Update app.py

app.py

@@ -1,45 +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
-import torch
-
-# LangChain imports
-from langchain_community.embeddings import HuggingFaceInstructEmbeddings, 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 initialization with fallback
-try:
-    embeddings = HuggingFaceInstructEmbeddings(
-        model_name="all-MiniLM-L6-v2",  # Using simpler model as default
-        model_kwargs={"device": "cuda" if torch.cuda.is_available() else "cpu"}
-    )
-except Exception as e:
-    print(f"Warning: Using basic embeddings due to: {e}")
-    embeddings = HuggingFaceEmbeddings()
-
-SERPER_API_KEY = os.getenv("SERPER_API_KEY")
-BRAVE_API_KEY = os.getenv("BRAVE_API_KEY")
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
 
 # Directory to store FAISS indexes
 FAISS_INDEX_DIR = "faiss_indexes_finance"
@@ -49,672 +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 modern UI enhancements
+# Custom CSS for Finance theme
 custom_css = """
 :root {
-    --main-bg: #ffffff;
-    --main-text: #000000;
-    --accent-primary: #10b981;  // Finance green
-    --accent-secondary: #2563eb; // Blue for references
-    --accent-tertiary: #fb923c;  // Orange for alerts
-    --border-color: #e5e7eb;
-    --radius-md: 8px;
-    --spacing-sm: 8px;
-    --spacing-md: 16px;
-    --spacing-lg: 24px;
-    --shadow-sm: 0 1px 3px rgba(0,0,0,0.12);
-    --shadow-md: 0 4px 6px -1px rgba(0,0,0,0.1);
-    --shadow-lg: 0 10px 15px -3px rgba(0,0,0,0.1);
-}
-
-.dark-mode {
-    --main-bg: #121212;
-    --main-text: #f3f4f6;
-    --border-color: #374151;
-}
-
-/* Chat Interface Styling */
-.chat-container {
-    background: var(--main-bg);
-    border-radius: var(--radius-md);
-    border: 1px solid var(--border-color);
-    padding: var(--spacing-md);
-    margin-bottom: var(--spacing-md);
-    min-height: 600px;
-}
-
-.message {
-    padding: 12px 16px;
-    border-radius: 8px;
-    margin: 8px 0;
-    max-width: 80%;
-}
-
-.user-message {
-    background: #f0f4ff;
-    margin-left: auto;
-    border: 1px solid #d6e0ff;
-}
-
-.bot-message {
-    background: #fff;
-    margin-right: auto;
-    border: 1px solid #e0e0e0;
-}
-
-/* Finance-specific styles */
-.finance-card {
-    background: #f8fafc;
-    border: 1px solid var(--border-color);
-    border-radius: var(--radius-md);
-    padding: var(--spacing-lg);
-    margin: var(--spacing-md) 0;
-    box-shadow: var(--shadow-md);
-    transition: transform 0.3s, box-shadow 0.3s;
-}
-
-.header {
-    text-align: center;
-    padding: var(--spacing-lg) 0;
-    border-bottom: 1px solid var(--border-color);
-    margin-bottom: var(--spacing-lg);
-}
-
-.header-title {
-    font-size: 1.5rem;
-    font-weight: 600;
-    color: var(--main-text);
-    display: flex;
-    align-items: center;
-    justify-content: center;
-    gap: 8px;
-}
-
-.badge {
-    font-size: 0.7em;
-    padding: 2px 8px;
-    border-radius: 12px;
-    background: var(--accent-primary);
-    color: white;
-}
-
-.finance-card:hover {
-    transform: translateY(-5px);
-    box-shadow: var(--shadow-lg);
-    border-color: var(--accent-primary);
-}
-
-.chart-container {
-    background: var(--main-bg);
-    border-radius: var(--radius-md);
-    padding: var(--spacing-md);
-    margin: var(--spacing-md) 0;
-    box-shadow: var(--shadow-sm);
-}
-
-.news-feed {
-    max-height: 400px;
-    overflow-y: auto;
-    padding: var(--spacing-md);
-    border: 1px solid var(--border-color);
-    border-radius: var(--radius-md);
-    background: var(--main-bg);
-}
-
-.news-item {
-    margin-bottom: var(--spacing-md);
-    padding-bottom: var(--spacing-md);
-    border-bottom: 1px solid var(--border-color);
-}
-
-.news-title {
-    font-size: 1.1rem;
-    font-weight: 600;
-    color: var(--main-text);
-    margin-bottom: var(--spacing-sm);
-}
-
-.news-summary {
-    color: var(--main-text-secondary);
-    line-height: 1.6;
-}
-
-.sentiment-gauge {
-    width: 100%;
-    height: 200px;
-    margin: var(--spacing-md) 0;
-}
-
-/* Modern Financial Dashboard Styles */
-.dashboard-grid {
-    display: grid;
-    gap: 1.5rem;
-    grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
-}
-
-.ticker-tape {
-    background: linear-gradient(90deg, var(--accent-primary), var(--accent-secondary));
-    color: white;
-    padding: 1rem;
-    border-radius: var(--radius-md);
-    overflow: hidden;
-    position: relative;
-}
-
-.stock-card {
-    background: white;
-    border-radius: var(--radius-md);
-    padding: 1.5rem;
-    transition: transform 0.3s ease;
-    cursor: pointer;
-    position: relative;
-    overflow: hidden;
-}
-
-.stock-card::before {
-    content: '';
-    position: absolute;
-    top: -50%;
-    left: -50%;
-    width: 200%;
-    height: 200%;
-    background: linear-gradient(45deg, transparent, rgba(255,255,255,0.1));
-    transform: rotate(45deg);
-    transition: all 0.5s ease;
-}
-
-.stock-card:hover {
-    transform: translateY(-5px);
-    box-shadow: 0 10px 20px rgba(0,0,0,0.1);
-}
-
-.stock-card:hover::before {
-    transform: rotate(45deg) translateX(50%);
-}
-
-.realtime-chart {
-    border: 1px solid var(--border-color);
-    border-radius: var(--radius-md);
-    padding: 1rem;
-    background: white;
-    position: relative;
-}
-
-.chart-tooltip {
-    position: absolute;
-    background: var(--main-text);
-    color: var(--main-bg);
-    padding: 0.5rem 1rem;
-    border-radius: var(--radius-sm);
-    pointer-events: none;
-    opacity: 0;
-    transition: opacity 0.2s ease;
-}
-
-.dark-mode .ticker-tape {
-    background: linear-gradient(90deg, #1e3a8a, #991b1b) !important;
-}
-
-.dark-mode .stock-card {
-    background: #2d2d2d !important;
-}
-
-/* Modern Chat Interface */
-.persistent-chat-container {
-    position: fixed;
-    bottom: 0;
-    left: 0;
-    right: 0;
-    height: 400px;
-    background: var(--main-bg);
-    border-top: 1px solid var(--border-color);
-    z-index: 1000;
-    display: flex;
-    flex-direction: column;
-    box-shadow: 0 -2px 10px rgba(0,0,0,0.05);
-}
-
-.dark-mode .persistent-chat-container {
-    background: #121212;
-    border-top: 1px solid #374151;
-}
-
-.chat-messages {
-    flex: 1;
-    overflow-y: auto;
-    padding: 1rem;
-}
-
-.chat-input-area {
-    padding: 1rem;
-    border-top: 1px solid var(--border-color);
-    display: flex;
-    gap: 0.5rem;
-}
-
-.dark-mode .chat-input-area {
-    border-top: 1px solid #374151;
-}
-
-.chat-input {
-    flex: 1;
-    padding: 0.75rem;
-    border: 1px solid var(--border-color);
-    border-radius: 8px;
-    resize: none;
-}
-
-.dark-mode .chat-input {
-    background: #1e1e1e;
-    color: white;
-    border-color: #4b5563;
-}
-
-.send-button {
-    background: var(--accent-primary);
-    color: white;
-    border: none;
-    border-radius: 8px;
-    padding: 0 1.5rem;
-    cursor: pointer;
-    font-weight: 600;
-}
-
-.content-with-chat {
-    padding-bottom: 400px;
-}
-
-.tab-content {
-    padding-bottom: 400px;
-}
-
-@media (max-width: 768px) {
-    .persistent-chat-container {
-        height: 300px;
-    }
-    
-    .content-with-chat {
-        padding-bottom: 300px;
-    }
-}
-
-.stock-badge {
-    background: var(--accent-primary);
-    color: white;
-    font-size: 0.8rem;
-    padding: 0.3rem 0.6rem;
-    border-radius: 1rem;
-    display: inline-block;
-    margin-left: 0.5rem;
-}
-
-.stock-up {
-    color: #10b981;
-}
-
-.stock-down {
-    color: #ef4444;
-}
-
-.dark-mode .stock-up {
-    color: #34d399;
-}
-
-.dark-mode .stock-down {
-    color: #f87171;
-}
-
-.chart-container {
-    border: 1px solid var(--border-color);
-    border-radius: var(--radius-md);
-    padding: var(--spacing-md);
-}
-
-.dark-mode .chart-container {
-    border-color: #374151;
-}
-
-/* Add sidebar and layout CSS */
-custom_css += """
-/* Sidebar Navigation */
-.sidebar {
-    position: fixed;
-    left: 0;
-    top: 0;
-    bottom: 400px; /* Leave space for chat */
-    width: 250px;
-    background: var(--main-bg);
-    border-right: 1px solid var(--border-color);
-    padding: 1rem;
-    overflow-y: auto;
-    z-index: 100;
-    transition: all 0.3s ease;
-}
-
-.dark-mode .sidebar {
-    background: #1a1a1a;
-    border-right: 1px solid #333;
-}
-
-.sidebar-header {
-    padding-bottom: 1rem;
-    margin-bottom: 1rem;
-    border-bottom: 1px solid var(--border-color);
-}
-
-.sidebar-nav {
-    display: flex;
-    flex-direction: column;
-    gap: 0.5rem;
-}
-
-.nav-item {
-    padding: 0.75rem 1rem;
-    border-radius: 8px;
-    cursor: pointer;
-    display: flex;
-    align-items: center;
-    gap: 0.75rem;
-    transition: all 0.2s ease;
-}
-
-.nav-item:hover {
-    background: rgba(0,0,0,0.05);
-}
-
-.dark-mode .nav-item:hover {
-    background: rgba(255,255,255,0.05);
-}
-
-.nav-item.active {
-    background: var(--accent-primary);
-    color: white;
-}
-
-.nav-icon {
-    font-size: 1.25rem;
-    width: 1.5rem;
-    text-align: center;
-}
-
-/* Main Content Area */
-.main-content {
-    margin-left: 250px;
-    padding: 1rem;
-    padding-bottom: 400px; /* Space for chat */
-    min-height: calc(100vh - 400px);
-}
-
-.collapsed-sidebar .sidebar {
-    width: 60px;
-}
-
-.collapsed-sidebar .main-content {
-    margin-left: 60px;
-}
-
-.collapsed-sidebar .nav-text {
-    display: none;
-}
-
-.collapsed-sidebar .sidebar-header {
-    text-align: center;
-}
-
-/* Split Screen Mode */
-.split-screen .main-content {
-    height: calc(50vh - 200px);
-    overflow-y: auto;
-}
-
-.split-screen .persistent-chat-container {
-    height: 50vh;
-}
-
-.split-screen .content-with-chat {
-    padding-bottom: 50vh;
-}
-
-/* Toggle Buttons */
-.toggle-button {
-    position: fixed;
-    z-index: 2000;
-    background: var(--accent-primary);
-    color: white;
-    border: none;
-    border-radius: 50%;
-    width: 40px;
-    height: 40px;
-    display: flex;
-    align-items: center;
-    justify-content: center;
-    font-size: 1.25rem;
-    cursor: pointer;
-    box-shadow: 0 2px 5px rgba(0,0,0,0.2);
-}
-
-.sidebar-toggle {
-    left: 260px;
-    top: 20px;
-}
-
-.split-toggle {
-    right: 80px;
-    bottom: 410px;
-}
-
-.dark-mode .toggle-button {
-    background: #333;
-}
-
-/* Improve responsive design */
-@media (max-width: 768px) {
-    .sidebar {
-        width: 60px;
-    }
-    
-    .main-content {
-        margin-left: 60px;
-    }
-    
-    .nav-text {
-        display: none;
-    }
-    
-    .sidebar-header {
-        text-align: center;
-    }
-    
-    .sidebar-toggle {
-        left: 70px;
-    }
-}
-
-/* Add code viewer and PDF viewer styles */
-custom_css += """
-/* Tool panels */
-.tool-panel {
-    background: var(--main-bg);
-    border-radius: var(--radius-md);
-    border: 1px solid var(--border-color);
-    padding: 1rem;
-    margin-bottom: 1rem;
-}
-
-.dark-mode .tool-panel {
-    background: #1a1a1a;
-    border: 1px solid #333;
-}
-
-/* Code Editor Styles */
-.code-editor-container {
-    display: flex;
-    flex-direction: column;
-    height: 100%;
-}
-
-.code-editor-header {
-    display: flex;
-    justify-content: space-between;
-    align-items: center;
-    padding-bottom: 0.5rem;
-    margin-bottom: 1rem;
-    border-bottom: 1px solid var(--border-color);
-}
-
-.code-editor-filename {
-    font-weight: 600;
-    color: var(--main-text);
-}
-
-.code-editor-controls {
-    display: flex;
-    gap: 0.5rem;
-}
-
-.code-editor-area {
-    font-family: monospace;
-    line-height: 1.5;
-    height: 100%;
-    border: 1px solid var(--border-color);
-    border-radius: var(--radius-md);
-    padding: 1rem;
-    background: #f8f9fa;
-    overflow: auto;
-}
-
-.dark-mode .code-editor-area {
-    background: #1e1e1e;
-    color: #f0f0f0;
-    border-color: #333;
-}
-
-.code-editor-area.editable {
-    border-color: var(--accent-primary);
-    background: #f0f7f4;
-}
-
-.dark-mode .code-editor-area.editable {
-    background: #162922;
-}
-
-/* Enhanced PDF Viewer */
-.pdf-toolbar {
-    display: flex;
-    gap: 0.5rem;
-    margin-bottom: 1rem;
-    padding-bottom: 0.5rem;
-    border-bottom: 1px solid var(--border-color);
-}
-
-.pdf-container {
-    display: flex;
-    flex-direction: column;
-    height: 100%;
-}
-
-.pdf-sidebar {
-    width: 25%;
-    border-right: 1px solid var(--border-color);
-    overflow-y: auto;
-    padding-right: 0.5rem;
-}
-
-.pdf-content {
-    flex: 1;
-    overflow: auto;
-    padding: 1rem;
-    background: #f8f9fa;
-    border-radius: var(--radius-md);
-}
-
-.dark-mode .pdf-content {
-    background: #1e1e1e;
-}
-
-.pdf-thumbnail {
-    cursor: pointer;
-    margin-bottom: 0.5rem;
-    border: 2px solid transparent;
-    border-radius: var(--radius-sm);
-    transition: all 0.2s ease;
-}
-
-.pdf-thumbnail:hover {
-    transform: translateY(-2px);
-}
-
-.pdf-thumbnail.active {
-    border-color: var(--accent-primary);
-}
-
-.pdf-search {
-    margin-bottom: 1rem;
-}
-
-.pdf-search-results {
-    margin-top: 0.5rem;
-    font-size: 0.9rem;
-}
-
-.pdf-search-highlight {
-    background: rgba(255, 213, 0, 0.3);
-    border-radius: 2px;
-}
-
-/* Initial state with just chatbot */
-.tools-hidden .sidebar {
-    display: block;
-}
-
-.tools-hidden .main-content {
-    display: none;
-}
-
-.tools-visible .main-content {
-    display: block;
-}
+    --primary-color: #FFD700;  /* Gold */
+    --secondary-color: #008000;  /* Dark Green */
+    --light-background: #F0FFF0;  /* Honeydew */
+    --dark-text: #333333;
+    --white: #FFFFFF;
+    --border-color: #E5E7EB;
+}
+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(--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(--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: 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: #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; }
 """
 
-# Add JavaScript interactions
-financial_js = """
-<script>
-document.addEventListener('DOMContentLoaded', () => {
-    // Real-time chart interactions
-    const charts = document.querySelectorAll('.realtime-chart');
-    charts.forEach(chart => {
-        chart.addEventListener('mousemove', (e) => {
-            const tooltip = chart.querySelector('.chart-tooltip');
-            if(tooltip) {
-                tooltip.style.left = `${e.offsetX + 10}px`;
-                tooltip.style.top = `${e.offsetY + 10}px`;
-                tooltip.style.opacity = '1';
-            }
-        });
-        
-        chart.addEventListener('mouseleave', () => {
-            const tooltip = chart.querySelector('.chart-tooltip');
-            if(tooltip) tooltip.style.opacity = '0';
-        });
-    });
-
-    // Animated ticker tape
-    const ticker = document.querySelector('.ticker-tape');
-    if(ticker) {
-        let position = 0;
-        setInterval(() => {
-            position -= 1;
-            ticker.style.backgroundPosition = `${position}px 0`;
-        }, 50);
-    }
-});
-</script>
-"""
-
-# 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}
@@ -751,196 +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:
@@ -956,98 +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
         
@@ -1061,86 +141,12 @@ def generate_response(message, session_id, model_name, history, current_ticker=N
             max_tokens=1024
         )
         response = completion.choices[0].message.content
-        disclaimer = "\n\n*Note: This is informational only and not financial advice. Consult a professional advisor for investment decisions.*"
-        response += disclaimer
-        
         history.append((message, response))
         return history
     except Exception as e:
         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"]:
@@ -1276,7 +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
@@ -1287,29 +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']:,}  
@@ -1317,576 +305,71 @@ 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."
-    
-    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 main interface layout with sidebar, content area, and persistent chat
+# Gradio interface
 with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
-    # Shared state
     current_session_id = gr.State(None)
     pdf_state = gr.State({"page_images": [], "total_pages": 0, "total_words": 0})
-    market_data = gr.State({})
-    chat_history = gr.State([])
-    selected_tool = gr.State("market-analysis") # Default selected tool
-    is_split_screen = gr.State(False) # Split screen toggle state
-    is_sidebar_collapsed = gr.State(False) # Sidebar collapsed state
+    current_ticker = gr.State(None)
     
-    # App Header
     gr.HTML("""
     <div class="header">
-        <div class="header-title">💹 Fin-Vision AI 
-            <span class="stock-badge">Market Analysis</span>
-        </div>
-        <div class="header-subtitle">Financial Document & Market Copilot</div>
+        <div class="header-title">Fin-Vision</div>
+        <div class="header-subtitle">Analyze financial documents with Groq's LLM API.</div>
     </div>
     """)
     
-    # Sidebar Navigation
-    with gr.Column(elem_classes=["sidebar"]):
-        gr.HTML("""
-        <div class="sidebar-header">
-            <div class="sidebar-logo">📊 Fin-AI</div>
-        </div>
-        """)
-        
-        with gr.Column(elem_classes=["sidebar-nav"]):
-            with gr.Group():
-                market_btn = gr.Button("📈 Market Analysis", elem_classes=["nav-item"])
-                document_btn = gr.Button("📄 Documents", elem_classes=["nav-item"])
-                economic_btn = gr.Button("📊 Economic Data", elem_classes=["nav-item"])
-                news_btn = gr.Button("📰 Financial News", elem_classes=["nav-item"])
-                portfolio_btn = gr.Button("💼 Portfolio", elem_classes=["nav-item"])
-                pdf_viewer_btn = gr.Button("📕 PDF Viewer", elem_classes=["nav-item", "active"])
-                code_editor_btn = gr.Button("💻 Code Editor", elem_classes=["nav-item"])
-                settings_btn = gr.Button("⚙️ Settings", elem_classes=["nav-item"])
-    
-    # Main Content Area
-    with gr.Column(elem_classes=["main-content"]):
-        # PDF Viewer Tool
-        with gr.Group(visible=True) as pdf_viewer_tool:
-            with gr.Row():
-                gr.Markdown("## 📕 PDF Viewer Tool")
-        
-        with gr.Row():
-            with gr.Column(scale=1):
-                pdf_file_upload = gr.File(
-                    label="Upload PDF Document",
-                    file_types=[".pdf"],
-                    type="binary"
-                )
-                with gr.Row():
-                    pdf_process_btn = gr.Button("Process PDF", variant="primary")
-                    pdf_clear_btn = gr.Button("Clear", variant="secondary")
-                
-                # PDF Navigation
-                with gr.Box():
-                    gr.Markdown("### Navigation")
-                    with gr.Row():
-                        pdf_prev_btn = gr.Button("◀ Previous")
-                        pdf_page_slider = gr.Slider(
-                            minimum=1,
-                            maximum=1,
-                            step=1,
-                            label="Page",
-                            value=1
-                        )
-                        pdf_next_btn = gr.Button("Next ▶")
-                    
-                    # Search functionality
-                    with gr.Row():
-                        pdf_search_input = gr.Textbox(
-                            label="Search in PDF",
-                            placeholder="Enter search term...",
-                            elem_classes=["pdf-search"]
-                        )
-                        pdf_search_btn = gr.Button("Search")
-                    
-                    pdf_search_results = gr.Markdown(elem_classes=["pdf-search-results"])
-                    
-                    # Statistics and metadata
-                    pdf_stats = gr.Markdown(elem_classes=["stats-box"])
-            
-            with gr.Column(scale=2):
-                # PDF Display
-                with gr.Box(elem_classes=["pdf-container"]):
-                    with gr.Row():
-                        pdf_thumbnail_gallery = gr.Gallery(
-                            label="Thumbnails",
-                            elem_classes=["pdf-thumbnail-gallery"],
-                            columns=1,
-                            rows=5,
-                            height=500
-                        )
-                        
-                        pdf_display = gr.Image(
-                            label="Document View",
-                            type="pil",
-                            elem_classes=["pdf-display"],
-                            height=600
-                        )
-                
-                # Extracted text
-                pdf_text = gr.Textbox(
-                    label="Extracted Text",
-                    lines=10,
-                    max_lines=20,
-                    interactive=False,
-                    elem_classes=["pdf-text"]
-                )
-    
-    # Code Editor Tool
-    with gr.Group(visible=False) as code_editor_tool:
-        with gr.Row():
-            gr.Markdown("## 💻 Code Editor")
-        
-        with gr.Row():
-            with gr.Column(scale=1):
-                # File selection and management
-                code_file_upload = gr.File(
-                    label="Upload Code File",
-                    file_types=[".py", ".js", ".html", ".css", ".json", ".txt"],
-                    type="binary"
-                )
-                
-                with gr.Row():
-                    code_load_btn = gr.Button("Load File", variant="primary")
-                    code_clear_btn = gr.Button("Clear", variant="secondary")
-                
-                # File browser
-                with gr.Box():
-                    gr.Markdown("### File Browser")
-                    code_file_path = gr.Textbox(
-                        label="File Path",
-                        placeholder="Enter file path to open...",
-                        value=""
-                    )
-                    code_file_list = gr.Dropdown(
-                        label="Recent Files",
-                        choices=[],
-                        interactive=True
-                    )
-                    with gr.Row():
-                        code_browse_btn = gr.Button("Browse")
-                        code_refresh_btn = gr.Button("↻ Refresh")
-                
-                # Code analysis
-                with gr.Box():
-                    gr.Markdown("### Code Analysis")
-                    code_analyze_btn = gr.Button("Analyze Code")
-                    code_analysis_results = gr.Markdown("Click 'Analyze Code' to get analysis")
-            
-            with gr.Column(scale=2):
-                # Code editor header
-                with gr.Row(elem_classes=["code-editor-header"]):
-                    code_filename = gr.Textbox(
-                        label="Filename",
-                        value="",
-                        interactive=False,
-                        elem_classes=["code-editor-filename"]
-                    )
-                    with gr.Row(elem_classes=["code-editor-controls"]):
-                        code_edit_toggle = gr.Checkbox(
-                            label="Edit Mode",
-                            value=False
-                        )
-                        code_save_btn = gr.Button("Save", variant="primary")
-                
-                # Code editor
-                code_content = gr.Code(
-                    label="",
-                    language="python",
-                    value="# No file loaded\n\n# Upload or select a file to edit",
-                    interactive=False,
-                    elem_classes=["code-editor-area"]
-                )
-                
-                # Execution results
-                code_output = gr.Textbox(
-                    label="Output",
-                    lines=5,
-                    interactive=False
-                )
-    
-    # Market Analysis Tool (keeping as reference but initially hidden)
-    with gr.Group(visible=False) as market_tool:
-        gr.Markdown("## 📈 Market Analysis")
-        with gr.Row():
-            with gr.Column(scale=1):
-                ticker_input = gr.Textbox(
-                    label="Stock Ticker",
-                    placeholder="Enter ticker symbol (e.g., AAPL, MSFT)",
-                    value=""
-                )
-                period_dropdown = gr.Dropdown(
-                    choices=["1mo", "3mo", "6mo", "1y", "2y", "5y", "10y", "ytd", "max"],
-                    value="1y",
-                    label="Time Period"
-                )
-                analyze_btn = gr.Button("Analyze Stock", variant="primary")
-            
-            with gr.Column(scale=2):
-                stock_chart = gr.Plot(label="Stock Price Chart")
-                stock_info = gr.Markdown(elem_classes="finance-card")
-    
-    # Document Analysis Tool (keeping as reference but initially hidden)
-    with gr.Group(visible=False) as document_tool:
-        gr.Markdown("## 📄 Financial Documents")
-        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.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):
+            # Finance Tools Section
+            gr.Markdown("### Financial Tools", elem_classes="tool-title")
+            with gr.Group(elem_classes="tool-container"):
                 with gr.Tabs():
-                    with gr.TabItem("Document Viewer"):
-                        page_slider = gr.Slider(
-                            minimum=1,
-                            maximum=1,
-                            step=1,
-                            label="Page Navigation",
-                            value=1
+                    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"
                         )
-                        pdf_image = gr.Image(label="Document Page", type="pil")
-                        stats_display = gr.Markdown(elem_classes="stats-box")
-    
-    # Economic Data Tool (keeping as reference but initially hidden)
-    with gr.Group(visible=False) as economic_tool:
-        gr.Markdown("## 📊 Economic Indicators")
-        with gr.Row():
-            with gr.Column(scale=1):
-                indicator_dropdown = gr.Dropdown(
-                    choices=["GDP", "Unemployment", "Inflation", "Interest Rates", "Consumer Confidence"],
-                    value="GDP",
-                    label="Economic Indicator"
-                )
-                indicator_btn = gr.Button("Fetch Data", variant="primary")
-            
-            with gr.Column(scale=2):
-                indicator_chart = gr.Plot(label="Economic Indicator")
-                indicator_info = gr.Markdown(elem_classes="finance-card")
-    
-    # News Tool (keeping as reference but initially hidden)
-    with gr.Group(visible=False) as news_tool:
-        gr.Markdown("## 📰 Financial News")
-        with gr.Row():
-            with gr.Column():
-                news_query = gr.Textbox(
-                    label="Search News",
-                    placeholder="Enter topic, company, or leave blank for top financial news"
-                )
-                news_btn = gr.Button("Get News", variant="primary")
-                news_results = gr.Markdown("Click 'Get News' to fetch the latest financial headlines")
-    
-    # Portfolio Tool (keeping as reference but initially hidden)
-    with gr.Group(visible=False) as portfolio_tool:
-        gr.Markdown("## 💼 Portfolio Tracker")
-        with gr.Row():
-            gr.Markdown("Portfolio tracking features coming soon!")
-    
-    # Settings Tool (keeping as reference but initially hidden)
-    with gr.Group(visible=False) as settings_tool:
-        gr.Markdown("## ⚙️ Settings")
-        with gr.Row():
-            with gr.Column():
-                theme_toggle = gr.Checkbox(label="Dark Mode", value=False)
-                api_key_input = gr.Textbox(
-                    label="API Key (Optional)",
-                    placeholder="Enter your own API key for extended functionality",
-                    type="password"
-                )
-                save_btn = gr.Button("Save Settings", variant="primary")
-    
-    # Toggle buttons for sidebar collapse and split screen
-    gr.HTML("""
-    <div class="sidebar-toggle toggle-button">
-        <span id="sidebar-toggle-icon">◀</span>
-    </div>
-    <div class="split-toggle toggle-button">
-        <span id="split-toggle-icon">⬍</span>
-    </div>
-    """)
-    
-    # Persistent Chat Interface
-    with gr.Column(elem_classes=["persistent-chat-container"]):
-        with gr.Column(elem_classes=["chat-messages"]) as chat_display:
-            chatbot = gr.Chatbot(
-                show_copy_button=True,
-                avatar_images=(
-                    "assets/user.png",
-                    "assets/bot_finance.png"  
-                )
-            )
+                        analyze_button = gr.Button("Analyze Stock")
             
-        with gr.Row(elem_classes=["chat-input-area"]):
-            msg = gr.Textbox(
-                show_label=False,
-                placeholder="Ask questions about financial data & documents...",
-                elem_classes=["chat-input"]
-            )
-            send_btn = gr.Button("Send", elem_classes=["send-button"])
-    
-    # Event Handlers for Navigation
-    def switch_to_tool(tool_name):
-        """Switch to a specific tool by name"""
-        is_market = tool_name == "market"
-        is_document = tool_name == "document"
-        is_economic = tool_name == "economic"
-        is_news = tool_name == "news"
-        is_portfolio = tool_name == "portfolio"
-        is_pdf_viewer = tool_name == "pdf_viewer"
-        is_code_editor = tool_name == "code_editor"
-        is_settings = tool_name == "settings"
-        
-        return (
-            is_market,
-            is_document,
-            is_economic,
-            is_news,
-            is_portfolio,
-            is_pdf_viewer,
-            is_code_editor,
-            is_settings,
-            "active" if is_market else "",
-            "active" if is_document else "",
-            "active" if is_economic else "",
-            "active" if is_news else "",
-            "active" if is_portfolio else "",
-            "active" if is_pdf_viewer else "",
-            "active" if is_code_editor else "",
-            "active" if is_settings else ""
-        )
-    
-    def select_market_tool():
-        return switch_to_tool("market")
-    
-    def select_document_tool():
-        return switch_to_tool("document")
-    
-    def select_economic_tool():
-        return switch_to_tool("economic")
-    
-    def select_news_tool():
-        return switch_to_tool("news")
-    
-    def select_portfolio_tool():
-        return switch_to_tool("portfolio")
-    
-    def select_pdf_viewer_tool():
-        return switch_to_tool("pdf_viewer")
-    
-    def select_code_editor_tool():
-        return switch_to_tool("code_editor")
-    
-    def select_settings_tool():
-        return switch_to_tool("settings")
-    
-    market_btn.click(
-        fn=select_market_tool,
-        inputs=None,
-        outputs=[
-            market_tool, document_tool, economic_tool, news_tool, portfolio_tool, pdf_viewer_tool, code_editor_tool, settings_tool,
-            market_btn, document_btn, economic_btn, news_btn, portfolio_btn, pdf_viewer_btn, code_editor_btn, settings_btn
-        ]
-    )
-    
-    document_btn.click(
-        fn=select_document_tool,
-        inputs=None,
-        outputs=[
-            market_tool, document_tool, economic_tool, news_tool, portfolio_tool, pdf_viewer_tool, code_editor_tool, settings_tool,
-            market_btn, document_btn, economic_btn, news_btn, portfolio_btn, pdf_viewer_btn, code_editor_btn, settings_btn
-        ]
-    )
-    
-    economic_btn.click(
-        fn=select_economic_tool,
-        inputs=None,
-        outputs=[
-            market_tool, document_tool, economic_tool, news_tool, portfolio_tool, pdf_viewer_tool, code_editor_tool, settings_tool,
-            market_btn, document_btn, economic_btn, news_btn, portfolio_btn, pdf_viewer_btn, code_editor_btn, settings_btn
-        ]
-    )
-    
-    news_btn.click(
-        fn=select_news_tool,
-        inputs=None,
-        outputs=[
-            market_tool, document_tool, economic_tool, news_tool, portfolio_tool, pdf_viewer_tool, code_editor_tool, settings_tool,
-            market_btn, document_btn, economic_btn, news_btn, portfolio_btn, pdf_viewer_btn, code_editor_btn, settings_btn
-        ]
-    )
-    
-    portfolio_btn.click(
-        fn=select_portfolio_tool,
-        inputs=None,
-        outputs=[
-            market_tool, document_tool, economic_tool, news_tool, portfolio_tool, pdf_viewer_tool, code_editor_tool, settings_tool,
-            market_btn, document_btn, economic_btn, news_btn, portfolio_btn, pdf_viewer_btn, code_editor_btn, settings_btn
-        ]
-    )
-    
-    pdf_viewer_btn.click(
-        fn=select_pdf_viewer_tool,
-        inputs=None,
-        outputs=[
-            market_tool, document_tool, economic_tool, news_tool, portfolio_tool, pdf_viewer_tool, code_editor_tool, settings_tool,
-            market_btn, document_btn, economic_btn, news_btn, portfolio_btn, pdf_viewer_btn, code_editor_btn, settings_btn
-        ]
-    )
-    
-    code_editor_btn.click(
-        fn=select_code_editor_tool,
-        inputs=None,
-        outputs=[
-            market_tool, document_tool, economic_tool, news_tool, portfolio_tool, pdf_viewer_tool, code_editor_tool, settings_tool,
-            market_btn, document_btn, economic_btn, news_btn, portfolio_btn, pdf_viewer_btn, code_editor_btn, settings_btn
-        ]
-    )
-    
-    settings_btn.click(
-        fn=select_settings_tool,
-        inputs=None,
-        outputs=[
-            market_tool, document_tool, economic_tool, news_tool, portfolio_tool, pdf_viewer_tool, code_editor_tool, settings_tool,
-            market_btn, document_btn, economic_btn, news_btn, portfolio_btn, pdf_viewer_btn, code_editor_btn, settings_btn
-        ]
-    )
-    
-    # Existing event handlers
-    def process_message(message, history, session_id, pdf_state, market_data):
-        if not message:
-            return history, ""
-        
-        # Access context from document analysis if available
-        context = ""
-        if session_id and session_id in user_vectorstores:
-            vectorstore = user_vectorstores[session_id]
-            docs = vectorstore.similarity_search(message, k=3)
-            if docs:
-                context = "\n\nRelevant information from financial document:\n" + "\n".join(f"- {doc.page_content}" for doc in docs)
-        
-        # Add stock market data if available
-        if market_data and 'ticker' in market_data:
-            ticker = market_data['ticker']
-            context += f"\n\nStock data for {ticker} is available from the market analysis tab."
-            if 'price' in market_data:
-                context += f"\nCurrent price: ${market_data['price']}"
-            if 'change' in market_data:
-                context += f"\nChange: {market_data['change']}%"
-        
-        # Check if it's a special command for stock lookup
-        if message.lower().startswith("/stock "):
-            ticker = message.split(" ", 1)[1].strip().upper()
-            try:
-                # Get stock data
-                stock_data = get_stock_data(ticker)
-                # Format response
-                response = f"**Stock Information for {ticker}**\n\n"
-                response += f"Current Price: ${stock_data['price']:.2f}\n"
-                response += f"Change: {stock_data['change']:.2f}% "
-                response += "📈" if stock_data['change'] > 0 else "📉"
-                response += f"\nVolume: {stock_data['volume']:,}\n"
-                response += f"Market Cap: ${stock_data['market_cap']:,.2f}\n"
+        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")
                 
-                # Add market data for future context
-                market_data['ticker'] = ticker
-                market_data['price'] = stock_data['price']
-                market_data['change'] = stock_data['change']
-                
-                history.append((message, response))
-                return history, ""
-            except Exception as e:
-                response = f"Error retrieving stock data for {ticker}: {str(e)}"
-                history.append((message, response))
-                return history, ""
-        
-        # Generate response
-        system_prompt = """You are a financial assistant specializing in analyzing financial data, market trends, and investment documents.
-        You provide clear, accurate information about stocks, economic indicators, and financial reports.
-        Always note that you're not providing investment advice and users should consult with a qualified financial advisor."""
-        
-        if context:
-            system_prompt += f"\nUse this context to inform your response if relevant: {context}"
-        
-        completion = client.chat.completions.create(
-            model="llama3-70b-8192",
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": message}
-            ],
-            temperature=0.7,
-            max_tokens=1024
-        )
-        
-        response = completion.choices[0].message.content
-        disclaimer = "\n\n*Note: This is informational only and not financial advice. Consult a professional advisor for investment decisions.*"
-        response += disclaimer
-        
-        history.append((message, response))
-        return history, ""
-    
-    # Keep other existing event handlers
-    send_btn.click(
-        process_message,
-        inputs=[msg, chatbot, current_session_id, pdf_state, market_data],
-        outputs=[chatbot, msg]
-    )
-    
-    msg.submit(
-        process_message,
-        inputs=[msg, chatbot, current_session_id, pdf_state, market_data],
-        outputs=[chatbot, msg]
-    )
+                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],
@@ -1897,126 +380,44 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
         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]
     )
     
-    analyze_btn.click(
-        lambda ticker, period: (create_stock_chart(ticker, period), get_stock_analysis(ticker)),
+    # Stock analysis handler
+    analyze_button.click(
+        analyze_ticker,
         inputs=[ticker_input, period_dropdown],
-        outputs=[stock_chart, stock_info]
-    ).then(
-        lambda ticker: {'ticker': ticker},
-        inputs=[ticker_input],
-        outputs=[market_data]
-    )
-    
-    indicator_btn.click(
-        lambda indicator: (get_economic_indicator_chart(indicator), get_economic_indicator_info(indicator)),
-        inputs=[indicator_dropdown],
-        outputs=[indicator_chart, indicator_info]
+        outputs=[stock_chart, stock_summary, current_ticker]
     )
-    
-    # Add logic for news search
-    news_btn.click(
-        lambda query: get_financial_news(query) if query else get_financial_news("top financial news"),
-        inputs=[news_query],
-        outputs=[news_results]
-    )
-    
-    # Add JavaScript for toggle functionality
-    gr.HTML("""
-    <script>
-    document.addEventListener('DOMContentLoaded', () => {
-        // Dark mode toggle
-        const darkModeToggle = document.querySelector('input[aria-label="Dark Mode"]');
-        if (darkModeToggle) {
-            darkModeToggle.addEventListener('change', () => {
-                document.body.classList.toggle('dark-mode', darkModeToggle.checked);
-                localStorage.setItem('dark-mode', darkModeToggle.checked);
-            });
-            
-            // Check for saved preference
-            if (localStorage.getItem('dark-mode') === 'true') {
-                darkModeToggle.checked = true;
-                document.body.classList.add('dark-mode');
-            }
-        }
-        
-        // Sidebar toggle
-        const sidebarToggle = document.querySelector('.sidebar-toggle');
-        const sidebarToggleIcon = document.getElementById('sidebar-toggle-icon');
-        
-        if (sidebarToggle) {
-            sidebarToggle.addEventListener('click', () => {
-                document.body.classList.toggle('collapsed-sidebar');
-                sidebarToggleIcon.textContent = document.body.classList.contains('collapsed-sidebar') ? '▶' : '◀';
-                localStorage.setItem('sidebar-collapsed', document.body.classList.contains('collapsed-sidebar'));
-            });
-            
-            // Check for saved preference
-            if (localStorage.getItem('sidebar-collapsed') === 'true') {
-                document.body.classList.add('collapsed-sidebar');
-                sidebarToggleIcon.textContent = '▶';
-            }
-        }
-        
-        // Split screen toggle
-        const splitToggle = document.querySelector('.split-toggle');
-        const splitToggleIcon = document.getElementById('split-toggle-icon');
-        
-        if (splitToggle) {
-            splitToggle.addEventListener('click', () => {
-                document.body.classList.toggle('split-screen');
-                document.body.classList.toggle('tools-visible');
-                splitToggleIcon.textContent = document.body.classList.contains('split-screen') ? '⬆' : '⬍';
-                localStorage.setItem('split-screen', document.body.classList.contains('split-screen'));
-            });
-            
-            // Check for saved preference
-            if (localStorage.getItem('split-screen') === 'true') {
-                document.body.classList.add('split-screen');
-                document.body.classList.add('tools-visible');
-                splitToggleIcon.textContent = '⬆';
-            } else {
-                // Default: show just chat at first
-                document.body.classList.add('tools-hidden');
-            }
-        }
-        
-        // Code editor syntax highlighting enhancement
-        const codeEditor = document.querySelector('.code-editor-area');
-        if (codeEditor) {
-            // Add line numbers
-            const addLineNumbers = () => {
-                const content = codeEditor.textContent;
-                const lines = content.split('\n');
-                const numberedLines = lines.map((line, i) => `${i+1} | ${line}`);
-                codeEditor.textContent = numberedLines.join('\n');
-            };
-            
-            // Observe when edit mode is toggled
-            const editToggle = document.querySelector('input[aria-label="Edit Mode"]');
-            if (editToggle) {
-                editToggle.addEventListener('change', () => {
-                    codeEditor.classList.toggle('editable', editToggle.checked);
-                });
-            }
-        }
-    });
-    </script>
-    """)
-    
-    # Add footer
-    gr.HTML("""
-    <div style="text-align: center; margin-top: 20px; padding: 20px; color: #666; font-size: 0.9rem; border-top: 1px solid #eee;">
-        Created by Calvin Allen-Crawford<br>
-        <span style="font-style: italic; font-size: 0.8rem;">Founder of Cosmick Visions</span>
-    </div>
-    """)
 
-# Launch the app with modern UI
+# Add footer with attribution
+gr.HTML("""
+<div style="text-align: center; margin-top: 20px; padding: 10px; color: #666; font-size: 0.8rem; border-top: 1px solid #eee;">
+    Created by Calvin Allen Crawford
+</div>
+""")
+
+# Launch the app
 if __name__ == "__main__":
     demo.launch()