Update app.py

app.py

@@ -1,34 +1,26 @@
-# Standard library imports
+import gradio as gr
+import groq
 import os
 import tempfile
 import uuid
-import base64
-import io
-import json
-import re
-from datetime import datetime, timedelta
-
-# Third-party imports
-import gradio as gr
-import groq
-import numpy as np
+import yfinance as yf
 import pandas as pd
-import requests
-import fitz  # PyMuPDF
-from PIL import Image
+import plotly.graph_objects as go
 from dotenv import load_dotenv
-
-# LangChain imports
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
 from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+import fitz  # PyMuPDF
+import base64
+from PIL import Image
+import io
+import requests
+import json
 
 # Load environment variables
 load_dotenv()
 client = groq.Client(api_key=os.getenv("GROQ_LEGAL_API_KEY"))
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-SERPER_API_KEY = os.getenv("SERPER_API_KEY")
-BRAVE_API_KEY = os.getenv("BRAVE_API_KEY")
 
 # Directory to store FAISS indexes
 FAISS_INDEX_DIR = "faiss_indexes_finance"
@@ -38,45 +30,37 @@ if not os.path.exists(FAISS_INDEX_DIR):
 # Dictionary to store user-specific vectorstores
 user_vectorstores = {}
 
-# Dictionary to store chart data
-chart_data_store = {}
-
-# Custom CSS for Finance theme with new voice and speech buttons
+# Custom CSS for Finance theme
 custom_css = """
 :root {
-    --primary-color: #0C4160;
-    --secondary-color: #0D6980;
-    --accent-color: #16A6DB;
-    --light-color: #EBF5FA;
+    --primary-color: #FFD700;  /* Gold */
+    --secondary-color: #008000;  /* Dark Green */
+    --light-background: #F0FFF0;  /* Honeydew */
     --dark-text: #333333;
-    --light-text: #F5F5F5;
+    --white: #FFFFFF;
     --border-color: #E5E7EB;
 }
-body { background-color: var(--light-color); font-family: 'IBM Plex Sans', sans-serif; }
+body { background-color: var(--light-background); font-family: 'Inter', sans-serif; }
 .container { max-width: 1200px !important; margin: 0 auto !important; padding: 10px; }
-.header { background-color: var(--primary-color); padding: 20px 0; margin-bottom: 20px; border-radius: 12px 12px 0 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1); }
-.header-title { color: var(--light-text); font-size: 1.8rem; font-weight: 700; text-align: center; }
-.header-subtitle { color: var(--light-text); opacity: 0.8; font-size: 1rem; text-align: center; margin-top: 5px; }
-.chat-container { border-radius: 12px !important; box-shadow: 0 4px 6px rgba(0,0,0,0.1) !important; background-color: #FFFFFF !important; border: 1px solid var(--border-color) !important; min-height: 500px; }
-.message-user { background-color: var(--accent-color) !important; color: var(--light-text) !important; border-radius: 18px 18px 4px 18px !important; padding: 12px 16px !important; margin-left: auto !important; max-width: 80% !important; }
-.message-bot { background-color: #F5F7FA !important; color: var(--dark-text) !important; border-radius: 18px 18px 18px 4px !important; padding: 12px 16px !important; margin-right: auto !important; max-width: 80% !important; }
-.input-area { background-color: #FFFFFF !important; border-top: 1px solid var(--border-color) !important; padding: 12px !important; border-radius: 0 0 12px 12px !important; }
+.header { background-color: var(--white); border-bottom: 2px solid var(--border-color); padding: 15px 0; margin-bottom: 20px; border-radius: 12px 12px 0 0; box-shadow: 0 2px 4px rgba(0,0,0,0.05); }
+.header-title { color: var(--secondary-color); font-size: 1.8rem; font-weight: 700; text-align: center; }
+.header-subtitle { color: var(--dark-text); font-size: 1rem; text-align: center; margin-top: 5px; }
+.chat-container { border-radius: 12px !important; box-shadow: 0 4px 6px rgba(0,0,0,0.1) !important; background-color: var(--white) !important; border: 1px solid var(--border-color) !important; min-height: 500px; }
+.message-user { background-color: var(--primary-color) !important; color: var(--dark-text) !important; border-radius: 18px 18px 4px 18px !important; padding: 12px 16px !important; margin-left: auto !important; max-width: 80% !important; }
+.message-bot { background-color: #F0F0F0 !important; color: var(--dark-text) !important; border-radius: 18px 18px 18px 4px !important; padding: 12px 16px !important; margin-right: auto !important; max-width: 80% !important; }
+.input-area { background-color: var(--white) !important; border-top: 1px solid var(--border-color) !important; padding: 12px !important; border-radius: 0 0 12px 12px !important; }
 .input-box { border: 1px solid var(--border-color) !important; border-radius: 24px !important; padding: 12px 16px !important; box-shadow: 0 2px 4px rgba(0,0,0,0.05) !important; }
-.send-btn { background-color: var(--accent-color) !important; border-radius: 24px !important; color: var(--light-text) !important; padding: 10px 20px !important; font-weight: 500 !important; }
+.send-btn { background-color: var(--secondary-color) !important; border-radius: 24px !important; color: var(--white) !important; padding: 10px 20px !important; font-weight: 500 !important; }
 .clear-btn { background-color: #F0F0F0 !important; border: 1px solid var(--border-color) !important; border-radius: 24px !important; color: var(--dark-text) !important; padding: 8px 16px !important; font-weight: 500 !important; }
-.pdf-viewer-container { border-radius: 12px !important; box-shadow: 0 4px 6px rgba(0,0,0,0.1) !important; background-color: #FFFFFF !important; border: 1px solid var(--border-color) !important; padding: 20px; }
+.pdf-viewer-container { border-radius: 12px !important; box-shadow: 0 4px 6px rgba(0,0,0,0.1) !important; background-color: var(--white) !important; border: 1px solid var(--border-color) !important; padding: 20px; }
 .pdf-viewer-image { max-width: 100%; height: auto; border: 1px solid var(--border-color); border-radius: 12px; box-shadow: 0 2px 4px rgba(0,0,0,0.05); }
-.stats-box { background-color: var(--light-color); padding: 10px; border-radius: 8px; margin-top: 10px; }
-.tool-container { background-color: white; border-radius: 12px; box-shadow: 0 2px 4px rgba(0,0,0,0.05); padding: 15px; margin-bottom: 20px; }
-.tool-title { font-weight: bold; color: var(--primary-color); margin-bottom: 10px; font-size: 1.1rem; }
+.stats-box { background-color: #E6F2E6; padding: 10px; border-radius: 8px; margin-top: 10px; }
+.tool-container { background-color: var(--white); border-radius: 12px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); padding: 15px; margin-bottom: 20px; }
+.tool-title { color: var(--secondary-color); font-size: 1.2rem; font-weight: 600; margin-bottom: 10px; }
 .chart-container { height: 400px; width: 100%; border-radius: 8px; overflow: hidden; }
-.toggle-container { display: flex; align-items: center; margin-bottom: 15px; }
-.toggle-label { margin-right: 10px; font-weight: 500; }
-.search-toggle { margin-left: 5px; }
-.audio-controls { display: flex; align-items: center; margin-top: 10px; }
 """
 
-# Function to process PDF files
+# Function to process PDF files (unchanged)
 def process_pdf(pdf_file):
     if pdf_file is None:
         return None, "No file uploaded", {"page_images": [], "total_pages": 0, "total_words": 0}
@@ -113,196 +97,8 @@ def process_pdf(pdf_file):
             os.unlink(pdf_path)
         return None, f"Error processing PDF: {str(e)}", {"page_images": [], "total_pages": 0, "total_words": 0}
 
-# Serper API functions for enhanced financial data
-def serper_search(query, search_type="search"):
-    """
-    Perform a search using Serper.dev API to get financial information
-    """
-    if not SERPER_API_KEY:
-        return {"error": "Serper API key not configured. Set SERPER_API_KEY in environment variables."}
-    
-    url = "https://google.serper.dev/search"
-    payload = json.dumps({
-        "q": query,
-        "gl": "us",
-        "hl": "en",
-        "autocorrect": True
-    })
-    headers = {
-        'X-API-KEY': SERPER_API_KEY,
-        'Content-Type': 'application/json'
-    }
-    
-    try:
-        response = requests.request("POST", url, headers=headers, data=payload)
-        return response.json()
-    except Exception as e:
-        print(f"Error in Serper search: {e}")
-        return {"error": str(e)}
-
-# Brave Search API functions
-def brave_search(query, search_type="search"):
-    """
-    Perform a search using Brave Search API to get financial information
-    """
-    if not BRAVE_API_KEY:
-        return {"error": "Brave Search API key not configured. Set BRAVE_API_KEY in environment variables."}
-    
-    url = "https://api.search.brave.com/res/v1/web/search"
-    params = {
-        "q": query,
-        "count": 10,
-        "search_lang": "en",
-        "country": "us"
-    }
-    headers = {
-        'Accept': 'application/json',
-        'Accept-Encoding': 'gzip',
-        'X-Subscription-Token': BRAVE_API_KEY
-    }
-    
-    try:
-        response = requests.get(url, params=params, headers=headers)
-        return response.json()
-    except Exception as e:
-        print(f"Error in Brave search: {e}")
-        return {"error": str(e)}
-
-# Add this new function for LLM-based search
-def llm_search(query, model_name="llama3-8b-8192"):
-    """
-    Fallback search using LLM when no search APIs are configured
-    """
-    try:
-        system_prompt = """You are a financial research assistant. Based on your knowledge, 
-        provide relevant information about the query. Format your response as a list of 3-5 
-        relevant pieces of information, each with a title and brief description."""
-        
-        completion = client.chat.completions.create(
-            model=model_name,
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": query}
-            ],
-            temperature=0.3,
-            max_tokens=500
-        )
-        
-        # Format response as search results
-        return [{
-            "title": "LLM-Generated Results",
-            "link": "",
-            "snippet": completion.choices[0].message.content,
-            "source": "AI Knowledge Base"
-        }]
-    except Exception as e:
-        print(f"Error in LLM search: {e}")
-        return []
-
-# Update the get_financial_news function
-def get_financial_news(ticker, use_brave_search=False, model_name="llama3-8b-8192"):
-    """
-    Get latest financial news about a stock using selected search API or LLM fallback
-    """
-    query = f"{ticker} stock news financial analysis latest"
-    news_items = []
-    
-    # Try Brave Search first if selected
-    if use_brave_search and BRAVE_API_KEY:
-        results = brave_search(query)
-        if "web" in results and "results" in results["web"]:
-            for item in results["web"]["results"][:5]:
-                news_items.append({
-                    "title": item.get("title", ""),
-                    "link": item.get("url", ""),
-                    "snippet": item.get("description", ""),
-                    "source": item.get("source", "")
-                })
-            return news_items
-    
-    # Try Serper API if Brave Search is not used or failed
-    if not news_items and SERPER_API_KEY:
-        results = serper_search(query)
-        if "organic" in results:
-            for item in results["organic"][:5]:
-                news_items.append({
-                    "title": item.get("title", ""),
-                    "link": item.get("link", ""),
-                    "snippet": item.get("snippet", ""),
-                    "source": item.get("source", "")
-                })
-            return news_items
-    
-    # Fallback to LLM if no API results
-    if not news_items:
-        return llm_search(f"Provide recent financial news and analysis about {ticker} stock", model_name)
-
-# Update the get_market_sentiment function
-def get_market_sentiment(ticker, use_brave_search=False, model_name="llama3-8b-8192"):
-    """
-    Get market sentiment for a stock using selected search API or LLM fallback
-    """
-    query = f"{ticker} stock market sentiment analysis"
-    snippets = []
-    
-    # Try Brave Search first if selected
-    if use_brave_search and BRAVE_API_KEY:
-        results = brave_search(query)
-        if "web" in results and "results" in results["web"]:
-            for item in results["web"]["results"][:3]:
-                if "description" in item:
-                    snippets.append(item["description"])
-    
-    # Try Serper API if Brave Search is not used or failed
-    if not snippets and SERPER_API_KEY:
-        results = serper_search(query)
-        if "organic" in results:
-            for item in results["organic"][:3]:
-                if "snippet" in item:
-                    snippets.append(item["snippet"])
-    
-    # Generate sentiment analysis
-    if snippets:
-        combined_snippets = "\n".join(snippets)
-    else:
-        # If no API results, use LLM to generate market sentiment directly
-        system_prompt = f"""You are a financial analyst. Based on your knowledge, 
-        provide a brief market sentiment analysis for {ticker} stock. Consider recent 
-        trends, company performance, and market conditions."""
-        
-        try:
-            completion = client.chat.completions.create(
-                model=model_name,
-                messages=[
-                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": f"What is the current market sentiment for {ticker} stock?"}
-                ],
-                temperature=0.2,
-                max_tokens=150
-            )
-            return completion.choices[0].message.content
-        except Exception as e:
-            print(f"Error in LLM sentiment analysis: {e}")
-            return "Unable to determine sentiment"
-    
-    # If we have API snippets, analyze them
-    try:
-        completion = client.chat.completions.create(
-            model=model_name,
-            messages=[
-                {"role": "system", "content": "You are a financial sentiment analyzer. Based on the text provided, determine if the market sentiment for the stock is positive, negative, or neutral. Provide a brief explanation."},
-                {"role": "user", "content": combined_snippets}
-            ],
-            temperature=0.2,
-            max_tokens=150
-        )
-        return completion.choices[0].message.content
-    except Exception as e:
-        print(f"Error analyzing sentiment: {e}")
-        return "Unable to determine sentiment"
-
 # Function to generate chatbot responses with Finance theme
-def generate_response(message, session_id, model_name, history, current_ticker=None, use_brave_search=False):
+def generate_response(message, session_id, model_name, history):
     if not message:
         return history
     try:
@@ -318,98 +114,20 @@ def generate_response(message, session_id, model_name, history, current_ticker=N
             ticker = message[1:].upper()
             try:
                 stock_data = get_stock_data(ticker)
-                news = get_financial_news(ticker, use_brave_search)
-                sentiment = get_market_sentiment(ticker, use_brave_search)
-                
                 response = f"**Stock Information for {ticker}**\n\n"
                 response += f"Current Price: ${stock_data['current_price']}\n"
                 response += f"52-Week High: ${stock_data['52wk_high']}\n"
                 response += f"Market Cap: ${stock_data['market_cap']:,}\n"
-                response += f"P/E Ratio: {stock_data['pe_ratio']}\n\n"
-                response += f"**Market Sentiment:**\n{sentiment}\n\n"
-                response += "**Recent News:**\n"
-                
-                for i, news_item in enumerate(news[:3]):
-                    response += f"{i+1}. [{news_item['title']}]({news_item['link']})\n"
-                    response += f"   {news_item['snippet'][:100]}...\n\n"
-                
+                response += f"P/E Ratio: {stock_data['pe_ratio']}\n"
                 response += f"More data available in the Stock Analysis tab."
                 history.append((message, response))
                 return history
             except Exception as e:
                 history.append((message, f"Error retrieving stock data for {ticker}: {str(e)}"))
                 return history
-        
-        # Check if it's a news search request
-        if message.lower().startswith("/news "):
-            topic = message[6:].strip()
-            news = get_financial_news(topic, use_brave_search)
-            
-            if news:
-                search_provider = "Brave Search" if use_brave_search else "Serper"
-                response = f"**Latest Financial News on {topic} (via {search_provider}):**\n\n"
-                for i, news_item in enumerate(news[:5]):
-                    response += f"{i+1}. **{news_item['title']}**\n"
-                    response += f"   Source: {news_item['source']}\n"
-                    response += f"   {news_item['snippet']}\n"
-                    response += f"   [Read more]({news_item['link']})\n\n"
-            else:
-                response = f"No recent news found for {topic}."
-                
-            history.append((message, response))
-            return history
-        
-        # Check if it's a chart analysis request
-        if message.lower() == "/chart" or message.lower().startswith("/analyze chart"):
-            if current_ticker and current_ticker in chart_data_store:
-                chart_context = generate_chart_context(current_ticker)
-                
-                # Get additional market analysis using selected search API
-                market_context = ""
-                try:
-                    news = get_financial_news(current_ticker, use_brave_search)
-                    sentiment = get_market_sentiment(current_ticker, use_brave_search)
-                    market_context = f"\n\nMarket Sentiment: {sentiment}\n\nRecent News Context:"
-                    for item in news[:2]:
-                        market_context += f"\n- {item['title']}: {item['snippet'][:150]}..."
-                except Exception as e:
-                    print(f"Error getting additional market context: {e}")
-                
-                system_prompt = "You are a financial analyst specializing in stock market analysis. You have been provided with chart and financial data for a stock, along with recent market sentiment and news. Analyze this data and provide insights about the stock's performance trends, potential support/resistance levels, and overall pattern."
-                completion = client.chat.completions.create(
-                    model=model_name,
-                    messages=[
-                        {"role": "system", "content": system_prompt},
-                        {"role": "user", "content": f"Analyze this stock data and chart information:\n\n{chart_context}{market_context}"}
-                    ],
-                    temperature=0.7,
-                    max_tokens=1024
-                )
-                response = completion.choices[0].message.content
-                history.append((message, response))
-                return history
-            else:
-                history.append((message, "Please analyze a stock first using the Stock Analysis tab before requesting chart analysis."))
-                return history
                 
         system_prompt = "You are a financial assistant specializing in analyzing financial reports, statements, and market trends."
         system_prompt += " You can help with stock market information, financial terminology, ratio analysis, and investment concepts."
-        
-        # Add chart context if available
-        if current_ticker and current_ticker in chart_data_store and ("chart" in message.lower() or "stock" in message.lower() or current_ticker.lower() in message.lower()):
-            chart_context = generate_chart_context(current_ticker)
-            context += f"\n\nRecent stock data for {current_ticker}:\n{chart_context}"
-            
-            # Add news and sentiment if it's a stock-related query
-            try:
-                news = get_financial_news(current_ticker, use_brave_search)
-                sentiment = get_market_sentiment(current_ticker, use_brave_search)
-                context += f"\n\nMarket Sentiment: {sentiment}\n\nRecent News Headlines:"
-                for item in news[:2]:
-                    context += f"\n- {item['title']}"
-            except Exception as e:
-                print(f"Error adding news context: {e}")
-        
         if context:
             system_prompt += " Use the following context to answer the question if relevant: " + context
         
@@ -429,77 +147,6 @@ def generate_response(message, session_id, model_name, history, current_ticker=N
         history.append((message, f"Error generating response: {str(e)}"))
         return history
 
-# Helper function to generate chart context for LLM
-def generate_chart_context(ticker):
-    data = chart_data_store[ticker]
-    df = data["history"]
-    stats = data["stats"]
-    
-    # Calculate key metrics from the chart data
-    start_price = df["Close"].iloc[0]
-    end_price = df["Close"].iloc[-1]
-    percent_change = ((end_price - start_price) / start_price) * 100
-    highest = df["High"].max()
-    lowest = df["Low"].min()
-    
-    # Calculate average volume
-    avg_volume = df["Volume"].mean()
-    
-    # Calculate simple moving averages
-    if len(df) > 50:
-        sma_50 = df["Close"].rolling(window=50).mean().iloc[-1]
-    else:
-        sma_50 = "Not enough data"
-    
-    if len(df) > 200:
-        sma_200 = df["Close"].rolling(window=200).mean().iloc[-1]
-    else:
-        sma_200 = "Not enough data"
-    
-    # Calculate RSI (Relative Strength Index)
-    delta = df['Close'].diff()
-    gain = delta.where(delta > 0, 0).rolling(window=14).mean()
-    loss = -delta.where(delta < 0, 0).rolling(window=14).mean()
-    rs = gain / loss
-    rsi = 100 - (100 / (1 + rs.iloc[-1])) if not pd.isna(rs.iloc[-1]) and loss.iloc[-1] != 0 else 50
-    
-    # Calculate volatility (standard deviation of returns)
-    returns = df['Close'].pct_change()
-    volatility = returns.std() * 100  # Annualize by multiplying by sqrt(252)
-    
-    # Get recent price movement (last 5 days)
-    recent_prices = []
-    if len(df) >= 5:
-        for i in range(1, 6):
-            if i <= len(df):
-                recent_prices.append(df["Close"].iloc[-i])
-    
-    # Format the context for the LLM
-    context = f"""
-Ticker: {ticker}
-Period: {data["period"]}
-Current Price: ${end_price:.2f}
-Price Change: {percent_change:.2f}%
-52-Week High: ${stats['52wk_high']}
-52-Week Low: ${lowest:.2f}
-Market Cap: ${stats['market_cap']:,}
-P/E Ratio: {stats['pe_ratio']}
-Average Volume: {avg_volume:.0f}
-Volatility: {volatility:.2f}%
-RSI (14-day): {rsi:.2f}
-"""
-
-    if isinstance(sma_50, float):
-        context += f"50-day Moving Average: ${sma_50:.2f}\n"
-    if isinstance(sma_200, float):
-        context += f"200-day Moving Average: ${sma_200:.2f}\n"
-        
-    context += "\nRecent Price Movement (last 5 days, most recent first):\n"
-    for i, price in enumerate(recent_prices):
-        context += f"Day {i+1}: ${price:.2f}\n"
-    
-    return context
-
 # Functions to update PDF viewer (unchanged)
 def update_pdf_viewer(pdf_state):
     if not pdf_state["total_pages"]:
@@ -635,7 +282,7 @@ def create_stock_chart(ticker, period="1y"):
         print(f"Error creating stock chart: {e}")
         return None
 
-def analyze_ticker(ticker_input, period, use_brave_search=False):
+def analyze_ticker(ticker_input, period):
     """Process the ticker input and return analysis"""
     if not ticker_input:
         return None, "Please enter a valid ticker symbol", None
@@ -646,29 +293,11 @@ def analyze_ticker(ticker_input, period, use_brave_search=False):
     
     try:
         stock_data = get_stock_data(ticker)
-        stock_history = get_stock_history(ticker, period)
         chart = create_stock_chart(ticker, period)
         
-        # Store chart data for LLM analysis
-        chart_data_store[ticker] = {
-            "history": stock_history,
-            "stats": stock_data,
-            "period": period
-        }
-        
-        # Get market sentiment using selected search API or LLM fallback
-        try:
-            sentiment = get_market_sentiment(ticker, use_brave_search)
-            sentiment_summary = f"\n\n**Market Sentiment:**\n{sentiment}"
-        except Exception as e:
-            print(f"Error getting sentiment: {e}")
-            sentiment_summary = ""
-        
         # Create a formatted summary
-        search_provider = "Brave Search" if (use_brave_search and BRAVE_API_KEY) else "Serper" if SERPER_API_KEY else "AI Knowledge Base"
         summary = f"""
-### {ticker} Analysis (Using {search_provider})
-
+### {ticker} Analysis
 **Current Price:** ${stock_data['current_price']}  
 **52-Week High:** ${stock_data['52wk_high']}  
 **Market Cap:** ${stock_data['market_cap']:,}  
@@ -676,203 +305,111 @@ def analyze_ticker(ticker_input, period, use_brave_search=False):
 **Dividend Yield:** {stock_data['dividend_yield'] * 100 if stock_data['dividend_yield'] != 'N/A' else 'N/A'}%  
 **Beta:** {stock_data['beta']}  
 **Avg Volume:** {stock_data['average_volume']:,}  
-{sentiment_summary}
-
-For in-depth analysis of this chart, ask the chatbot by typing "/chart" or "/analyze chart".
-For latest news, type "/news {ticker}".
         """
         
         return chart, summary, ticker
     except Exception as e:
         return None, f"Error analyzing ticker {ticker}: {str(e)}", None
 
-# Replace the load_docling_model function with a simpler image analysis function
-def analyze_image(image_file):
-    """
-    Basic image analysis function that doesn't rely on external models
-    """
-    if image_file is None:
-        return "No image uploaded. Please upload an image to analyze."
+# Gradio interface
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
+    current_session_id = gr.State(None)
+    pdf_state = gr.State({"page_images": [], "total_pages": 0, "total_words": 0})
+    current_ticker = gr.State(None)
     
-    try:
-        image = Image.open(image_file)
-        width, height = image.size
-        format = image.format
-        mode = image.mode
-        
-        analysis = f"""## Technical Document Analysis
-
-**Image Properties:**
-- Dimensions: {width}x{height} pixels
-- Format: {format}
-- Color Mode: {mode}
-
-**Technical Analysis:**
-1. Document Quality:
-   - Resolution: {'High' if width > 2000 or height > 2000 else 'Medium' if width > 1000 or height > 1000 else 'Low'}
-   - Color Depth: {mode}
-
-2. Recommendations:
-   - For text extraction, consider using PDF format
-   - For technical diagrams, ensure high resolution
-   - Consider OCR for text content
-   
-**Note:** For detailed technical analysis, please convert to PDF format
-"""
-        return analysis
-    except Exception as e:
-        return f"Error analyzing image: {str(e)}\n\nPlease try using PDF format instead."
-
-# Update the Gradio interface
-def create_interface():
-    with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
-        current_session_id = gr.State(None)
-        pdf_state = gr.State({"page_images": [], "total_pages": 0, "total_words": 0})
-        current_ticker = gr.State(None)
-        
-        gr.HTML("""
-        <div class="header">
-            <div class="header-title">Fin-Vision</div>
-            <div class="header-subtitle">Analyze financial documents with Groq's LLM API.</div>
-        </div>
-        """)
-        
-        with gr.Row(elem_classes="container"):
-            with gr.Column(scale=1, min_width=300):
-                pdf_file = gr.File(label="Upload PDF Document", file_types=[".pdf"], type="binary")
-                upload_button = gr.Button("Process PDF", variant="primary")
-                pdf_status = gr.Markdown("No PDF uploaded yet")
-                
-                # Search Engine Toggle
-                with gr.Row(elem_classes="toggle-container"):
-                    gr.Markdown("Search Provider:", elem_classes="toggle-label")
-                    use_brave_search = gr.Checkbox(
-                        label="Use Brave Search (unchecked = Serper)", 
-                        value=False, 
-                        elem_classes="search-toggle"
-                    )
-                    
-                model_dropdown = gr.Dropdown(
-                    choices=["llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768", "gemma-7b-it"],
-                    value="llama3-70b-8192",
-                    label="Select Groq Model"
-                )
-                
-                # Finance Tools Section
-                gr.Markdown("### Financial Tools", elem_classes="tool-title")
-                with gr.Group(elem_classes="tool-container"):
-                    with gr.Tabs():
-                        with gr.TabItem("Stock Analysis"):
-                            ticker_input = gr.Textbox(label="Enter Ticker Symbol (e.g., AAPL)", placeholder="AAPL")
-                            period_dropdown = gr.Dropdown(
-                                choices=["1mo", "3mo", "6mo", "1y", "2y", "5y", "max"],
-                                value="1y",
-                                label="Time Period"
-                            )
-                            analyze_button = gr.Button("Analyze Stock")
-                        
-                        with gr.TabItem("Image Analysis"):
-                            gr.Markdown("""
-                            ### Basic Image Analysis
-                            Upload an image to see basic properties and recommendations.
-                            For detailed document analysis, please use PDF format.
-                            """)
-                            image_input = gr.File(
-                                label="Upload Document Image", 
-                                file_types=["image"],
-                                type="filepath"
-                            )
-                            analyze_btn = gr.Button("Analyze Image")
+    gr.HTML("""
+    <div class="header">
+        <div class="header-title">Fin-Vision</div>
+        <div class="header-subtitle">Analyze financial documents with Groq's LLM API.</div>
+    </div>
+    """)
+    
+    with gr.Row(elem_classes="container"):
+        with gr.Column(scale=1, min_width=300):
+            pdf_file = gr.File(label="Upload PDF Document", file_types=[".pdf"], type="binary")
+            upload_button = gr.Button("Process PDF", variant="primary")
+            pdf_status = gr.Markdown("No PDF uploaded yet")
+            model_dropdown = gr.Dropdown(
+                choices=["llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768", "gemma-7b-it"],
+                value="llama3-70b-8192",
+                label="Select Groq Model"
+            )
             
-            with gr.Column(scale=2, min_width=600):
+            # Finance Tools Section
+            gr.Markdown("### Financial Tools", elem_classes="tool-title")
+            with gr.Group(elem_classes="tool-container"):
                 with gr.Tabs():
-                    with gr.TabItem("PDF Viewer"):
-                        with gr.Column(elem_classes="pdf-viewer-container"):
-                            page_slider = gr.Slider(minimum=1, maximum=1, step=1, label="Page Number", value=1)
-                            pdf_image = gr.Image(label="PDF Page", type="pil", elem_classes="pdf-viewer-image")
-                            stats_display = gr.Markdown("No PDF uploaded yet", elem_classes="stats-box")
-                    
                     with gr.TabItem("Stock Analysis"):
-                        with gr.Column(elem_classes="pdf-viewer-container"):
-                            stock_chart = gr.Plot(label="Stock Price Chart", elem_classes="chart-container")
-                            stock_summary = gr.Markdown("Enter a ticker symbol to see analysis")
-                    
-                    with gr.TabItem("Image Analysis Results"):
-                        image_analysis_results = gr.Markdown("Upload an image and click 'Analyze Image' to see analysis results")
-                        image_preview = gr.Image(label="Image Preview", type="pil")
-        
-        with gr.Row(elem_classes="container"):
-            with gr.Column(scale=2, min_width=600):
-                chatbot = gr.Chatbot(height=500, bubble_full_width=False, show_copy_button=True, elem_classes="chat-container")
-                with gr.Row():
-                    msg = gr.Textbox(
-                        show_label=False, 
-                        placeholder="Ask about your financial document or click the microphone icon to speak...", 
-                        scale=5
-                    )
-                    send_btn = gr.Button("Send", scale=1)
+                        ticker_input = gr.Textbox(label="Enter Ticker Symbol (e.g., AAPL)", placeholder="AAPL")
+                        period_dropdown = gr.Dropdown(
+                            choices=["1mo", "3mo", "6mo", "1y", "2y", "5y", "max"],
+                            value="1y",
+                            label="Time Period"
+                        )
+                        analyze_button = gr.Button("Analyze Stock")
             
-        # Event Handlers
-        upload_button.click(
-            process_pdf,
-            inputs=[pdf_file],
-            outputs=[current_session_id, pdf_status, pdf_state]
-        ).then(
-            update_pdf_viewer,
-            inputs=[pdf_state],
-            outputs=[page_slider, pdf_image, stats_display]
-        )
-        
-        msg.submit(
-            generate_response,
-            inputs=[msg, current_session_id, model_dropdown, chatbot, current_ticker, use_brave_search],
-            outputs=[chatbot]
-        ).then(lambda: "", None, [msg])
-        
-        send_btn.click(
-            generate_response,
-            inputs=[msg, current_session_id, model_dropdown, chatbot, current_ticker, use_brave_search],
-            outputs=[chatbot]
-        ).then(lambda: "", None, [msg])
-        
-        # Update display when search provider changes
-        use_brave_search.change(
-            lambda x: f"Using {'Brave Search' if x else 'Serper'} API for queries",
-            inputs=[use_brave_search],
-            outputs=[pdf_status]
-        )
-        
-        page_slider.change(
-            update_image,
-            inputs=[page_slider, pdf_state],
-            outputs=[pdf_image]
-        )
-        
-        # Stock analysis handler
-        analyze_button.click(
-            analyze_ticker,
-            inputs=[ticker_input, period_dropdown, use_brave_search],
-            outputs=[stock_chart, stock_summary, current_ticker]
-        )
-        
-        # Image analysis button handler
-        analyze_btn.click(
-            analyze_image,
-            inputs=[image_input],
-            outputs=[image_analysis_results]
-        ).then(
-            lambda x: Image.open(x) if x else None,
-            inputs=[image_input],
-            outputs=[image_preview]
-        )
-
-        clear_btn.click(
-            lambda: ([], None, "No PDF uploaded yet", {"page_images": [], "total_pages": 0, "total_words": 0}, 0, None, "No PDF uploaded yet"),
-            None,
-            [chatbot, current_session_id, pdf_status, pdf_state, page_slider, pdf_image, stats_display]
-        )
-
-    return demo
+        with gr.Column(scale=2, min_width=600):
+            with gr.Tabs():
+                with gr.TabItem("PDF Viewer"):
+                    with gr.Column(elem_classes="pdf-viewer-container"):
+                        page_slider = gr.Slider(minimum=1, maximum=1, step=1, label="Page Number", value=1)
+                        pdf_image = gr.Image(label="PDF Page", type="pil", elem_classes="pdf-viewer-image")
+                        stats_display = gr.Markdown("No PDF uploaded yet", elem_classes="stats-box")
+                
+                with gr.TabItem("Stock Analysis"):
+                    with gr.Column(elem_classes="pdf-viewer-container"):
+                        stock_chart = gr.Plot(label="Stock Price Chart", elem_classes="chart-container")
+                        stock_summary = gr.Markdown("Enter a ticker symbol to see analysis")
+    
+    with gr.Row(elem_classes="container"):
+        with gr.Column(scale=2, min_width=600):
+            chatbot = gr.Chatbot(height=500, bubble_full_width=False, show_copy_button=True, elem_classes="chat-container")
+            with gr.Row():
+                msg = gr.Textbox(show_label=False, placeholder="Ask about your financial document or type $TICKER for stock info...", scale=5)
+                send_btn = gr.Button("Send", scale=1)
+            clear_btn = gr.Button("Clear Conversation")
+    
+    # Event Handlers
+    upload_button.click(
+        process_pdf,
+        inputs=[pdf_file],
+        outputs=[current_session_id, pdf_status, pdf_state]
+    ).then(
+        update_pdf_viewer,
+        inputs=[pdf_state],
+        outputs=[page_slider, pdf_image, stats_display]
+    )
+    
+    msg.submit(
+        generate_response,
+        inputs=[msg, current_session_id, model_dropdown, chatbot],
+        outputs=[chatbot]
+    ).then(lambda: "", None, [msg])
+    
+    send_btn.click(
+        generate_response,
+        inputs=[msg, current_session_id, model_dropdown, chatbot],
+        outputs=[chatbot]
+    ).then(lambda: "", None, [msg])
+    
+    clear_btn.click(
+        lambda: ([], None, "No PDF uploaded yet", {"page_images": [], "total_pages": 0, "total_words": 0}, 0, None, "No PDF uploaded yet", None),
+        None,
+        [chatbot, current_session_id, pdf_status, pdf_state, page_slider, pdf_image, stats_display, current_ticker]
+    )
+    
+    page_slider.change(
+        update_image,
+        inputs=[page_slider, pdf_state],
+        outputs=[pdf_image]
+    )
+    
+    # Stock analysis handler
+    analyze_button.click(
+        analyze_ticker,
+        inputs=[ticker_input, period_dropdown],
+        outputs=[stock_chart, stock_summary, current_ticker]
+    )
 
 # Add footer with attribution
 gr.HTML("""
@@ -883,5 +420,4 @@ gr.HTML("""
 
 # Launch the app
 if __name__ == "__main__":
-    demo = create_interface()
     demo.launch()