Update app.py

app.py

@@ -1,26 +1,52 @@
-import gradio as gr
-import groq
+# Standard library imports
 import os
 import tempfile
 import uuid
-import yfinance as yf
-import pandas as pd
-import plotly.graph_objects as go
-from dotenv import load_dotenv
-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
+import re
+from datetime import datetime, timedelta
+
+# Third-party imports
+import gradio as gr
+import groq
+import numpy as np
+import pandas as pd
+import requests
+import fitz  # PyMuPDF
+from PIL import Image
+from dotenv import load_dotenv
+import torch
+
+# LangChain imports
+from langchain_community.embeddings import HuggingFaceInstructEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
 
 # 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")
+
+# Embeddings initialization with fallback
+try:
+    embeddings = HuggingFaceInstructEmbeddings(
+        model_name="hkunlp/instructor-base",
+        model_kwargs={"device": "cuda" if torch.cuda.is_available() else "cpu"}
+    )
+except Exception as e:
+    print(f"Warning: Failed to load primary embeddings model: {e}")
+    try:
+        embeddings = HuggingFaceInstructEmbeddings(
+            model_name="all-MiniLM-L6-v2",
+            model_kwargs={"device": "cuda" if torch.cuda.is_available() else "cpu"}
+        )
+    except Exception as e:
+        print(f"Warning: Failed to load fallback embeddings model: {e}")
+        embeddings = None
+
+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"
@@ -30,37 +56,44 @@ if not os.path.exists(FAISS_INDEX_DIR):
 # Dictionary to store user-specific vectorstores
 user_vectorstores = {}
 
-# Custom CSS for Finance theme
+# Dictionary to store chart data
+chart_data_store = {}
+
+# Custom CSS for Finance theme with new voice and speech buttons
 custom_css = """
 :root {
-    --primary-color: #FFD700;  /* Gold */
-    --secondary-color: #008000;  /* Dark Green */
-    --light-background: #F0FFF0;  /* Honeydew */
+    --primary-color: #0C4160;
+    --secondary-color: #0D6980;
+    --accent-color: #16A6DB;
+    --light-color: #EBF5FA;
     --dark-text: #333333;
-    --white: #FFFFFF;
+    --light-text: #F5F5F5;
     --border-color: #E5E7EB;
 }
-body { background-color: var(--light-background); font-family: 'Inter', sans-serif; }
+body { background-color: var(--light-color); font-family: 'IBM Plex Sans', sans-serif; }
 .container { max-width: 1200px !important; margin: 0 auto !important; padding: 10px; }
-.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; }
+.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; }
 .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(--secondary-color) !important; border-radius: 24px !important; color: var(--white) !important; padding: 10px 20px !important; font-weight: 500 !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; }
 .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: var(--white) !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: #FFFFFF !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: #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; }
+.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; }
 .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; }
 """
 
-# Function to process PDF files (unchanged)
+# Function to process PDF files
 def process_pdf(pdf_file):
     if pdf_file is None:
         return None, "No file uploaded", {"page_images": [], "total_pages": 0, "total_words": 0}
@@ -97,8 +130,196 @@ 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):
+def generate_response(message, session_id, model_name, history, current_ticker=None, use_brave_search=False):
     if not message:
         return history
     try:
@@ -114,20 +335,98 @@ def generate_response(message, session_id, model_name, history):
             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"
+                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"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
         
@@ -147,6 +446,77 @@ def generate_response(message, session_id, model_name, history):
         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"]:
@@ -282,7 +652,7 @@ def create_stock_chart(ticker, period="1y"):
         print(f"Error creating stock chart: {e}")
         return None
 
-def analyze_ticker(ticker_input, period):
+def analyze_ticker(ticker_input, period, use_brave_search=False):
     """Process the ticker input and return analysis"""
     if not ticker_input:
         return None, "Please enter a valid ticker symbol", None
@@ -293,11 +663,29 @@ def analyze_ticker(ticker_input, period):
     
     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
+### {ticker} Analysis (Using {search_provider})
+
 **Current Price:** ${stock_data['current_price']}  
 **52-Week High:** ${stock_data['52wk_high']}  
 **Market Cap:** ${stock_data['market_cap']:,}  
@@ -305,13 +693,54 @@ def analyze_ticker(ticker_input, period):
 **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
 
-# Gradio interface
+# 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."
+    
+    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
 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})
@@ -319,97 +748,114 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
     
     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 class="header-title">Fin-Vision AI</div>
+        <div class="header-subtitle">Advanced Financial Analysis Assistant</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"
+    # Main container with all functionality in tabs
+    with gr.Tabs() as main_tabs:
+        # Chat Assistant Tab
+        with gr.TabItem("💬 Chat Assistant", id=0):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    model_dropdown = gr.Dropdown(
+                        choices=["llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768", "gemma-7b-it"],
+                        value="llama3-70b-8192",
+                        label="Model Selection"
+                    )
+                    web_search_toggle = gr.Checkbox(
+                        label="Enable Financial Search",
+                        value=True,
+                        info="Toggle web search functionality"
+                    )
+                    
+            chatbot = gr.Chatbot(
+                height=600,
+                show_copy_button=True,
+                elem_classes="chat-container",
+                container=True
             )
-            
-            # 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.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)
+                msg = gr.Textbox(
+                    show_label=False,
+                    placeholder="Ask about financial markets, analyze stocks, or discuss financial documents...",
+                    scale=8
+                )
                 send_btn = gr.Button("Send", scale=1)
             clear_btn = gr.Button("Clear Conversation")
+
+        # Document Analysis Tab
+        with gr.TabItem("📄 Document Analysis", id=1):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    pdf_file = gr.File(
+                        label="Upload Financial Document",
+                        file_types=[".pdf"],
+                        type="binary"
+                    )
+                    upload_button = gr.Button("Process Document", variant="primary")
+                    pdf_status = gr.Markdown("Upload a document to begin analysis")
+                
+                with gr.Column(scale=2):
+                    with gr.Tabs():
+                        with gr.TabItem("Document Viewer"):
+                            page_slider = gr.Slider(
+                                minimum=1,
+                                maximum=1,
+                                step=1,
+                                label="Page Navigation",
+                                value=1
+                            )
+                            pdf_image = gr.Image(label="Document Page", type="pil")
+                            stats_display = gr.Markdown(elem_classes="stats-box")
+
+        # Financial Tools Tab
+        with gr.TabItem("🔍 Financial Tools", id=2):
+            with gr.Tabs() as financial_tabs:
+                # Stock Analysis
+                with gr.TabItem("Stock Analysis"):
+                    with gr.Row():
+                        with gr.Column():
+                            ticker_input = gr.Textbox(
+                                label="Ticker Symbol",
+                                placeholder="e.g., AAPL, MSFT, GOOGL"
+                            )
+                            period_dropdown = gr.Dropdown(
+                                choices=["1mo", "3mo", "6mo", "1y", "2y", "5y", "max"],
+                                value="1y",
+                                label="Time Period"
+                            )
+                            analyze_stock_btn = gr.Button("Analyze Stock")
+                    with gr.Row():
+                        stock_chart = gr.Plot(label="Stock Price Chart")
+                    stock_analysis = gr.Markdown()
+
+                # Market News
+                with gr.TabItem("Market News"):
+                    news_ticker = gr.Textbox(
+                        label="Company/Ticker",
+                        placeholder="Enter company name or ticker symbol"
+                    )
+                    news_btn = gr.Button("Fetch News")
+                    news_results = gr.Markdown()
+
+                # Financial Report Analysis
+                with gr.TabItem("Report Analysis"):
+                    with gr.Row():
+                        with gr.Column():
+                            report_image = gr.File(
+                                label="Upload Financial Chart/Image",
+                                file_types=["image"],
+                                type="filepath"
+                            )
+                            analyze_report_btn = gr.Button("Analyze Image")
+                        with gr.Column():
+                            report_preview = gr.Image(label="Preview", type="pil")
+                    report_analysis = gr.Markdown()
     
     # 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 appropriate event handlers based on fin-vision functions]
 
 # Add footer with attribution
 gr.HTML("""
@@ -420,4 +866,5 @@ gr.HTML("""
 
 # Launch the app
 if __name__ == "__main__":
+    demo = create_interface()
     demo.launch()