Update app.py

app.py

@@ -1,19 +1,15 @@
 import streamlit as st
 import requests
-import pymupdf  # PyMuPDF for PDF extraction
+import pymupdf 
 import traceback
 from sentence_transformers import SentenceTransformer
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_groq import ChatGroq
 
-# Load API keys from Streamlit secrets
 ALPHA_VANTAGE_API_KEY = st.secrets["ALPHA_VANTAGE_API_KEY"]
 GROQ_API_KEY = st.secrets["GROQ_API_KEY"]
-
-# Initialize Sentence Transformer for embeddings
 embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
 
-# Initialize LLM
 try:
     llm = ChatGroq(temperature=0, model="llama3-70b-8192", api_key=GROQ_API_KEY)
     st.success("✅ Groq LLM initialized successfully.")
@@ -21,29 +17,24 @@ except Exception as e:
     st.error("❌ Failed to initialize Groq LLM.")
     traceback.print_exc()
 
-# Function to extract and chunk text from PDFs
 def extract_text_from_pdf(uploaded_file, max_length=5000):
     try:
-        doc = pymupdf.open(stream=uploaded_file.read(), filetype="pdf")  # Load PDF
+        doc = pymupdf.open(stream=uploaded_file.read(), filetype="pdf")  
         full_text = "".join(page.get_text() for page in doc)
-
-        # Split text into chunks to avoid LLM token limits
         text_splitter = RecursiveCharacterTextSplitter(chunk_size=max_length, chunk_overlap=200)
         chunks = text_splitter.split_text(full_text)
 
-        return chunks  # Return list of text chunks
+        return chunks 
     except Exception as e:
         st.error("❌ Failed to extract text from PDF.")
         traceback.print_exc()
         return ["Error extracting text."]
 
-# Function to fetch financial data from Alpha Vantage
 def fetch_financial_data(company_ticker):
     if not company_ticker:
         return "No ticker symbol provided. Please enter a valid company ticker."
 
     try:
-        # Fetch Market Cap from Company Overview
         overview_url = f"https://www.alphavantage.co/query?function=OVERVIEW&symbol={company_ticker}&apikey={ALPHA_VANTAGE_API_KEY}"
         overview_response = requests.get(overview_url)
         
@@ -53,8 +44,6 @@ def fetch_financial_data(company_ticker):
         else:
             st.error(f"❌ Failed to fetch company overview. Status Code: {overview_response.status_code}")
             return "Error fetching company overview."
-
-        # Fetch Revenue from Income Statement
         income_url = f"https://www.alphavantage.co/query?function=INCOME_STATEMENT&symbol={company_ticker}&apikey={ALPHA_VANTAGE_API_KEY}"
         income_response = requests.get(income_url)
 
@@ -73,12 +62,11 @@ def fetch_financial_data(company_ticker):
         traceback.print_exc()
         return "Error fetching financial data."
 
-# Function to generate response using Groq's LLM
 def generate_response(user_query, company_ticker, mode, uploaded_file):
     try:
         if mode == "PDF Upload Mode":
             chunks = extract_text_from_pdf(uploaded_file)
-            chunked_summary = "\n\n".join(chunks[:3])  # Use first few chunks
+            chunked_summary = "\n\n".join(chunks[:3]) 
             prompt = f"Summarize the key financial insights from this document:\n\n{chunked_summary}"
         elif mode == "Live Data Mode":
             financial_info = fetch_financial_data(company_ticker)
@@ -93,17 +81,15 @@ def generate_response(user_query, company_ticker, mode, uploaded_file):
         traceback.print_exc()
         return "Error generating response."
 
-# Streamlit UI
 st.title("📊 AI-Powered Financial Insights Chatbot")
 st.write("Upload financial reports or fetch live financial data to get AI-driven insights.")
 
-# User Input Fields
+
 user_query = st.text_input("Enter your query:")
 company_ticker = st.text_input("Enter company ticker symbol (optional):")
 mode = st.radio("Select Mode:", ["PDF Upload Mode", "Live Data Mode"])
 uploaded_file = st.file_uploader("Upload PDF (Only for PDF Mode)", type=["pdf"])
 
-# Button to process request
 if st.button("Get Insights"):
     if mode == "PDF Upload Mode" and not uploaded_file:
         st.error("❌ Please upload a PDF file.")