Spaces:
Runtime error
Runtime error
| import os | |
| import pprint | |
| import codecs | |
| import chardet | |
| import gradio as gr | |
| from langchain.llms import HuggingFacePipeline | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate | |
| from langchain.chains.conversation.memory import ConversationalBufferWindowMemory | |
| from EdgeGPT import Chatbot | |
| db_path = 'data/s-class-manual' | |
| cookie_path = 'data/cookies.json' | |
| embeddings = HuggingFaceEmbeddings() | |
| index = FAISS.load_local(folder_path=db_path, embeddings=embeddings) | |
| bot = Chatbot(cookiePath=cookie_path) | |
| def init_chain(): | |
| template = """ | |
| {history} | |
| Human: {human_input} | |
| Assistant:""" | |
| prompt = PromptTemplate( | |
| input_variables=["history", "human_input"], | |
| template=template | |
| ) | |
| chatgpt_chain = LLMChain( | |
| llm=OpenAI(temperature=0), | |
| prompt=prompt, | |
| verbose=True, | |
| memory=ConversationalBufferWindowMemory(k=2), | |
| ) | |
| human_input = """I want you to act as a voice assistant for a mercedes-benz vehicle. I will provide you with exerts from a vehicle manual. You must use the exerts to answer the user question as best as you can. If you are unsure about the answer, you will truthfully say "not sure".""" | |
| bot_response = chatgpt_chain.predict(human_input=human_input) | |
| print(bot_response) | |
| return chatgpt_chain | |
| def get_prompt(question, index, k=4): | |
| prompt = """I need information from my vehicle manual. I will provide an [EXCERT] from the manual. Use the [EXCERT] and nothing else to answer the [QUESTION]. You must refer to the "[EXCERT]" as "S-Clss Manual" in your response. Here is the [EXCERT]:""" | |
| similar_docs = index.similarity_search(query=question, k=k) | |
| context = [] | |
| for d in similar_docs: | |
| content = d.page_content | |
| context.append(content) | |
| user_input = prompt + '\n[EXCERT]' + '\n' + \ | |
| '\n'.join(context[:k]) + '\n' + '[QUESTION]\n' + question | |
| return user_input | |
| async def ask_question(question, index, backend='bing', k=2, create_bot=False): | |
| global bot | |
| if bot is None or create_bot: | |
| bot = Chatbot(cookiePath=cookie_path) | |
| if backend == 'bing': | |
| prompt = get_prompt(question=question, index=index, k=k) | |
| response = (await bot.ask(prompt=prompt))["item"]["messages"][1]["adaptiveCards"][0]["body"][0]["text"] | |
| elif backend == 'gpt3': | |
| prompt = get_prompt(question=question, index=index, k=k) | |
| response = chatgpt_chain.predict(human_input=prompt) | |
| else: | |
| raise ValueError(f"Invalid backend specified: {backend}") | |
| return response | |
| async def chatbot(question, create_bot=False, k=2): | |
| response = await ask_question(question=question, index=index, backend='bing', k=k, create_bot=create_bot) | |
| return response | |
| def start_ui(): | |
| chatbot_interface = gr.Interface( | |
| fn=chatbot, | |
| inputs=["text", gr.inputs.Checkbox(label="Create bot"), gr.inputs.Slider( | |
| minimum=1, maximum=10, step=1, label="k")], | |
| outputs="text", | |
| title="Owner's Manual", | |
| description="Ask your vehicle manual and get a response.", | |
| examples=[ | |
| ["What are the different features of the dashboard console?", True, 2], | |
| ["What do they do?", False, 3] | |
| ] | |
| ) | |
| chatbot_interface.launch() |