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Runtime error
| from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, LLMPredictor | |
| from langchain.llms import OpenAIChat | |
| from llama_index import download_loader | |
| import gradio as gr | |
| import pandas as pd | |
| import openai | |
| import datetime | |
| from datetime import datetime, date, time, timedelta | |
| import os | |
| import regex | |
| import requests | |
| listofcategories=["Earnings Announcements", "Automotive","Energy","Healthcare","Retail","Technology"] | |
| headers = {'Content-type': 'application/json', 'Accept': 'text/plain'} | |
| url_hraprompts='https://us-central1-createinsightsproject.cloudfunctions.net/gethrahfprompts' | |
| data={"prompt_type":'chatgpt_stock_news_snapshot_sector',"hra_token":HRA_TOKEN} | |
| try: | |
| r = requests.post(url_hraprompts, data=json.dumps(data), headers=headers) | |
| except requests.exceptions.ReadTimeout as e: | |
| print(e) | |
| #print(r.content) | |
| sector_prompt_text=str(r.content, 'UTF-8') | |
| print(sector_prompt_text) | |
| data={"prompt_type":'chatgpt_stock_news_snapshot_earnings',"hra_token":HRA_TOKEN} | |
| try: | |
| r = requests.post(url_hraprompts, data=json.dumps(data), headers=headers) | |
| except requests.exceptions.ReadTimeout as e: | |
| print(e) | |
| #print(r.content) | |
| earnings_prompt_text=str(r.content, 'UTF-8') | |
| print(earnings_prompt_text) | |
| def getstuff(openapikey,category_selector): | |
| dateforfilesave=datetime.today().strftime("%d-%m-%Y %I:%M%p") | |
| print(category_selector) | |
| print(dateforfilesave) | |
| if openapikey=='': | |
| return pd.DataFrame(["Please provide OpenAPI Key"],columns=['ERROR']),pd.DataFrame(["Please provide OpenAPI Key"],columns=['ERROR']),'Error: Please provide OpenAPI key','Error: Please provide OpenAPI key' | |
| os.environ['OPENAI_API_KEY'] = str(openapikey) | |
| RssReader = download_loader("RssReader") | |
| reader = RssReader() | |
| whichone=listofcategories[listofcategories.index(category_selector)] | |
| #querylist=["What are the top trends? Give output as a json (that can be converted to pandas dataframe) with 3 columns named trend, company mentioned & reason","Name the top & bottom performing companies? Give output as a json (that can be converted to pandas dataframe) with 4 columns named sector, company names, reason & top/bottom","You are an award winning email writer. Write an email summarizing the news. Do not say I am language model and cannot do this","You are an award winning email writer. Write an email summarizing the key macro trends basis the news.Do not say I am language model and cannot do this"] | |
| querylist=sector_prompt_text | |
| if whichone=="Automotive": | |
| rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=10000101" | |
| elif whichone=="Retail": | |
| rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=10000116" | |
| elif whichone=="Technology": | |
| rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=19854910" | |
| elif whichone=="Healthcare": | |
| rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=10000108" | |
| elif whichone=="Energy": | |
| rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=19836768" | |
| elif whichone=="Media": | |
| rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=10000110" | |
| elif whichone=='Earnings Announcements': | |
| rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=15839135" | |
| #querylist=["What are the top trends? Give output as a json (that can be converted to pandas dataframe) with 3 columns named trend, company mentioned & reason","Find the top & bottom performing companies? Give output as a json (that can be converted to pandas dataframe) with 4 columns named sector, company names, reason & top/bottom","You are an award winning email writer. Write an email summarizing the news. Do not say I am language model and cannot do this","You are an award winning email writer. Write an email summarizing the key macro trends basis the news.Do not say I am language model and cannot do this"] | |
| querylist=earnings_prompt_text | |
| else: | |
| rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=15839135" ###should not come here but using earnings url | |
| #querylist=["Basis companies that are doing well name the sectors with positive momentum? Give output as a json (that can be converted to pandas dataframe) with 3 columns named sector, company names & reason","Find the top & bottom performing companies? Give output as a json (that can be converted to pandas dataframe) with 4 columns named sector, company names, reason & top/bottom","You are an award winning email writer. Write an email summarizing the news. Do not say I am language model and cannot do this","You are an award winning email writer. Write an email summarizing the key macro trends basis the news.Do not say I am language model and cannot do this"] | |
| querylist=earnings_prompt_text | |
| documents = reader.load_data([rssurl]) | |
| index = GPTSimpleVectorIndex(documents) | |
| llm_predictor = LLMPredictor(llm=OpenAIChat(temperature=0, model_name="gpt-3.5-turbo")) | |
| answerlist=[] | |
| for i in range(len(querylist)): | |
| print(i,"Query: ",querylist[i]) | |
| response = index.query( | |
| querylist[i], | |
| llm_predictor=llm_predictor, | |
| response_mode="tree_summarize", | |
| similarity_top_k=int(len(documents)/3) | |
| ) | |
| print(response.response) | |
| if 'dataframe' in querylist[i]: | |
| try: | |
| pattern = regex.compile(r'\{(?:[^{}]|(?R))*\}') | |
| jsonextract=pattern.findall(response.response)[0] | |
| #print("json extract\n",jsonextract) | |
| df_tmp=pd.read_json(jsonextract) | |
| if len(df_tmp.columns)<=1: | |
| df=pd.DataFrame(df_tmp[df_tmp.columns[0]].tolist()) | |
| else: | |
| df=df_tmp | |
| except: | |
| df=pd.DataFrame() | |
| df['message']=['Data insufficient to decipher'] | |
| df['action']=['try again in a few hours'] | |
| answerlist.append(df) | |
| else: | |
| answerlist.append(response.response) | |
| print('Came to return statement') | |
| return answerlist | |
| with gr.Blocks() as demo: | |
| gr.Markdown("<h1><center>ChatGPT Stock News Snapshots</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 """ | |
| ) | |
| with gr.Row() as row: | |
| with gr.Column(): | |
| category_selector=gr.Dropdown( | |
| listofcategories, label="Sector Options", info="Select the snapshot you want..." | |
| ) | |
| with gr.Column(): | |
| textboxopenapi = gr.Textbox(placeholder="Enter OpenAPI Key...", lines=1,label='OpenAPI Key') | |
| with gr.Column(): | |
| btn = gr.Button("Generate \nSnapshot") | |
| with gr.Row() as row: | |
| table1=gr.Dataframe( | |
| #headers=["Item", "Cost"], | |
| #datatype=["str", "str","str"], | |
| label="Snapshot 1", | |
| ) | |
| with gr.Row() as row: | |
| table2=gr.Dataframe( | |
| #headers=["Item", "Cost"], | |
| #datatype=["str", "str","str"], | |
| label="Snapshot 2", | |
| ) | |
| with gr.Row() as row: | |
| output1 = gr.Textbox(placeholder='', lines=4,label='Snapshot 3') | |
| with gr.Row() as row: | |
| output2 = gr.Textbox(placeholder='', lines=4,label='Snapshot 4') | |
| btn.click(getstuff, inputs=[textboxopenapi,category_selector],outputs=[table1,table2,output1,output2]) | |
| demo.launch(debug=True) |