryConfig\r\n\r\n\r\ncelery_app: Celery = Celery(__name__)\r\ncelery_app.config_from_object(CeleryConfig)\r\ncelery_app.conf.beat_schedule = {\r\n 'create-daily-playlist': {\r\n 'task': 'tasks.create_daily_playlist',\r\n 'schedule': 10\r\n }\r\n }\r\ncelery_app.autodiscover_tasks(['tasks'])\r\n"} +{"file_name": "afae6183-8f48-4339-9bc6-c7d22698ab65.png", "code": "from youtube import YouTube\r\nfrom youtube.models import Search\r\nfrom youtube.resources.schemas import (\r\n CreatePlaylistSchema, CreatePlaylistSnippet, CreateStatus, CreatePlaylistItem, CreatePlaylistItemSnippet,\r\n VideoResourceId, YouTubeRequest, SearchPart, SearchOptionalParameters, SearchFilter,\r\n CommentThreadPart, CommentThreadFilter, CommentThreadOptionalParameters, YouTubeResponse,\r\n Part, Filter, OptionalParameters\r\n)\r\nfrom typing import Any\r\n\r\n\r\nclient_secret_file: str = 'client_secret.json'\r\nyoutube: YouTube = YouTube(client_secret_file=client_secret_file)\r\nyoutube.authenticate()\r\n# part = Part()\r\n# optional_parameters: OptionalParameters = OptionalParameters(\r\n# q='news',\r\n# maxResults=2,\r\n# eventType='live',\r\n# type=['video']\r\n# )\r\n# optional_parameters: OptionalParameters = OptionalParameters(\r\n# q='Python programming',\r\n# maxResults=2,\r\n# type=['video', 'channel', 'playlist'],\r\n# )\r\n# search = SearchSchema(part=part, optional_parameters=optional_parameters)\r\n\r\n# print(youtube.search(search))\r\n# search_iterator = youtube.get_search_iterator(search)\r\n# print(next(search_iterator))\r\n# print(next(search_iterator))\r\n# print(next(search_iterator))\r\n# print(youtube.find_video_by_id('rfscVS0vtbw'))\r\n# print(youtube.find_channel_by_name('East Meets West'))\r\n# print(youtube.get_video_ratings(['s7AvT7cGdSo']))\r\n# print(youtube.find_most_popular_video_by_regionn(region_code='KE'))\r\n# print(youtube.rate_video('s7AvT7cGdSo', 'like'))\r\n# print(youtube.update_video())\r\n# print(youtube.upload_video())\r\n"} +{"file_name": "c944ab14-9bb9-4891-9aec-2a27d13a8c35.png", "code": "# print(youtube.find_channel_by_name('The Joy Ride'))\r\n# print(youtube.find_channel_playlists('UCCjULCQvh2cQQLzYe4DC2Nw'))\r\n# print(youtube.find_my_playlists())\r\n# snippet: CreatePlaylistSnippet = CreatePlaylistSnippet(\r\n# title='Another Test Playlist 5'\r\n# )\r\n# playlist_schema: CreatePlaylistSchema = CreatePlaylistSchema(\r\n# snippet=snippet\r\n# )\r\n# print(youtube.insert_playlist(playlist_schema))\r\n# snippet: CreatePlaylistSnippet = CreatePlaylistSnippet(\r\n# title='Another Test Playlist 6',\r\n# description='New description.',\r\n# defaultLanguage='en'\r\n# )\r\n# playlist_schema: CreatePlaylistSchema = CreatePlaylistSchema(\r\n# snippet=snippet,\r\n# status=CreateStatus(privacyStatus='public')\r\n# )\r\n# print(youtube.update_playlist('PL_26vmg8W_AfJWy6SVtoSmtYWimhexwF7', playlist_schema))\r\n# print(youtube.delete_playlist('PL_26vmg8W_AfeubZM4lQJiBU8UbCl-R3L'))\r\n# print(youtube.find_channel_by_name('Isaac Author'))\r\n# print(youtube.find_playlist_items('PLgCR4dyaQRlq0SM6y7cQqspDnfqiNHfWf'))\r\n# print(youtube.find_playlist_items_by_ids(['UExnQ1I0ZHlhUVJscTBTTTZ5N2NRcXNwRG5mcWlOSGZXZi5EMEEwRUY5M0RDRTU3NDJC', \r\n# 'UExnQ1I0ZHlhUVJscTBTTTZ5N2NRcXNwRG5mcWlOSGZXZi45NDk1REZENzhEMzU5MDQz', \r\n# 'UExnQ1I0ZHlhUVJscTBTTTZ5N2NRcXNwRG5mcWlOSGZXZi41MzJCQjBCNDIyRkJDN0VD']))\r\n\r\n# video_resource: VideoResourceId = VideoResourceId(videoId='j0OvCL-6ic4')\r\n# snippet: CreatePlaylistItemSnippet = CreatePlaylistItemSnippet(\r\n# playlistId='PL_26vmg8W_AfJWy6SVtoSmtYWimhexwF7',\r\n# resourceId=video_resource\r\n# )\r\n# create = CreatePlaylistItem(snippet=snippet)\r\n# print(youtube.insert_playlist_item(create))\r\n# playlist_id='PL_26vmg8W_AfJWy6SVtoSmtYWimhexwF7', \r\n# playlist_item_id='UExfMjZ2bWc4V19BZkpXeTZTVnRvU210WVdpbWhleHdGNy41MzJCQjBCNDIyRkJDN0VD'\r\n# video_id='j0OvCL-6ic4' \r\n# position=6\r\n# print(youtube.update_playlist_item(playlist_id, playlist_item_id, video_id, position))\r\n# print(youtube.delete_playlist_item('UExnQ1I0ZHlhUVJsclpDZEtoN2ZQb1FwejVGYXI3Xy1HSS4yODlGNEE0NkRGMEEzMEQy'))\r\n"} +{"file_name": "d5e868bd-2209-447a-ac2b-a98978f2684f.png", "code": "# channel_list = ['Asianometry', '']\r\n# watched_list = []\r\n# playlist_id = 'PL_26vmg8W_AfJWy6SVtoSmtYWimhexwF7'\r\n# playlist_title = ''\r\n# channel_name = 'Asianometry'\r\n# channel_id = ''\r\n# video_id = ''\r\n# channels = youtube.find_channel_by_name(channel_name)\r\n# for channel in channels.items:\r\n# if channel.title == channel_name:\r\n# channel_id = channel.channel_id\r\n# part = Part()\r\n# optional_parameters: OptionalParameters = OptionalParameters(\r\n# q='',\r\n# maxResults=1,\r\n# type=['video'],\r\n# channelId=channel_id,\r\n# order='date'\r\n# )\r\n# search = SearchSchema(part=part, optional_parameters=optional_parameters)\r\n# search_result = youtube.search(search)\r\n# videos = search_result.items\r\n# latest_video = videos[0]\r\n# video_id = latest_video.resource_id\r\n# video_resource: VideoResourceId = VideoResourceId(videoId=video_id)\r\n# snippet: CreatePlaylistItemSnippet = CreatePlaylistItemSnippet(\r\n# playlistId=playlist_id,\r\n# resourceId=video_resource\r\n# )\r\n# create = CreatePlaylistItem(snippet=snippet)\r\n# playlist_item = youtube.insert_playlist_item(create)\r\n# print(f'Added {playlist_item.snippet.title} to the playlist.')\r\n# print(youtube.get_video_categories())\r\n\r\n# def search_save():\r\n# from json import dump\r\n# part: SearchPart = SearchPart()\r\n# optional_parameters: SearchOptionalParameters = SearchOptionalParameters(\r\n# q='israel palestine conflict',\r\n# maxResults=5,\r\n"} +{"file_name": "9bb62d31-d5ba-4be1-ba64-a9209d7c3bc7.png", "code": "# eventType='live',\r\n# type=['video']\r\n# )\r\n# search = YouTubeRequest(part=part, optional_parameters=optional_parameters)\r\n# results: list[Search] = youtube.search(search).items\r\n# results: dict[str, Any] = [result.model_dump() for result in results]\r\n# with open('live-news.json', 'w', encoding='utf-8') as f:\r\n# dump(results, f, indent=4, default=str)\r\n# print(results)\r\n \r\n# search_save()\r\n# part: Part = CommentThreadPart()\r\n# filter: Filter = CommentThreadFilter(videoId='crYum29M-VE')\r\n# optional: OptionalParameters = CommentThreadOptionalParameters(\r\n# maxResults=10\r\n# )\r\n# req: YouTubeRequest = YouTubeRequest(\r\n# part=part,\r\n# optional_parameters=optional,\r\n# filter=filter\r\n# )\r\n# res: YouTubeResponse = youtube.find_video_comments(request=req)\r\n# print(res)\r\nprint(youtube.list_activities())"} +{"file_name": "f9896f24-bc72-49fc-a684-bef8eaf696ae.png", "code": "from setuptools import find_packages, setup\r\n\r\n# For consistent encoding\r\nfrom codecs import open\r\nfrom os import path\r\n\r\n# The directory containing this file\r\nHERE = path.abspath(path.dirname(__file__))\r\n\r\n# Get the long description from the README file\r\nwith open(path.join(HERE, 'README.md'), encoding='utf-8') as f:\r\n LONG_DESCRIPTION = f.read()\r\n\r\nVERSION = '0.5.1'\r\nDESCRIPTION = 'A python library that wraps around the YouTube V3 API. You can use it find and manage YouTube resources including Videos, Playlists, Channels and Comments.'\r\n\r\nkey_words = [\r\n 'youtube', 'youtube-api', 'youtube comments', 'youtube videos',\r\n 'youtube channels', 'youtube comment thread', 'create youtube playlist'\r\n]\r\n\r\ninstall_requires = [\r\n 'google-api-python-client',\r\n 'google-auth-oauthlib'\r\n]\r\n\r\nsetup(\r\n name='ayv',\r\n packages=find_packages(\r\n include=[\r\n 'youtube',\r\n 'youtube.oauth',\r\n 'youtube.models',\r\n 'youtube.resources',\r\n 'youtube.resources.video',\r\n 'youtube.exceptions',\r\n 'youtube.resources.channel',\r\n 'youtube.resources.playlist',\r\n 'youtube.resources.playlist_item',\r\n 'youtube.resources.comment_thread',\r\n"} +{"file_name": "a60cc5b2-8d92-4810-87c2-9b296fd2e5ba.png", "code": " 'youtube.resources.mixins'\r\n ]\r\n ),\r\n version=VERSION,\r\n description=DESCRIPTION,\r\n long_description_content_type='text/markdown',\r\n long_description=LONG_DESCRIPTION,\r\n url='https://youtube-wrapper.readthedocs.io/en/latest/index.html',\r\n author='Lyle Okoth',\r\n author_email='lyceokoth@gmail.com',\r\n license='MIT',\r\n install_requires=install_requires,\r\n keywords=key_words,\r\n classifiers=[\r\n 'Intended Audience :: Developers',\r\n 'License :: OSI Approved :: MIT License',\r\n 'Programming Language :: Python',\r\n 'Programming Language :: Python :: 3',\r\n 'Programming Language :: Python :: 3.10',\r\n 'Programming Language :: Python :: 3.11',\r\n 'Operating System :: OS Independent'\r\n ],\r\n)\r\n"} +{"file_name": "36af1812-8599-43cf-aa6e-38cdb48406f0.png", "code": "from youtube import YouTube\r\n\r\nclient_secret_file = '/home/downloads/client_secret.json'\r\nyoutube = YouTube(client_secret_file)\r\nyoutube.authenticate()\r\n\r\ndef get_channel_id():\r\n videos = youtube.find_video_by_id('pIzyo4cCGxU')\r\n channel_id = videos[0].channel_id\r\n return channel_id\r\n\r\ndef get_channel_details(channel_id):\r\n channel = youtube.find_channel_by_id(channel_id)\r\n return channel\r\n\r\ndef get_channel_playlists(channel_id):\r\n channel_playlists = youtube.find_channel_playlists(channel_id)\r\n return channel_playlists\r\n\r\ndef get_playlist_items(playlist_id):\r\n search_iterator = youtube.find_playlist_items(playlist_id, max_results=10)\r\n playlists = list(next(search_iterator))\r\n return playlists\r\n\r\ndef get_playlist_item_video_id(playlist_item):\r\n video_id = playlist_item.video_id\r\n return video_id\r\n\r\ndef get_videos(video_ids):\r\n videos = youtube.find_video_by_id(video_ids)\r\n return videos\r\n\r\ndef get_video_comments(video_id):\r\n search_iterator = youtube.find_video_comments(video_id, max_results=20)\r\n video_comments = list(next(search_iterator))\r\n return video_comments\r\n\r\ndef main():\r\n # channel_id = get_channel_id()\r\n # channel = get_channel_details(channel_id)\r\n"} +{"file_name": "8860e71b-1a74-4759-9dc3-cbae9d402d2c.png", "code": " # channel_playlists = get_channel_playlists('UC5WVOSvL9bc6kwCMXXeFLLw')\r\n # playlist_items = get_playlist_items('PLouh1K1d9jkYZo8h1zPH3P1ScAWA8gxbu')\r\n # playlist_video_ids = list(map(get_playlist_item_video_id, playlist_items))\r\n # playlist_videos = get_videos(playlist_video_ids)\r\n video_comments = get_video_comments('pIzyo4cCGxU')\r\n print(video_comments)\r\n\r\nif __name__ == '__main__':\r\n main()"} +{"file_name": "528971dc-2540-4032-be01-0b0a1e6502a8.png", "code": "from youtube import YouTube\r\nimport json\r\nimport os\r\nfrom typing import Optional\r\nfrom youtube.models.video_model import Video\r\n\r\ndef to_json(channels):\r\n with open('channels.json', 'w', encoding='utf-8') as f:\r\n f.write(json.dumps(channels, indent=4))\r\n \r\ndef save_to_channels(video: Video, file_name: Optional[str] = \"kenyan_channels.json\") -> None:\r\n kenyan_channels = []\r\n if video:\r\n if os.path.exists(file_name):\r\n with open(file_name, 'r', encoding='utf-8') as f:\r\n try:\r\n kenyan_channels = json.loads(f.read())\r\n except json.decoder.JSONDecodeError:\r\n pass\r\n with open(file_name, 'w', encoding='utf-8') as f:\r\n data = {\r\n video.channel_title: video.channel_id\r\n }\r\n if not data in kenyan_channels:\r\n kenyan_channels.append(data)\r\n f.write(json.dumps(kenyan_channels, indent=2))\r\n print(kenyan_channels)\r\n \r\n\r\ndef print_videos(videos):\r\n vids = []\r\n for video in videos:\r\n vid = {\r\n 'title': video.video_title,\r\n 'channel': video.channel_title\r\n }\r\n vids.append(vid)\r\n print(vids)\r\n\r\n# client_secrets_file = '/home/lyle/Downloads/python_learning_site.json'\r\n"} +{"file_name": "78e24fb7-85a4-49fc-8fdf-2b55539fa00b.png", "code": "client_secrets_file = '/home/lyle/Downloads/client_secret.json'\r\ncredentials_path = '.'\r\nyoutube = YouTube(client_secrets_file)\r\nyoutube.authenticate(credentials_directory=credentials_path)\r\nsearch_iterator = youtube.search_video('Python for beginners' ,max_results=2)\r\nprint(list(next(search_iterator)))\r\n# print(list(next(search_iterator)))\r\n# print(len(search_iterator.items))\r\n# print_videos(next(search_iterator))\r\n# print_videos(next(search_iterator))\r\n# print_videos(next(search_iterator))\r\n# print(next(search_iterator))\r\n# print_videos(list(next(search_iterator)))\r\n# print_videos(next(search_iterator))\r\n# print_videos(next(search_iterator))\r\n# videos = youtube.find_video_by_id('RFDK1rdJ_gg')\r\n# print(videos)\r\n# save_to_channels(video)\r\n# ids = ['rfscVS0vtbw', 'TFa38ONq5PY']\r\n# youtube.find_videos(ids)\r\n# youtube.find_most_popular_video_by_region('us')\r\n# search_iterator = youtube.search_channel('Python for beginners',max_results=2)\r\n# channel = youtube.find_channel_by_id('UCu8luTDe_Xxd2ahAXsCWX5g')\r\n# print(channel)\r\n# print(channel.to_json())\r\n# to_json([channel.to_dict()])\r\n# search_iterator = youtube.search_channel('Python for beginners',max_results=2)\r\n# print(next(search_iterator))\r\n# channel = youtube.find_channel_by_name('GoogleDevelopers')\r\n# channel = youtube.find_channel_by_name('@PROROBOTS')\r\n# print(channel)\r\n# search_iterator = youtube.find_video_comments('VSB2vjWa1LA', max_results=20)\r\n# print(next(search_iterator))\r\n# print(next(search_iterator))\r\n# search_iterator = youtube.find_all_channel_comments('UCu8luTDe_Xxd2ahAXsCWX5g', max_results=20)\r\n# print(next(search_iterator))\r\n# print(next(search_iterator))\r\n# search_iterator = youtube.search_playlist('Python for beginners',max_results=20)\r\n# print(next(search_iterator))\r\n# print(next(search_iterator))\r\n"} +{"file_name": "f3c1b728-b855-4069-b98e-b1ddd58d4673.png", "code": "# channel_playlists = youtube.find_channel_playlists('UC5WVOSvL9bc6kwCMXXeFLLw')\r\n# print(channel_playlists)\r\n# search_iterator = youtube.find_playlist_items('PLsyeobzWxl7poL9JTVyndKe62ieoN-MZ3', max_results=25)\r\n# print(next(search_iterator))\r\n# print(youtube.search())"} +{"file_name": "a1980283-03bb-4d49-974d-01f12cd14efa.png", "code": "import torch\r\nimport os\r\nfrom torch import nn\r\nfrom torchvision import transforms\r\nimport numpy as np\r\nimport os\r\nfrom PIL import Image\r\nimport torch\r\nimport os\r\nfrom torch import nn\r\nimport torch.nn.functional as F\r\nimport random\r\n\r\n\r\nclass MaizeNet(nn.Module):\r\n def __init__(self, K) -> None:\r\n super(MaizeNet, self).__init__()\r\n\r\n self.conv_layers = nn.Sequential(\r\n # convolution 1\r\n nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1),\r\n nn.ReLU(),\r\n nn.BatchNorm2d(32),\r\n nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, padding=1),\r\n nn.ReLU(),\r\n nn.BatchNorm2d(32),\r\n nn.MaxPool2d(2),\r\n # Convolution 2\r\n nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1),\r\n nn.ReLU(),\r\n nn.BatchNorm2d(64),\r\n nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1),\r\n nn.ReLU(),\r\n nn.BatchNorm2d(64),\r\n nn.MaxPool2d(2),\r\n # Convolution 3\r\n nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1),\r\n nn.ReLU(),\r\n nn.BatchNorm2d(128),\r\n nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1),\r\n"} +{"file_name": "e936284d-151d-4af2-ba4e-456bb55d6d37.png", "code": " nn.ReLU(),\r\n nn.BatchNorm2d(128),\r\n nn.MaxPool2d(2),\r\n # Convolution 4\r\n nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1),\r\n nn.ReLU(),\r\n nn.BatchNorm2d(256),\r\n nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1),\r\n nn.ReLU(),\r\n nn.BatchNorm2d(256),\r\n nn.MaxPool2d(2),\r\n )\r\n\r\n self.dense_layers = nn.Sequential(\r\n # Dropout layer\r\n nn.Dropout(0.5),\r\n # first fully connected layer\r\n nn.Linear(224*224, 1024),\r\n # Relu activation function\r\n nn.ReLU(),\r\n nn.Dropout(0.4),\r\n # Final output layer\r\n nn.Linear(1024, K),\r\n )\r\n\r\n def forward(self, output):\r\n # Convolution Layers\r\n out = self.conv_layers(output)\r\n\r\n # Flatten the layers\r\n out = out.view(-1, 224*224)\r\n\r\n # Fully connected Dense Layers\r\n out = self.dense_layers(out)\r\n\r\n return out\r\n\r\n\r\ndef load_model(model_path: str = os.environ['MODEL_PATH']):\r\n \"\"\"Load the pytorch model.\"\"\"\r\n"} +{"file_name": "9f88b31a-c6b0-40da-bef7-4fdca793c858.png", "code": " n_classes = 4\r\n maizenet = MaizeNet(n_classes)\r\n maizenet.load_state_dict(torch.load(model_path, map_location=torch.device('cpu') ))\r\n return maizenet\r\n\r\ndef preprocess_image(image):\r\n mean = np.array([0.5, 0.5, 0.5])\r\n std = np.array([0.25, 0.25, 0.25])\r\n data_transform = transforms.Compose([\r\n transforms.RandomResizedCrop(224), # resize and crop image to 224 x 224 pixels\r\n transforms.RandomHorizontalFlip(), # flip the images horizontally\r\n transforms.ToTensor(), # convert to pytorch tensor data type\r\n transforms.Normalize(mean, std) # normalize the input image dataset.\r\n ])\r\n transformed_image = data_transform(image).to('cpu')\r\n transformed_image = torch.unsqueeze(transformed_image, 0)\r\n return transformed_image\r\n\r\ndef evaluate_image(image, model):\r\n transformed_image = preprocess_image(image)\r\n labels = ['Maize Leaf Rust', 'Northern Leaf Blight', 'Healthy', 'Gray Leaf Spot']\r\n model.eval()\r\n prediction = F.softmax(model(transformed_image), dim = 1)\r\n data = {\r\n 'Maize Leaf Rust': round(float(prediction[0][0]), 4) * 100,\r\n 'Northern Leaf Blight': round(float(prediction[0][1]) * 100, 4),\r\n 'Healthy': round(float(prediction[0][2]), 4) * 100,\r\n 'Gray Leaf Spot': round(float(prediction[0][3]) * 100, 4)\r\n }\r\n prediction = prediction.argmax()\r\n return labels[prediction], data\r\n"} +{"file_name": "7542a0cd-44ee-46a2-b4ad-c18fb5e9dfca.png", "code": "from google_calendar import GoogleCalendar\r\nimport os\r\n\r\nclient_secret: str = os.environ['CLIENT_SECRET_FILE']\r\ngoogle_calendar: GoogleCalendar = GoogleCalendar(secret_file=client_secret)\r\ngoogle_calendar.authenticate()"} +{"file_name": "26aa8508-591c-4386-9e78-c925829d5830.png", "code": "import torch\r\nimport os\r\nfrom torch import nn\r\nfrom torchvision import transforms\r\nimport numpy as np\r\nimport os\r\nfrom PIL import Image\r\nimport torch\r\nimport os\r\nfrom torch import nn\r\nimport torch.nn.functional as F\r\nimport random\r\n\r\n\r\nclass MaizeNet(nn.Module):\r\n def __init__(self, K) -> None:\r\n super(MaizeNet, self).__init__()\r\n\r\n self.conv_layers = nn.Sequential(\r\n # convolution 1\r\n nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1),\r\n nn.ReLU(),\r\n nn.BatchNorm2d(32),\r\n nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, padding=1),\r\n nn.ReLU(),\r\n nn.BatchNorm2d(32),\r\n nn.MaxPool2d(2),\r\n # Convolution 2\r\n nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1),\r\n nn.ReLU(),\r\n nn.BatchNorm2d(64),\r\n nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1),\r\n nn.ReLU(),\r\n nn.BatchNorm2d(64),\r\n nn.MaxPool2d(2),\r\n # Convolution 3\r\n nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1),\r\n nn.ReLU(),\r\n nn.BatchNorm2d(128),\r\n nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1),\r\n"} +{"file_name": "ba6ce25f-b568-4217-9762-c136c1d5a46e.png", "code": " nn.ReLU(),\r\n nn.BatchNorm2d(128),\r\n nn.MaxPool2d(2),\r\n # Convolution 4\r\n nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1),\r\n nn.ReLU(),\r\n nn.BatchNorm2d(256),\r\n nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1),\r\n nn.ReLU(),\r\n nn.BatchNorm2d(256),\r\n nn.MaxPool2d(2),\r\n )\r\n\r\n self.dense_layers = nn.Sequential(\r\n # Dropout layer\r\n nn.Dropout(0.5),\r\n # first fully connected layer\r\n nn.Linear(224*224, 1024),\r\n # Relu activation function\r\n nn.ReLU(),\r\n nn.Dropout(0.4),\r\n # Final output layer\r\n nn.Linear(1024, K),\r\n )\r\n\r\n def forward(self, output):\r\n # Convolution Layers\r\n out = self.conv_layers(output)\r\n\r\n # Flatten the layers\r\n out = out.view(-1, 224*224)\r\n\r\n # Fully connected Dense Layers\r\n out = self.dense_layers(out)\r\n\r\n return out\r\n\r\n\r\ndef load_model(model_path: str = os.environ['MODEL_PATH']):\r\n \"\"\"Load the pytorch model.\"\"\"\r\n"} +{"file_name": "180dbc8a-7522-4504-9f59-b9461b98243d.png", "code": " n_classes = 4\r\n maizenet = MaizeNet(n_classes)\r\n maizenet.load_state_dict(torch.load(model_path, map_location=torch.device('cpu') ))\r\n return maizenet\r\n\r\ndef preprocess_image(image):\r\n mean = np.array([0.5, 0.5, 0.5])\r\n std = np.array([0.25, 0.25, 0.25])\r\n data_transform = transforms.Compose([\r\n transforms.RandomResizedCrop(224), # resize and crop image to 224 x 224 pixels\r\n transforms.RandomHorizontalFlip(), # flip the images horizontally\r\n transforms.ToTensor(), # convert to pytorch tensor data type\r\n transforms.Normalize(mean, std) # normalize the input image dataset.\r\n ])\r\n transformed_image = data_transform(image).to('cpu')\r\n transformed_image = torch.unsqueeze(transformed_image, 0)\r\n return transformed_image\r\n\r\ndef evaluate_image(image, model):\r\n transformed_image = preprocess_image(image)\r\n labels = ['Maize Leaf Rust', 'Northern Leaf Blight', 'Healthy', 'Gray Leaf Spot']\r\n model.eval()\r\n prediction = F.softmax(model(transformed_image), dim = 1)\r\n data = {\r\n 'Maize Leaf Rust': round(float(prediction[0][0]), 4) * 100,\r\n 'Northern Leaf Blight': round(float(prediction[0][1]) * 100, 4),\r\n 'Healthy': round(float(prediction[0][2]), 4) * 100,\r\n 'Gray Leaf Spot': round(float(prediction[0][3]) * 100, 4)\r\n }\r\n prediction = prediction.argmax()\r\n return labels[prediction], data\r\n"} +{"file_name": "0a91a582-2dcf-43fb-a26c-e04d15c6b176.png", "code": "from youtube import YouTube\r\n\r\nclient_secrets_file = \"/home/lyle/Downloads/search.json\"\r\nyoutube_client = YouTube(client_secret_file=client_secrets_file)\r\nyoutube_client_object = youtube_client.authenticate()\r\nyoutube_client.youtube_client = youtube_client_object\r\n"} +{"file_name": "4b086d38-f514-4ee3-b55e-dc9d32e2a9b3.png", "code": "import os\r\n\r\nfrom langchain.agents import AgentExecutor, Tool\r\nfrom langchain.agents.format_scratchpad import format_to_openai_function_messages\r\nfrom langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser\r\nfrom langchain.chat_models import ChatOpenAI\r\nfrom langchain.embeddings import OpenAIEmbeddings\r\nfrom langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\r\nfrom langchain.schema.document import Document\r\nfrom langchain.tools.render import format_tool_to_openai_function\r\nfrom langchain.vectorstores.faiss import FAISS\r\n\r\nfrom .tools import YouTubeSearchVideoTool\r\nfrom .tools.channel import MyYouTubeChannelDetailsTool, YouTubeChannelDetailsTool\r\nfrom .tools.comment import (\r\n FindMyCommentsTool,\r\n FindUserCommentsTool,\r\n ListVideoCommentRepliesTool,\r\n ListVideoCommentsTool,\r\n ReplyCommentTool,\r\n)\r\nfrom .tools.playlist import (\r\n CreatePlaylistTool,\r\n DeleteYoutubePlaylistsTool,\r\n InsertVideoIntoPlaylistTool,\r\n ListChannelPlaylistsTool,\r\n ListPlaylistVideosTool,\r\n ListUserPlaylistsTool,\r\n)\r\nfrom .tools.video import YouTubeVideoDetailsTool\r\n\r\nOPENAI_API_KEY = os.environ.get(\"OPENAI_API_KEY\")\r\nOPENAI_MODEL = os.environ.get(\"OPENAI_MODEL\") # \"gpt-3.5-turbo-0613\"\r\nllm = ChatOpenAI(temperature=0, model=OPENAI_MODEL, api_key=OPENAI_API_KEY)\r\ntools = [\r\n YouTubeSearchVideoTool(),\r\n ListUserPlaylistsTool(),\r\n ListPlaylistVideosTool(),\r\n ListVideoCommentsTool(),\r\n YouTubeVideoDetailsTool(),\r\n"} +{"file_name": "a311e7aa-0e0f-4ca6-a9fd-1ad3a5600435.png", "code": " YouTubeChannelDetailsTool(),\r\n DeleteYoutubePlaylistsTool(),\r\n InsertVideoIntoPlaylistTool(),\r\n CreatePlaylistTool(),\r\n ListChannelPlaylistsTool(),\r\n MyYouTubeChannelDetailsTool(),\r\n FindUserCommentsTool(),\r\n FindMyCommentsTool(),\r\n ListVideoCommentRepliesTool(),\r\n ReplyCommentTool(),\r\n]\r\n\r\n\r\ndef get_tools(query: str, tools: list[Tool] = tools) -> str:\r\n \"\"\"Get the agent tools.\"\"\"\r\n documents: list[Document] = [\r\n Document(page_content=tool.description, metadata={\"index\": i})\r\n for i, tool in enumerate(tools)\r\n ]\r\n vectore_store = FAISS.from_documents(documents, OpenAIEmbeddings())\r\n retriver = vectore_store.as_retriever()\r\n retrieved = retriver.get_relevant_documents(query)\r\n return [tools[document.metadata[\"index\"]] for document in retrieved]\r\n\r\n\r\ndef get_agent_executor():\r\n \"\"\"Get the agent\"\"\"\r\n prompt = ChatPromptTemplate.from_messages(\r\n [\r\n (\r\n \"system\",\r\n \"You are a funny and friendly youtube assistant. Your task is to help the user with tasks related to youtube..\",\r\n ),\r\n (\"user\", \"{input}\"),\r\n MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\r\n ]\r\n )\r\n\r\n functions = [format_tool_to_openai_function(t) for t in tools]\r\n\r\n"} +{"file_name": "3bceca4a-3470-400d-a852-5131d5642ec4.png", "code": " llm_with_tools = llm.bind(functions=functions)\r\n\r\n agent = (\r\n {\r\n \"input\": lambda x: x[\"input\"],\r\n \"agent_scratchpad\": lambda x: format_to_openai_function_messages(\r\n x[\"intermediate_steps\"]\r\n ),\r\n }\r\n | prompt\r\n | llm_with_tools\r\n | OpenAIFunctionsAgentOutputParser()\r\n )\r\n\r\n agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)\r\n return agent_executor\r\n"} +{"file_name": "5c9c3b9e-5b02-4000-abcf-bc39e858fb8f.png", "code": "from pydantic import BaseModel, Field\r\nfrom schemas import DatasetMetadata\r\nfrom datetime import datetime\r\nfrom json import dump\r\n\r\n\r\nclass ExperimentConfig(BaseModel):\r\n data_dir: str\r\n models_directory: str\r\n features_dir: str\r\n dataset_metadata: DatasetMetadata\r\n label_columns: list[str] = Field(default_factory=list)\r\n feature_cols: list[str] = Field(default_factory=list)\r\n columns_to_drop: list[str] = Field(default_factory=list)\r\n numerical_features: list[str] = Field(default_factory=list)\r\n categorical_features: list[str] = Field(default_factory=list)\r\n \r\n def save_experiment_config(self, path: str,\r\n title: str = '', \r\n description: str = '', \r\n date: datetime=datetime.now()\r\n ) -> None:\r\n with open(path, 'w', encoding='utf-8') as f:\r\n dump(self.model_dump(), f, indent=4)\r\n \r\n \r\n"} +{"file_name": "35ef02ee-a5b0-4462-a1b0-759aa11aed60.png", "code": "from config.config import app_config\r\nfrom os import path\r\nfrom zipfile import ZipFile\r\nimport logging\r\nimport pandas as pd\r\nfrom pandas import DataFrame\r\nfrom sklearn.pipeline import Pipeline\r\nfrom joblib import load\r\nfrom notification import get_gmail_client, create_message, send_message\r\n\r\n\r\ndef extract_dataset(archive_name: str = 'archive.zip', file_name: str = 'Titanic-Dataset.csv') -> None:\r\n \"\"\"Extract the downloaded archive file into the data folder.\"\"\"\r\n # Ubuntu OS\r\n downloads_path: str = path.join(path.expanduser('~'), 'Downloads')\r\n archive_path: str = path.join(downloads_path, archive_name)\r\n try:\r\n with ZipFile(archive_path, 'r') as zip_:\r\n try:\r\n zip_.extract(file_name, app_config.data_dir)\r\n logging.info(f'The file {file_name} has been extracted to {path.join(app_config.data_dir, file_name)}.')\r\n except KeyError:\r\n print(f'There is no file \"{file_name}\" in the archive \"{archive_path}\".')\r\n logging.error(f'There is no file \"{file_name}\" in the archive \"{archive_path}\".')\r\n except FileNotFoundError:\r\n print(f'There is no archive \"{archive_path}\".')\r\n logging.error(f'There is no archive \"{archive_path}\".')\r\n return path.join(app_config.data_dir, file_name)\r\n\r\n\r\ndef load_data(file_path: str = 'Titanic-Dataset.csv') -> DataFrame:\r\n \"\"\"Load the Titanic dataset into a dataframe.\"\"\"\r\n try:\r\n data: DataFrame = pd.read_csv(file_path)\r\n except FileNotFoundError:\r\n print(f'There is no file such file \"{file_path}\".')\r\n logging.error(f'There is no file such file \"{file_path}\".')\r\n return data\r\n\r\n\r\n"} +{"file_name": "85a2f6ec-493f-4181-9c68-6529a84a839f.png", "code": "def load_model(model_path: str) -> Pipeline:\r\n \"\"\"Load a saved model.\"\"\"\r\n try:\r\n model: Pipeline = load(model_path)\r\n except FileNotFoundError:\r\n logging.error(f'There is no such model \"{model_path}\".')\r\n return model\r\n\r\n\r\ndef send_email():\r\n logging.info('Sending email.')\r\n secrets_path = app_config.secret_file\r\n gmail_client = get_gmail_client(secrets_path)\r\n message = create_message()\r\n message = send_message(gmail_client, message)\r\n logging.info('Email Sent.')\r\n logging.info(message)\r\n"} +{"file_name": "a9f185d1-7089-4b93-8d6a-07c351eeda69.png", "code": "from sklearn.compose import ColumnTransformer\r\nfrom sklearn.pipeline import Pipeline\r\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\r\nfrom sklearn.impute import SimpleImputer\r\nfrom sklearn.pipeline import Pipeline\r\nfrom experiment_config import ExperimentConfig\r\n\r\n\r\ndef create_numeric_pipeline() -> Pipeline:\r\n num_pipeline: Pipeline = Pipeline(steps=[\r\n ('imputer', SimpleImputer(strategy='mean')),\r\n ('scaler', StandardScaler())\r\n ])\r\n return num_pipeline\r\n\r\ndef create_categorical_pipeline() -> Pipeline:\r\n cat_pipeline: Pipeline = Pipeline(steps=[\r\n ('imputer', SimpleImputer(strategy='most_frequent')),\r\n ('encoder', OneHotEncoder())\r\n ])\r\n return cat_pipeline\r\n\r\ndef create_experiment_pipeline(experiment_config: ExperimentConfig) -> ColumnTransformer:\r\n preprocessor: ColumnTransformer = ColumnTransformer(\r\n transformers=[\r\n ('drop_columns', 'drop', experiment_config.columns_to_drop),\r\n ('num', create_numeric_pipeline(), experiment_config.numerical_features),\r\n ('cat', create_categorical_pipeline(), experiment_config.categorical_features)\r\n ],\r\n remainder='passthrough'\r\n )\r\n return preprocessor"} +{"file_name": "dc06ac9c-a5ed-468d-9330-798be3e2e347.png", "code": "from sklearn.gaussian_process.kernels import RBF, DotProduct, Matern, RationalQuadratic, WhiteKernel\r\n\r\n\r\nnames = [\r\n \"Nearest Neighbors\",\r\n \"Linear SVM\",\r\n \"RBF SVM\",\r\n \"Gaussian Process\",\r\n \"Decision Tree\",\r\n \"Random Forest\",\r\n \"Neural Net\",\r\n \"AdaBoost\",\r\n \"Naive Bayes\",\r\n \"QDA\",\r\n]\r\nknn = dict(\r\n leaf_size=list(range(1, 15)),\r\n n_neighbors=list(range(1, 10)),\r\n p=[1, 2]\r\n)\r\n\r\ngaussian_process = dict(\r\n kernel=[1*RBF(), 1*DotProduct(), 1*Matern(), 1*RationalQuadratic(), 1*WhiteKernel()]\r\n)\r\n\r\ndecision_tree = dict(\r\n criterion=['gini', 'entropy'],\r\n max_depth=list(range(1, 10)),\r\n min_samples_split=list(range(1, 10)),\r\n min_samples_leaf=list(range(1, 10))\r\n)\r\n\r\nhyperparameters: dict[str, dict] = {\r\n \"Nearest Neighbors\": knn,\r\n \"Gaussian Process\": gaussian_process,\r\n \"Decision Tree\": decision_tree\r\n}"} +{"file_name": "153f6221-8804-4224-8eee-1940fb7171f8.png", "code": "from redis import Redis\r\nfrom config.config import app_config\r\nfrom celery import Celery\r\nfrom utils import extract_dataset\r\nfrom schemas import Model, TrainedModel, TunedModel\r\nimport logging\r\nfrom schemas import Metrics\r\nfrom datetime import datetime\r\nfrom sklearn.metrics import accuracy_score, precision_score, f1_score, recall_score\r\nfrom time import perf_counter\r\nfrom sklearn.pipeline import Pipeline\r\nfrom experiment_param_grids import hyperparameters\r\nfrom sklearn.model_selection import GridSearchCV\r\nfrom sklearn.base import BaseEstimator\r\nfrom schemas.train_config import TrainConfig\r\nfrom os import path\r\nfrom utils import send_email\r\n\r\n\r\nredis: Redis = Redis(host=app_config.redis.redis_host, port=app_config.redis.redis_port, decode_responses=True)\r\ncelery = Celery(__name__)\r\ncelery.conf.broker_url = app_config.celery_broker_url\r\ncelery.conf.result_backend = app_config.celery_result_backend\r\ncelery.conf.event_serializer = 'pickle' # this event_serializer is optional. somehow i missed this when writing this solution and it still worked without.\r\ncelery.conf.task_serializer = 'pickle'\r\ncelery.conf.result_serializer = 'pickle'\r\ncelery.conf.accept_content = ['application/json', 'application/x-python-serialize']\r\n\r\n\r\n@celery.task(name='send_training_report_task')\r\ndef send_training_report_task(training_result):\r\n try:\r\n logging.info('Sending the email')\r\n send_email()\r\n except Exception as e:\r\n logging.error(f'Unable to send email: {str(e)}')\r\n else:\r\n logging.info('Email sent')\r\n return training_result\r\n\r\n"} +{"file_name": "d526273b-4349-4d70-a72e-f8330ce1eeac.png", "code": "\r\n@celery.task(name=\"train_model_task\")\r\ndef train_model_task(train_config: TrainConfig):\r\n train_results: dict = fit_pipeline(train_config=train_config)\r\n trained_model: TrainedModel = TrainedModel(\r\n classifier_name=train_results['model_name'],\r\n train_date=datetime.now(),\r\n save_path=path.join(app_config.models_dir, 'trained', train_results['model_name']),\r\n owner='Lyle Okoth',\r\n metrics=train_results['metrics'],\r\n train_time=train_results['train_time']\r\n )\r\n trained_model.post_model_metrics(app_config, redis)\r\n return {\r\n 'Model Name': train_results['model_name'],\r\n 'Train Time': train_results['train_time'],\r\n 'Metrics': train_results['metrics']\r\n }\r\n \r\n\r\n@celery.task(name='tune_model')\r\ndef tune_model(train_config: TrainConfig):\r\n train_features = train_config.preprocessor.fit_transform(train_config.train_features)\r\n test_features = train_config.preprocessor.fit_transform(train_config.test_features)\r\n clf = GridSearchCV(train_config.model, hyperparameters[train_config.classifier_name], cv=10)\r\n best_model = clf.fit(train_features, train_config.train_labels.values.ravel())\r\n best_params = best_model.best_estimator_.get_params()\r\n preds = best_model.predict(test_features)\r\n accuracy = accuracy_score(preds, train_config.test_labels)\r\n return {\r\n 'params': best_params,\r\n 'acuracy': accuracy,\r\n 'name': train_config.classifier_name\r\n }\r\n \r\ndef fit_pipeline(train_config: TrainConfig, model_params: dict = {}, train: bool = True) -> dict:\r\n logging.info('Training and saving the tuned model')\r\n untrained_model: BaseEstimator = train_config.model\r\n pipeline: Pipeline = Pipeline(steps=[\r\n ('preprocessor', train_config.preprocessor),\r\n"} +{"file_name": "81a755f3-01aa-4a14-85cd-4b1e783a0173.png", "code": " ('classifier', untrained_model)\r\n ])\r\n if model_params:\r\n untrained_model.set_params(**model_params)\r\n train_start_time: float = perf_counter()\r\n pipeline.fit(train_config.train_features, train_config.train_labels.values.ravel())\r\n train_stop_time: float = perf_counter()\r\n predictions: list[int] = pipeline.predict(train_config.test_features).tolist()\r\n accuracy: float = accuracy_score(train_config.test_labels, predictions)\r\n precision: float = precision_score(train_config.test_labels, predictions)\r\n recall: float = recall_score(train_config.test_labels, predictions)\r\n f1: float = f1_score(train_config.test_labels, predictions)\r\n metrics: Metrics = Metrics(\r\n accuracy=round(accuracy,2),\r\n precision=round(precision,2),\r\n recall=round(recall,2),\r\n f1=round(f1,2)\r\n )\r\n model_train_time = train_stop_time - train_start_time\r\n if train:\r\n save_path: str = path.join(app_config.models_dir, 'trained', train_config.classifier_name)\r\n else:\r\n save_path: str = path.join(app_config.models_dir, 'tuned', train_config.classifier_name)\r\n Model.save_model(pipeline, save_path)\r\n return {\r\n 'model_name': train_config.classifier_name,\r\n 'metrics': metrics,\r\n 'train_time': model_train_time\r\n }\r\n \r\n \r\n@celery.task(name='train_tuned_model')\r\ndef train_tuned_model(tuned_model_data: dict, train_config: TrainConfig):\r\n train_results: dict = fit_pipeline(train_config=train_config, model_params=tuned_model_data['params'])\r\n tuned_model: TunedModel = TunedModel(\r\n classifier_name=train_results['model_name'],\r\n train_date=datetime.now(),\r\n save_path=path.join(app_config.models_dir, 'trained', train_results['model_name']),\r\n owner='Lyle Okoth',\r\n metrics=train_results['metrics'],\r\n"} +{"file_name": "a0f795ae-c7b1-4f5f-a723-81b55ebbff36.png", "code": " train_time=train_results['train_time']\r\n )\r\n logging.info('Posting the model metrics.')\r\n tuned_model.post_model_metrics(app_config, redis)\r\n return {\r\n 'Model Name': train_results['model_name'],\r\n 'Train Time': train_results['train_time'],\r\n 'Metrics': train_results['metrics']\r\n }\r\n "} +{"file_name": "6ab6ae57-402c-4407-b640-4786c8aa93c2.png", "code": "# Get the data\r\nfrom utils import extract_dataset, load_data\r\nfrom experiment import Experiment\r\nfrom pandas import DataFrame\r\nfrom schemas import DatasetMetadata\r\nfrom uuid import uuid4\r\nfrom os import path\r\nfrom config.config import app_config\r\nfrom experiment_config import ExperimentConfig\r\nfrom experiment_models import models\r\nfrom experiment_pipelines import create_experiment_pipeline\r\nimport logging\r\n\r\n\r\nlogging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S')\r\n# Unzip and store the downloaded dataset with its metadata\r\narchive_name: str = 'archive.zip'\r\nfile_name: str = 'Titanic-Dataset.csv'\r\ndata_path: str = extract_dataset(archive_name, file_name)\r\ndata_path: str = extract_dataset()\r\ndata: DataFrame = load_data(data_path)\r\ndataset_metadata: DatasetMetadata = DatasetMetadata(\r\n source='https://www.kaggle.com/datasets/yasserh/titanic-dataset',\r\n cols=data.columns.values.tolist(),\r\n description='A dataset that shows the survivors of the titanic tragedy.',\r\n path=data_path,\r\n id=f'Dataset_{str(uuid4())}'\r\n)\r\ndataset_metadata_path: str = path.join(app_config.data_dir, 'titanic_metadata.json')\r\ndataset_metadata.save(dataset_metadata_path)\r\n\r\n# Create an experiment for training various models\r\nlabel_cols: list[str] = ['Survived']\r\nfeature_cols: list[str] = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', \"PassengerId\", \"Name\", \"Ticket\", \"Cabin\"]\r\ncolumns_to_drop: list[str] = [\"PassengerId\", \"Name\", \"Ticket\", \"Cabin\"]\r\nnumerical_features: list[str] = [\"Age\", \"Fare\"]\r\ncategorical_features: list[str] = [\"Pclass\", \"Sex\", \"Embarked\"]\r\nexperiment_config: ExperimentConfig = ExperimentConfig(\r\n data_dir=app_config.data_dir,\r\n models_directory=app_config.models_dir,\r\n"} +{"file_name": "31447b77-4dee-4e04-8d85-8c643059c72c.png", "code": " features_dir=app_config.features_dir,\r\n dataset_metadata=dataset_metadata,\r\n label_columns=label_cols,\r\n feature_cols=feature_cols,\r\n columns_to_drop=columns_to_drop,\r\n numerical_features=numerical_features,\r\n categorical_features=categorical_features\r\n)\r\n\r\nexperiment: Experiment = Experiment(\r\n experiment_config=experiment_config,\r\n preprocessor=create_experiment_pipeline(experiment_config),\r\n models=models\r\n)\r\nexperiment.run()\r\n# experiment.get_results()\r\n# experiment.tune_best_models()\r\n# experiment.get_tuned_models()\r\n# print(experiment.get_best_models(start=-3, end=-1)) "} +{"file_name": "4693a99d-95fb-46ee-9900-7ac583541cf9.png", "code": "from experiment_config import ExperimentConfig\r\nfrom sklearn.compose import ColumnTransformer\r\nfrom sklearn.base import BaseEstimator\r\nfrom schemas import DataSet, Model\r\nfrom pandas import DataFrame, Series\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.pipeline import Pipeline\r\nfrom extensions import train_model_task, send_training_report_task, redis, tune_model, train_tuned_model\r\nfrom celery.result import AsyncResult\r\nimport logging\r\nfrom celery import chord\r\nfrom experiment_models import models\r\nfrom time import sleep\r\nfrom config.config import app_config\r\nfrom schemas.train_config import TrainConfig\r\n\r\n\r\nclass Experiment:\r\n def __init__(self, experiment_config: ExperimentConfig, preprocessor: ColumnTransformer, models: dict[str, BaseEstimator]):\r\n self.experiment_config = experiment_config\r\n self.preprocessor = preprocessor\r\n self.models = models\r\n self.dataset: DataSet = DataSet(metadata=experiment_config.dataset_metadata)\r\n self.trained_models: list[Model] = []\r\n self.train_task_ids: list[str] = []\r\n \r\n def get_features(self) -> DataFrame:\r\n data: DataFrame = self.dataset.get_dataset()\r\n features: DataFrame = data[self.experiment_config.feature_cols]\r\n return features\r\n\r\n def get_labels(self) -> Series:\r\n data: DataFrame = self.dataset.get_dataset()\r\n labels: Series = data[self.experiment_config.label_columns]\r\n return labels\r\n\r\n def get_train_test_data(self) -> ((DataFrame, Series), (DataFrame, Series)):\r\n features = self.get_features()\r\n labels = self.get_labels()\r\n train_features, test_features, train_labels, test_labels = train_test_split(\r\n"} +{"file_name": "0955270f-7189-4483-8fde-cc791f787609.png", "code": " features, labels, test_size=0.2, random_state=42, stratify=labels\r\n )\r\n return (train_features, train_labels), (test_features, test_labels)\r\n\r\n def save_features(self) -> DataFrame:\r\n pass\r\n\r\n def save_labels(self) -> DataFrame:\r\n pass\r\n\r\n def train_model(self, model: Model) -> float:\r\n (train_features, train_labels), (test_features, test_labels) = self.get_train_test_data()\r\n pipeline: Pipeline = Pipeline(steps=[\r\n ('preprocessor', self.preprocessor),\r\n ('classifier', model.model)\r\n ])\r\n logging.info('Queing the model \"%s\" for training.', model.name)\r\n res: AsyncResult = train_model_task.delay(pipeline, train_features, train_labels, test_features, test_labels, model.name, model.save_path)\r\n self.train_task_ids.append(res.id)\r\n return res.id\r\n \r\n\r\n def run(self) -> None: \r\n self._train_results = chord((train_model_task.s(\r\n self.create_train_config(model=model.model, name=model.classifier_name, save_path=model.save_path)\r\n ) for model in self.models), send_training_report_task.s())()\r\n \r\n def get_results(self) -> list[Model]:\r\n \"\"\"Get the training result.\"\"\"\r\n logging.info('Getting the training results')\r\n print(self._train_results.get())\r\n \r\n def get_best_models(self, start: int = 0, end: int = -1) -> Model:\r\n best_models = redis.zrange(name=app_config.accuracy_channel, start=start, end=end, withscores=True)\r\n return best_models\r\n \r\n def tune_best_models(self) -> None:\r\n logging.info('Tuning the best models.')\r\n best_models = self.get_best_models(start=-3, end=-1)\r\n logging.info(best_models)\r\n"} +{"file_name": "a22d2375-b42b-4b37-8a1f-86dc05c2d5e6.png", "code": " self.tuned_model_ids = []\r\n for model_path, _ in best_models:\r\n logging.info(model_path)\r\n model_name: str = model_path.split('/')[-1]\r\n for model in models:\r\n if model_name == model.classifier_name:\r\n train_config: TrainConfig = self.create_train_config(model.model, model.classifier_name, model.save_path)\r\n res = tune_model.apply_async((train_config,), \r\n link=train_tuned_model.s(train_config,)\r\n )\r\n self.tuned_model_ids.append(res.id)\r\n logging.info('%s queued for tuning and retraining.', model_name)\r\n \r\n def get_tuned_models(self):\r\n best_model_names = [name.split('/')[-1] for name, _ in self.get_best_models(start=-3, end=-1)]\r\n while self.tuned_model_ids:\r\n for index, id in enumerate(self.tuned_model_ids):\r\n res: AsyncResult = AsyncResult(id)\r\n if res.ready():\r\n logging.info('Tuned Model result for %s is ready.', res.result['name'])\r\n logging.info(res.result)\r\n self.tuned_model_ids.pop(index)\r\n best_model_names.remove(res.result['name'])\r\n names = ', '.join(best_model_names)\r\n if names:\r\n logging.info('Tuned Model result for %s are not ready.', names)\r\n sleep(3)\r\n \r\n def create_train_config(self, model: BaseEstimator, name: str, save_path: str) -> TrainConfig:\r\n (train_features, train_labels), (test_features, test_labels) = self.get_train_test_data()\r\n train_config: TrainConfig = TrainConfig(\r\n preprocessor=self.preprocessor,\r\n model=model,\r\n classifier_name=name,\r\n save_path=save_path,\r\n train_features=train_features,\r\n train_labels=train_labels,\r\n test_features=test_features,\r\n test_labels=test_labels\r\n )\r\n"} +{"file_name": "d1a1d080-836a-4c03-8d9d-5233ef46d7cc.png", "code": " return train_config"} +{"file_name": "9e59981a-3465-4bc1-a8c2-8414e6a4dc3a.png", "code": "from sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\r\nfrom sklearn.ensemble import AdaBoostClassifier\r\nfrom sklearn.gaussian_process import GaussianProcessClassifier\r\nfrom sklearn.gaussian_process.kernels import RBF\r\nfrom sklearn.naive_bayes import GaussianNB\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn.neural_network import MLPClassifier\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom schemas import Model, UntrainedModel\r\nfrom os import path\r\nfrom config.config import app_config\r\nfrom datetime import datetime\r\n\r\n\r\nnames = [\r\n \"Nearest Neighbors\",\r\n \"Linear SVM\",\r\n \"RBF SVM\",\r\n \"Gaussian Process\",\r\n \"Decision Tree\",\r\n \"Random Forest\",\r\n \"Neural Net\",\r\n \"AdaBoost\",\r\n \"Naive Bayes\",\r\n \"QDA\",\r\n]\r\n\r\nclassifiers = [\r\n KNeighborsClassifier(3),\r\n SVC(kernel=\"linear\", C=0.025, random_state=42),\r\n SVC(gamma=2, C=1, random_state=42),\r\n GaussianProcessClassifier(1.0 * RBF(1.0), random_state=42),\r\n DecisionTreeClassifier(random_state=42),\r\n RandomForestClassifier(\r\n max_depth=5, n_estimators=10, max_features=1, random_state=42\r\n ),\r\n MLPClassifier(alpha=1, max_iter=1000, random_state=42),\r\n AdaBoostClassifier(random_state=42),\r\n"} +{"file_name": "69194085-608a-441c-9e96-aa78c625ec62.png", "code": " GaussianNB(),\r\n QuadraticDiscriminantAnalysis(),\r\n]\r\nmodels: list[Model] = [\r\n UntrainedModel(\r\n save_path=path.join(app_config.models_dir, 'trained', model_name),\r\n model=model,\r\n classifier_name=model_name,\r\n train_date=datetime.now(),\r\n owner='Lyle Okoth'\r\n ) \r\n for model_name, model in zip(names, classifiers)\r\n]"} +{"file_name": "9f39f1d8-e69c-4030-a210-5b91070ac4bf.png", "code": "from api import create_app\r\nfrom api.helpers import generate_data, post_data\r\nimport os\r\n\r\n\r\napp = create_app()\r\n\r\ndef seed_data():\r\n data = generate_data(count=1)\r\n url: str = os.environ.get('url', 'http://127.0.0.1:8000/predict')\r\n post_data(data=data, url=url)\r\n \r\nif __name__ == '__main__':\r\n seed_data()"} +{"file_name": "5f21cb21-1a06-43f9-98db-bf2d70ef075f.png", "code": "import logging\r\nfrom logging import Handler\r\nfrom typing import Optional\r\n\r\n\r\ndef create_logger(handlers: Optional[Handler] = []) -> None:\r\n logging_format: str = \"%(asctime)s - %(levelname)s - %(message)s\"\r\n date_format: str = \"[%X]\"\r\n logging.basicConfig(\r\n format=logging_format,\r\n datefmt=date_format,\r\n handlers=handlers,\r\n level=logging.INFO,\r\n )\r\n"} +{"file_name": "fe9220a6-4eba-4a15-b7ca-d3f86c59526b.png", "code": "from dotenv import load_dotenv\r\n\r\nload_dotenv()\r\nimport logging\r\nfrom logging import Handler\r\n\r\nfrom rich.logging import RichHandler\r\n\r\nfrom .agent_nelly import AgentNelly\r\nfrom .logger import create_logger\r\nfrom .states import Introduction\r\nfrom .ui import RichUI\r\n\r\nif __name__ == \"__main__\":\r\n handlers: list[Handler] = [RichHandler()]\r\n create_logger(handlers=handlers)\r\n logging.getLogger().setLevel(logging.WARN)\r\n from .states import QA, DataAnalysis\r\n\r\n agent_nelly = AgentNelly(ui=RichUI(), initial_state=Introduction())\r\n agent_nelly.analyze_product_review()\r\n"} +{"file_name": "a56fc54b-23f9-4986-8a80-9242da4ccb48.png", "code": "import logging\r\n\r\nfrom .agent_state import AgentState\r\nfrom .states import State\r\nfrom .ui import BaseUI\r\n\r\n\r\nclass AgentNelly:\r\n def __init__(self, ui: BaseUI, initial_state: State) -> None:\r\n self._ui: BaseUI = ui\r\n self._ui.agent = self\r\n self._ui.launch()\r\n self._agent_state = AgentState()\r\n self.transition_to(initial_state)\r\n\r\n @property\r\n def ui(self) -> BaseUI:\r\n return self._ui\r\n\r\n @property\r\n def agent_state(self) -> AgentState:\r\n return self._agent_state\r\n\r\n def transition_to(self, state: State) -> None:\r\n if state:\r\n logging.info(\"Transitioning to the state: %s\", type(state).__name__)\r\n self._state = state\r\n self._state.agent = self\r\n else:\r\n self._state = None\r\n\r\n def analyze_product_review(self) -> None:\r\n while self._state:\r\n try:\r\n self._state.execute()\r\n except KeyboardInterrupt:\r\n from .ui.utils import (agent_confirm_prompt,\r\n agent_question_prompt)\r\n\r\n end_chat: bool = agent_confirm_prompt(\r\n"} +{"file_name": "b2036d79-7cf4-4b34-ae29-0c8cd6043977.png", "code": " question=\"Are you sure you want to end this chat?\"\r\n )\r\n if end_chat:\r\n agent_question_prompt(\"Goodbye\")\r\n break\r\n"} +{"file_name": "cbe4a368-81dc-4083-8847-539d8b4ebab9.png", "code": "from typing import Optional\r\n\r\nfrom pydantic import BaseModel, Field\r\n\r\n\r\nclass AgentState(BaseModel):\r\n product: Optional[str] = Field(\r\n description=\"The product being reviewed\", default=None\r\n )\r\n channel: Optional[str] = Field(description=\"The youtube channel used\", default=None)\r\n video: Optional[str] = Field(description=\"The video being reviewed\", default=None)\r\n transcript: Optional[str] = Field(description=\"The video transcript\", default=None)\r\n comments: Optional[list[str]] = Field(\r\n description=\"The video comments\", default_factory=list\r\n )\r\n summary: Optional[str] = Field(description=\"The video summary\", default=None)\r\n analysis_summary: Optional[str] = Field(\r\n description=\"The video analysis_summary\", default=None\r\n )\r\n features: Optional[str] = Field(\r\n description=\"The product features reviewed\", default=None\r\n )\r\n questions: Optional[list[str]] = Field(\r\n description=\"The questions that the analyst had\", default_factory=list\r\n )\r\n"} +{"file_name": "02d29481-de7a-43d7-a6da-4fd45202d675.png", "code": "from langchain.tools import tool\r\nfrom .helpers import advanced_video_search\r\nfrom youtube.models import Search\r\n\r\n\r\nclass FindProductVideoTools():\r\n @tool\r\n def find_product_video_id(product: str) -> str:\r\n \"\"\"Useful when you need to find a product review video from youtube.\"\"\"\r\n query: str = f'reviews of the latest {product}'\r\n search_results: list[Search] = advanced_video_search(query)\r\n return search_results[0].resource_id\r\n \r\n "} +{"file_name": "074036f6-7414-4f5b-93f0-7743035bc67d.png", "code": "from langchain.tools import tool\r\nfrom .helpers import list_video_comments\r\nfrom youtube.models import Comment\r\n\r\n\r\nclass FindProductReviewTools():\r\n @tool\r\n def find_product_reviews(video_id: str) -> str:\r\n \"\"\"Useful when you need to find a product reviews from youtube video comments.\"\"\"\r\n comments: list[Comment] = list_video_comments(video_id)\r\n comments: list[str] = [comment.snippet.text_display for comment in comments]\r\n return ' '.join(comments)"} +{"file_name": "a95fe4fe-2427-4940-9739-0bc7f7db2614.png", "code": "from .find_product_reviews_tool import FindProductReviewTools\r\nfrom .find_product_tool import FindProductVideoTools"} +{"file_name": "caec01ba-96f8-4d8d-bfdb-f36cc39ccf24.png", "code": "from youtube import YouTube\r\n\r\n\r\nclient_secrets_file = '/home/lyle/Downloads/search.json'\r\nyoutube_client = YouTube(client_secret_file=client_secrets_file)\r\nyoutube_client_object = youtube_client.authenticate()\r\nyoutube_client.youtube_client = youtube_client_object"} +{"file_name": "b6d995d0-833c-4f42-bb88-04fcbc90b510.png", "code": "from typing import Optional\r\nfrom youtube.schemas import (\r\n SearchPart, SearchOptionalParameters, YouTubeResponse, YouTubeRequest\r\n)\r\nfrom youtube.schemas import (\r\n CommentThreadFilter, CommentThreadOptionalParameters, CommentThreadPart\r\n)\r\nfrom youtube.models import Search, Comment\r\nfrom .extensions import youtube_client\r\nfrom collections.abc import Iterator\r\n\r\n\r\ndef advanced_video_search( \r\n query: str,\r\n channel_id: Optional[str] = None,\r\n max_results: Optional[int] = 10,\r\n order: Optional[str] = None,\r\n published_after: Optional[str] = None,\r\n published_before: Optional[str] = None,\r\n region_code: Optional[str] = None,\r\n relevance_language: Optional[str] = 'en',\r\n video_caption: Optional[str] = None,\r\n video_category_id: Optional[str] = None,\r\n video_definition: Optional[str] = None,\r\n video_dimension: Optional[str] = None,\r\n video_duration: Optional[str] = None,\r\n video_paid_product_placement: Optional[str] = None,\r\n video_syndicated: Optional[str] = None,\r\n video_type: Optional[str] = 'any'\r\n ) -> list[Search]:\r\n \"\"\"Search the given channel for the given videos.\"\"\"\r\n search_part: SearchPart = SearchPart()\r\n optional_params: SearchOptionalParameters = SearchOptionalParameters(\r\n channelId=channel_id,\r\n q=query,\r\n maxResults=max_results,\r\n order=order,\r\n publishedAfter=published_after,\r\n publishedBefore=published_before,\r\n regionCode=region_code,\r\n"} +{"file_name": "2d4a6390-95ff-44d1-ab3e-027c94876a47.png", "code": " relevanceLanguage=relevance_language,\r\n type=['video'],\r\n videoCaption=video_caption,\r\n videoCategoryId=video_category_id,\r\n videoDefinition=video_definition,\r\n videoDimension=video_dimension,\r\n videoDuration=video_duration,\r\n videoPaidProductPlacement=video_paid_product_placement,\r\n videoSyndicated=video_syndicated,\r\n videoType=video_type\r\n )\r\n search_schema: YouTubeRequest = YouTubeRequest(\r\n part=search_part, optional_parameters=optional_params\r\n )\r\n response: YouTubeResponse = youtube_client.search(search_schema)\r\n items: list[Search] = response.items\r\n return items\r\n\r\n\r\ndef list_video_comments(video_id: str) -> list[Comment]:\r\n \"\"\"List a given videos comments\"\"\"\r\n part: CommentThreadPart = CommentThreadPart()\r\n filter: CommentThreadFilter = CommentThreadFilter(\r\n videoId=video_id\r\n )\r\n optional: CommentThreadOptionalParameters = CommentThreadOptionalParameters(\r\n maxResults=30\r\n )\r\n request:YouTubeRequest = YouTubeRequest(\r\n part=part,\r\n filter=filter,\r\n optional_parameters=optional\r\n )\r\n comment_iterator: Iterator = youtube_client.get_comments_iterator(request)\r\n video_comments: list[Comment] = list()\r\n done: bool = False\r\n comment_count: int = 0\r\n for comment_threads in comment_iterator:\r\n if done:\r\n break\r\n"} +{"file_name": "c061953d-f897-4283-9157-eb9442c7eba7.png", "code": " for comment_thread in comment_threads:\r\n comment: Comment = comment_thread.snippet.top_level_comment\r\n video_comments.append(comment)\r\n comment_count += 1\r\n if comment_count > 30:\r\n done = True\r\n break\r\n return video_comments"} +{"file_name": "e4f52390-468a-4331-9e52-132f411c6934.png", "code": "from celery import Celery\r\nfrom pydantic_settings import BaseSettings\r\nfrom dotenv import load_dotenv\r\nfrom redis import Redis\r\nfrom json import dumps\r\n\r\nload_dotenv()\r\n\r\n\r\nclass BaseConfig(BaseSettings):\r\n celery_broker_url: str\r\n celery_result_backend: str\r\n redis_host: str\r\n \r\nconfig = BaseConfig()\r\n\r\ncelery = Celery(__name__)\r\ncelery.conf.broker_url = config.celery_broker_url\r\ncelery.conf.result_backend = config.celery_result_backend\r\nredis: Redis = Redis(host=config.redis_host, port=6379, db=0, decode_responses=True)\r\n\r\n@celery.task(name=\"analyze_quote\")\r\ndef analyze_quote(quote: dict) -> dict:\r\n analyzed_quote: dict = quote\r\n analyzed_quote.update({'result': 'Some result'})\r\n redis.publish('analyzed_quotes', dumps(analyzed_quote))\r\n return analyzed_quote\r\n\r\n\r\n@celery.task(name=\"send_email\")\r\ndef send_email(quote: dict) -> dict:\r\n \r\n return analyzed_quote"} +{"file_name": "30772a3c-a3ae-42ec-b556-fd5315a95b3b.png", "code": "from oauth import OAuth\r\nfrom helpers import create_message, send_message\r\n\r\n \r\ngmail_client = get_gmail_client('credentials.json')\r\nmessage = create_message()\r\nmessage = send_message(gmail_client, message)\r\nprint(message)"} +{"file_name": "710ca95c-89f2-4e57-af5d-0239f6a4c69b.png", "code": "\r\nfrom pydantic import BaseModel, Field\r\nfrom typing import Optional\r\nfrom googleapiclient.discovery import build\r\nfrom google.oauth2.credentials import Credentials\r\nfrom google_auth_oauthlib.flow import InstalledAppFlow\r\nfrom google.auth.exceptions import RefreshError\r\nfrom typing import Any\r\nfrom os import path, mkdir\r\nfrom json import dump, load\r\nfrom models import Scopes\r\n\r\n\r\nclass OAuth(BaseModel):\r\n secrets_file: str\r\n scopes: list[str] = [\r\n Scopes.drafts.value, Scopes.labels.value\r\n ]\r\n api_service_name: Optional[str] = 'gmail'\r\n api_version: Optional[str] = 'v1'\r\n credentials_file_name: Optional[str] = 'credentials.json' \r\n credentials_dir: Optional[str] = '.gmail_credentials'\r\n \r\n def credentials_to_dict(self, credentials: Credentials) -> dict:\r\n \"\"\"Convert credentials to a dict.\"\"\"\r\n return {\r\n 'token': credentials.token,\r\n 'refresh_token': credentials.refresh_token,\r\n 'token_uri': credentials.token_uri,\r\n 'client_id': credentials.client_id,\r\n 'client_secret': credentials.client_secret,\r\n 'scopes': credentials.scopes,\r\n }\r\n \r\n def create_default_credentials_path(self) -> str:\r\n \"\"\"Create the default credentials directory.\"\"\"\r\n current_user_home_dir = path.expanduser('~')\r\n if not path.exists(path.join(current_user_home_dir, self.credentials_dir)):\r\n mkdir(path.join(current_user_home_dir, self.credentials_dir))\r\n return path.join(current_user_home_dir, self.credentials_dir)\r\n"} +{"file_name": "dcdc92dc-e797-4be5-b6f9-365d4c308eca.png", "code": " \r\n def get_default_credentials_path(self) -> str:\r\n \"\"\"Generate the default api token file location.\"\"\"\r\n credentials_dir: str = self.create_default_credentials_path()\r\n credentials_file_path = path.join(credentials_dir, self.credentials_file_name)\r\n return credentials_file_path\r\n\r\n def get_credentials(self) -> Credentials:\r\n \"\"\"Get the credentials.\"\"\"\r\n credentials: Credentials = None\r\n credentials_path: str = self.get_default_credentials_path()\r\n try:\r\n with open(credentials_path, 'r', encoding='utf-8') as creds:\r\n credentials = Credentials(**load(creds))\r\n except FileNotFoundError:\r\n pass\r\n return credentials\r\n \r\n def generate_credentials(self) -> Credentials:\r\n flow = InstalledAppFlow.from_client_secrets_file(self.secrets_file, self.scopes)\r\n credentials = flow.run_local_server(port=0)\r\n return credentials\r\n \r\n def save_credentials(self, credentials: Credentials) -> None:\r\n credentials_dict = self.credentials_to_dict(credentials)\r\n credentials_path: str = self.get_default_credentials_path()\r\n with open(credentials_path, 'w', encoding='utf-8') as f:\r\n dump(credentials_dict, f)\r\n \r\n def credentials_expired(self, credentials: Credentials) -> bool:\r\n # youtube_client = self.get_youtube_client(credentials=credentials)\r\n # youtube_find_request = youtube_client.search().list(q='', part='id')\r\n # try:\r\n # youtube_find_request.execute()\r\n # except RefreshError:\r\n # return True\r\n # return False\r\n return False\r\n \r\n def get_gmail_client(self, credentials: Credentials) -> Any:\r\n"} +{"file_name": "4b096e7c-c22c-4b43-ad30-ad9bd695fe85.png", "code": " gmail_client = build(self.api_service_name, self.api_version, credentials=credentials)\r\n return gmail_client\r\n \r\n def authenticate(self) -> Any:\r\n credentials: Credentials = self.get_credentials()\r\n if not credentials or self.credentials_expired(credentials=credentials):\r\n credentials = self.generate_credentials()\r\n self.save_credentials(credentials=credentials)\r\n gmail_client = self.get_gmail_client(credentials=credentials)\r\n return gmail_client"} +{"file_name": "b232ce67-d033-43ac-b4f6-c7341df92145.png", "code": "from email.message import EmailMessage\r\nfrom base64 import urlsafe_b64encode\r\nfrom googleapiclient.errors import HttpError\r\nfrom oauth import OAuth\r\n\r\n\r\ndef create_draft(gmail_client) -> dict:\r\n try:\r\n message = EmailMessage()\r\n message.set_content('This is automated draft mail')\r\n message['To'] = 'lyleokothdev@gmail.com'\r\n message['From'] = 'lyceokoth@gmail.com'\r\n message['Subject'] = 'Automated draft'\r\n # encoded message\r\n encoded_message = urlsafe_b64encode(message.as_bytes()).decode()\r\n create_message = {\r\n 'message': {\r\n 'raw': encoded_message\r\n }\r\n }\r\n draft = gmail_client.users().drafts().create(userId=\"me\",body=create_message).execute()\r\n print(f'Draft id: {draft[\"id\"]}\\nDraft message: {draft[\"message\"]}')\r\n except HttpError as error:\r\n print(f'An error occurred: {error}')\r\n draft = None\r\n\r\n return draft\r\n\r\ndef create_message():\r\n try:\r\n message = EmailMessage()\r\n message.set_content('This is automated draft mail')\r\n message['To'] = 'lyleokothdev@gmail.com'\r\n message['From'] = 'lyceokoth@gmail.com'\r\n message['Subject'] = 'Automated draft'\r\n # encoded message\r\n encoded_message = urlsafe_b64encode(message.as_bytes()).decode()\r\n create_message = {\r\n 'raw': encoded_message\r\n }\r\n"} +{"file_name": "8d905a99-fbc9-41f0-bed0-40d5082736bc.png", "code": " except HttpError as error:\r\n print(F'An error occurred: {error}')\r\n create_message = None\r\n return create_message\r\n\r\ndef send_message(gmail_client, message):\r\n try:\r\n send_message = (gmail_client.users().messages().send\r\n (userId=\"me\", body=message).execute())\r\n print(F'Message Id: {send_message[\"id\"]}')\r\n except HttpError as error:\r\n print(F'An error occurred: {error}')\r\n send_message = None\r\n return send_message\r\n\r\ndef get_gmail_client(secrets_file: str):\r\n oauth: OAuth = OAuth(secrets_file=secrets_file)\r\n gmail_client = oauth.authenticate()\r\n return gmail_client"} +{"file_name": "8bcb3483-d677-4711-8197-933056b54475.png", "code": "from helpers import redis\r\n\r\n\r\ndef analyzed_quotes():\r\n sub = redis.pubsub()\r\n sub.subscribe('analyzed_quotes')\r\n for message in sub.listen():\r\n if message and isinstance(message['data'], str):\r\n quote: dict = message['data']\r\n print(quote)\r\n \r\nif __name__ == '__main__':\r\n while True:\r\n analyzed_quotes()"} +{"file_name": "a83d2bfb-463a-4bb1-ac0f-6b5b3221ad36.png", "code": "# -*- coding: utf-8 -*-\r\n\r\n# Define here the models for your scraped items\r\n#\r\n# See documentation in:\r\n# https://doc.scrapy.org/en/latest/topics/items.html\r\n\r\nfrom scrapy.item import Item, Field\r\nfrom itemloaders.processors import MapCompose, TakeFirst\r\nfrom datetime import datetime\r\n\r\n\r\ndef remove_quotes(text):\r\n # strip the unicode quotes\r\n text = text.strip(u'\\u201c'u'\\u201d')\r\n return text\r\n\r\n\r\ndef convert_date(text):\r\n # convert string March 14, 1879 to Python date\r\n return datetime.strptime(text, '%B %d, %Y')\r\n\r\n\r\ndef parse_location(text):\r\n # parse location \"in Ulm, Germany\"\r\n # this simply remove \"in \", you can further parse city, state, country, etc.\r\n return text[3:]\r\n\r\ndef parse_url(url: str) -> str:\r\n url: str = [url.split('/')[-2]]\r\n try:\r\n int(url)\r\n except:\r\n return '1'\r\n return url\r\n \r\nclass TagItem(Item):\r\n name = Field()\r\n\r\nclass QuoteItemSchema(Item):\r\n"}