Create app.py

app.py

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+import json 
+import openai
+import os
+
+import pandas as pd
+
+import os
+from collections import deque
+from typing import Dict, List, Optional, Any
+
+from langchain import LLMChain, OpenAI, PromptTemplate
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.llms import BaseLLM
+from langchain.vectorstores.base import VectorStore
+from pydantic import BaseModel, Field
+from langchain.chains.base import Chain
+from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
+from langchain import OpenAI, LLMChain
+from langchain.chat_models import ChatOpenAI
+import datetime
+from datetime import datetime, date, time, timedelta
+from llama_index  import GPTSimpleVectorIndex, SimpleDirectoryReader, LLMPredictor, Document, ServiceContext
+from langchain.llms import OpenAIChat
+
+import feedparser
+import pandas as pd
+import numpy as np
+
+from duckduckgo_search import ddg_videos
+from duckduckgo_search import ddg
+
+def get_learning_curriculum(openapikey,topic):
+    dateforfilesave=datetime.today().strftime("%d-%m-%Y %I:%M%p")
+    print(topic)
+    print(dateforfilesave)
+    if openapikey=='':
+        return pd.DataFrame(["Please provide OpenAPI Key"],columns=['ERROR'])
+    
+    os.environ['OPENAI_API_KEY'] = str(openapikey)
+
+    prompt='You are a training center AI. Give me a detailed curriculum to learn about "{topicforquery}" using search. The curriculum will be a series of learning tasks to be achieved. Give output as a python list of jsons with "task name", "search keyword" to search to complete the task. Donot repeat the taks. For each task name also add a list of "questions" to ask the search results data to select specific articles and complete the curriculum. Remember the search list will be a dataframe of titles & body of the searched article and you may not be able to go through the full article hence these questions should be of types "Which article best suits a learning curriculum?", "Which article is learning oriented?. To reiterate output should be in json with keys task name ex: get beginner training articles for painting, search keyword ex: beginner painting & questions ex: What are top articles for painting?'.format(topicforquery=topic)
+
+    openai.api_key = os.getenv("OPENAI_API_KEY")
+    resp=openai.ChatCompletion.create(
+      model="gpt-3.5-turbo",
+      messages=[
+        {"role": "user", "content": prompt}
+      ]
+    )
+    tasklist=json.loads(resp['choices'][0]['message']['content'])
+
+    llm = ChatOpenAI(temperature=0)
+
+    def research_search(search_keyword,question_to_ask,topic):
+      llm_predictor = LLMPredictor(llm=OpenAIChat(temperature=0, model_name="gpt-3.5-turbo"))
+      service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)
+      keyword=search_keyword
+      keyword="+".join(keyword.lower().split())
+      keyword=keyword.replace(' and ',' AND ')
+      posts = ddg(keyword+' '+topic, safesearch='Off', page=1)
+      latestnews_df=pd.DataFrame(posts)
+      print(latestnews_df.columns)
+      #latestnews_df=latestnews_df.drop_duplicates(subset=['title','link','published'])
+      latestnews_df['text']='Title: '+latestnews_df['title']+' Description: '+latestnews_df['body']
+      print(latestnews_df['text'].tolist())
+      documents=[Document(t) for t in latestnews_df['text'].tolist()]
+      index = GPTSimpleVectorIndex.from_documents(documents)
+      prompt_query=question_to_ask
+      respstr=str(index.query(prompt_query, 
+                    service_context=service_context,
+                    response_mode="tree_summarize",
+                    similarity_top_k=10))
+      print("Search response: ",respstr)
+      return respstr
+    
+    finallist=[]
+    list1=[]
+    list2=[]
+    list3=[]
+    for i in range(len(tasklist)):
+      taskstuff=tasklist[i]
+      search_keyword=taskstuff['search keyword']
+      print('Task Name: '+taskstuff['task name'])
+      finallist.append('Task Name: '+taskstuff['task name'])
+      
+      for question in taskstuff['questions']:
+        response_string=research_search(search_keyword,question,topic)
+        finallist.append("  Question: "+question) 
+        finallist.append("     "+response_string)
+    
+        list1.append(taskstuff['task name'])
+        list2.append(question) 
+        list3.append(response_string)
+    
+    outputdf=pd.DataFrame()
+    outputdf['Task']=list1
+    outputdf['Question']=list2
+    outputdf['Learning']=list3
+    
+    return outputdf
+
+with gr.Blocks() as demo:
+    gr.Markdown("<h1><center>BabyAGI creates Learning Curriculum</center></h1>")
+    gr.Markdown(
+        """What are the sectors with positive momentum? What are the macro trends? Which companies have momentum? Sector summaries and much more. \n\nThis is a demo & showcases ChatGPT integrated with real data. It shows how to get real-time data and marry it with ChatGPT capabilities. This demonstrates 'Chain of Thought' thinking using ChatGPT.\n\n4 snapshots are provided for illustration (trends, sector outlook, news summary email, macro trends email)\n\nNote: llama-index & gpt-3.5-turbo are used. The analysis takes roughly 120 secs & may not always be consistent. If ChatGPT API is overloaded you will get an error\n ![visitors](https://visitor-badge.glitch.me/badge?page_id=hra.chatgpt-stock-news-snapshots)"""
+        )
+    
+    with gr.Row() as row:
+        with gr.Column():
+            textboxtopic = gr.Textbox(placeholder="Enter Topic for Curriculum...", lines=1,label='Topic')
+        with gr.Column():
+            textboxopenapi = gr.Textbox(placeholder="Enter OpenAPI Key...", lines=1,label='OpenAPI Key')
+        
+    with gr.Row() as row:
+        btn = gr.Button("Generate \nCurriculum")
+        
+    with gr.Row() as row:
+        table1=gr.Dataframe(
+            #headers=["Item", "Cost"],
+            #datatype=["str", "str","str"],
+            label="Learning Curriculum",
+        )
+    
+
+    btn.click(getstuff, inputs=[textboxopenapi,textboxtopic],outputs=[table1])
+    
+    
+demo.launch(debug=True)