Update app.py

app.py

@@ -2,12 +2,10 @@ import os
 import nltk
 import requests
 import datetime
-# Use a directory within the user's home directory
 nltk_data_dir = os.path.expanduser("~/.nltk_data")
 os.makedirs(nltk_data_dir, exist_ok=True)
 nltk.data.path.append(nltk_data_dir)
 
-# Download NLTK data
 nltk.download('punkt', download_dir=nltk_data_dir, quiet=True)
 import chainlit as cl
 from llama_index.core import VectorStoreIndex, Document
@@ -21,15 +19,12 @@ import pandas as pd
 
 load_dotenv()
 
-# Fetch the API keys from environment variables
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 FMP_API_KEY = os.getenv("FMP_API_KEY")
 
-# Initialize models
 embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
 llm = Groq(model="llama3-70b-8192", api_key=GROQ_API_KEY)
 
-# Create service context
 service_context = ServiceContext.from_defaults(
     llm=llm,
     embed_model=embed_model,
@@ -37,23 +32,12 @@ service_context = ServiceContext.from_defaults(
 )
 
 def fetch_annual_report_10k(symbol: str) -> str:
-    """
-    Fetch the latest annual report on Form 10-K for a specific company.
-
-    Args:
-    - symbol (str): The stock ticker symbol (e.g., 'AAPL').
-
-    Returns:
-    - str: The entire JSON response as a string or an error message.
-    """
     current_year = datetime.datetime.now().year
     url = f"https://financialmodelingprep.com/api/v4/financial-reports-json?symbol={symbol}&year={current_year}&period=FY&apikey={FMP_API_KEY}"
-
     try:
         response = requests.get(url, timeout=10)
         response.raise_for_status()
-        return response.text  # Return the full API response as a string
-
+        return response.text
     except requests.exceptions.HTTPError as http_err:
         return f"HTTP error occurred: {http_err}"
     except requests.exceptions.RequestException as req_err:
@@ -61,7 +45,6 @@ def fetch_annual_report_10k(symbol: str) -> str:
     except Exception as err:
         return f"An unexpected error occurred: {err}"
 
-# Prompts
 summary_prompt = (
     "You are a world-class financial analyst with extensive experience analyzing annual reports. "
     "Provide a comprehensive summary of the 10-K report. Focus on Strategic Insights, Key Financial Figures, and Risk Factors. "
@@ -86,49 +69,44 @@ async def on_chat_start():
         )
     ).send()
 
+    # Check if ticker_response is None or lacks 'content'
+    if ticker_response is None or 'content' not in ticker_response:
+        await cl.Message(content="No ticker symbol provided. Please enter a valid ticker symbol to proceed.").send()
+        return
+
     ticker_symbol = ticker_response['content'].upper()
 
     msg = cl.Message(content=f"Retrieving the latest 10-K report for {ticker_symbol}...")
     await msg.send()
 
     try:
-        # Fetch the 10-K report using the adjusted function
         annual_report_text = fetch_annual_report_10k(ticker_symbol)
 
-        # Check if an error message was returned
         if annual_report_text.startswith("HTTP error") or \
            annual_report_text.startswith("Request error") or \
            annual_report_text.startswith("An unexpected error occurred"):
             await cl.Message(content=annual_report_text).send()
             return
 
-        # Create a Document object with the raw JSON response
         document = Document(text=annual_report_text, metadata={"company": ticker_symbol})
 
-        # Create index
         index = VectorStoreIndex.from_documents(
             [document], service_context=service_context
         )
 
-        # Store the index in the user session
         cl.user_session.set("index", index)
 
-        # Generate summary
         query_engine = index.as_query_engine()
         summary_response = await cl.make_async(query_engine.query)(summary_prompt)
         await cl.Message(content=f"**Summary:**\n{summary_response}").send()
 
-        # Generate questions
         questions_response = await cl.make_async(query_engine.query)(question_prompt)
         questions_format = str(questions_response).split('\n')
         relevant_questions = [question.strip() for question in questions_format if question.strip() and question.strip()[0].isdigit()]
 
-        # Display and answer questions
         await cl.Message(content="Generated strategic questions and answers:").send()
         for question in relevant_questions:
             await cl.Message(content=f"**{question}**").send()
-
-            # Query the engine to get the answer for the question
             answer = await cl.make_async(query_engine.query)(question)
             await cl.Message(content=f"**Answer:**\n{answer}").send()