Update app.py

app.py

@@ -1,35 +1,21 @@
-# Standard library imports
+import gradio as gr
+import groq
 import os
 import tempfile
 import uuid
+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 openpyxl
-import requests
-import fitz  # PyMuPDF
-from PIL import Image
-from dotenv import load_dotenv
-from transformers import AutoProcessor, AutoModelForVision2Seq
-import torch
-import sass
-from pathlib import Path
-import pyttsx3
-import speech_recognition as sr
-
-# LangChain imports
-from langchain_community.embeddings import HuggingFaceEmbeddings
-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_TECH_API_KEY"))
@@ -43,371 +29,47 @@ if not os.path.exists(FAISS_INDEX_DIR):
 # Dictionary to store user-specific vectorstores
 user_vectorstores = {}
 
-# Advanced SCSS with cyberpunk styling
-CYBERPUNK_SCSS = """
-// Advanced Cyberpunk Theme with Neural Network Aesthetics
-@use "sass:math";
-@use "sass:color";
-
-// Neural Color System
-$neural-colors: (
-    'synapse-blue': #00F3FF,
-    'neural-red': #FF0033,
-    'data-yellow': #FFE600,
-    'matrix-green': #00FF9F,
-    'void-black': #0D0D0D,
-    'deep-void': #080808,
-    'neural-white': #E6E6E6,
-    'grid-alpha': 0.1
-);
-
-// Dynamic Color Functions
-@function neural-glow($color, $intensity: 1) {
-    $glow-color: map-get($neural-colors, $color);
-    @return (
-        0 0 #{10px * $intensity} $glow-color,
-        0 0 #{20px * $intensity} $glow-color
-    );
-}
-
-@function generate-glitch-animation($name, $color1, $color2) {
-    @keyframes #{$name} {
-        0%, 100% { 
-            text-shadow: -2px 0 map-get($neural-colors, $color1), 
-                        2px 2px map-get($neural-colors, $color2);
-        }
-        25% {
-            text-shadow: 2px -2px map-get($neural-colors, $color1),
-                        -2px -2px map-get($neural-colors, $color2);
-        }
-        50% {
-            text-shadow: 1px 3px map-get($neural-colors, $color1),
-                        -3px -1px map-get($neural-colors, $color2);
-        }
-        75% {
-            text-shadow: -3px 1px map-get($neural-colors, $color1),
-                        1px -1px map-get($neural-colors, $color2);
-        }
-    }
-}
-
-// Generate Multiple Glitch Animations
-#{generate-glitch-animation('neural-glitch', 'synapse-blue', 'neural-red')}
-#{generate-glitch-animation('data-glitch', 'data-yellow', 'matrix-green')}
-
-// Advanced Mixins
-@mixin neural-container($depth: 1) {
-    background: linear-gradient(
-        170deg,
-        rgba(map-get($neural-colors, 'deep-void'), 0.9),
-        rgba(map-get($neural-colors, 'void-black'), 0.95)
-    );
-    border: #{$depth}px solid map-get($neural-colors, 'synapse-blue');
-    box-shadow: neural-glow('synapse-blue', $depth);
-    backdrop-filter: blur(5px);
-    position: relative;
-    overflow: hidden;
-
-    &::before {
-    content: '';
-    position: absolute;
-    top: 0;
-    left: 0;
-        right: 0;
-        height: 1px;
-        background: linear-gradient(
-            90deg,
-            transparent,
-            map-get($neural-colors, 'synapse-blue'),
-            transparent
-        );
-        animation: neural-scan 2s linear infinite;
-    }
-}
-
-@mixin cyber-text($size, $color: 'synapse-blue') {
-    font-family: 'Orbitron', 'Rajdhani', sans-serif;
-    font-size: $size;
-    color: map-get($neural-colors, $color);
-    text-transform: uppercase;
-    letter-spacing: 2px;
-    position: relative;
-    text-shadow: 0 0 5px map-get($neural-colors, $color);
-}
-
-// Advanced Animations
-@keyframes neural-scan {
-    0% { transform: translateX(-100%); opacity: 0; }
-    50% { opacity: 1; }
-    100% { transform: translateX(100%); opacity: 0; }
-}
-
-@keyframes data-pulse {
-    0%, 100% { opacity: 0.8; transform: scale(1); }
-    50% { opacity: 1; transform: scale(1.02); }
-}
-
-// Base Styles
-body {
-    background-color: map-get($neural-colors, 'void-black');
-    background-image: 
-        linear-gradient(
-            rgba(map-get($neural-colors, 'synapse-blue'), 
-            map-get($neural-colors, 'grid-alpha')) 1px,
-            transparent 1px
-        ),
-        linear-gradient(
-            90deg,
-            rgba(map-get($neural-colors, 'synapse-blue'), 
-            map-get($neural-colors, 'grid-alpha')) 1px,
-            transparent 1px
-        );
-    background-size: 20px 20px;
-    color: map-get($neural-colors, 'neural-white');
-}
-
-// Advanced Components
-.neural-interface {
-    @include neural-container(2);
-    padding: 20px;
-    margin: 20px;
-    clip-path: polygon(
-        0 20px,
-        20px 0,
-        calc(100% - 20px) 0,
-        100% 20px,
-        100% calc(100% - 20px),
-        calc(100% - 20px) 100%,
-        20px 100%,
-        0 calc(100% - 20px)
-    );
-
-    &__header {
-        @include cyber-text(2rem);
-    text-align: center;
-        margin-bottom: 20px;
-        animation: neural-glitch 5s infinite;
-    }
-
-    &__content {
-    position: relative;
-        z-index: 1;
-    }
-}
-
-.data-display {
-    @include neural-container(1);
-    padding: 15px;
-    margin: 10px 0;
-    animation: data-pulse 4s infinite;
-
-    &__label {
-        @include cyber-text(0.9rem, 'data-yellow');
-        margin-bottom: 5px;
-    }
-
-    &__value {
-        @include cyber-text(1.2rem, 'matrix-green');
-    }
-}
-
-// Interactive Elements
-.neural-button {
-    @include neural-container(1);
-    padding: 10px 20px;
-    cursor: pointer;
-    transition: all 0.3s ease;
-    
-    &:hover {
-        transform: translateY(-2px) scale(1.02);
-        box-shadow: neural-glow('synapse-blue', 2);
-    }
-    
-    &:active {
-        transform: translateY(1px);
-    }
-}
-
-// Code Display
-.code-matrix {
-    @include neural-container(1);
-    font-family: 'Source Code Pro', monospace;
-    padding: 20px;
-    margin: 15px 0;
-    
-    &__line {
-    position: relative;
-        padding-left: 20px;
-        
-        &::before {
-            content: '>';
-            position: absolute;
-            left: 0;
-            color: map-get($neural-colors, 'matrix-green');
-        }
-    }
-}
-
-// Status Indicators
-.neural-status {
-    display: flex;
-    align-items: center;
-    gap: 10px;
-    
-    &__indicator {
-        width: 10px;
-        height: 10px;
-        border-radius: 50%;
-        background: map-get($neural-colors, 'matrix-green');
-        animation: data-pulse 2s infinite;
-    }
-    
-    &__text {
-        @include cyber-text(0.9rem, 'matrix-green');
-    }
-}
-
-// Advanced Grid Layout
-.neural-grid {
-    display: grid;
-    grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
-    gap: 20px;
-    padding: 20px;
-    
-    &__item {
-        @include neural-container(1);
-        padding: 15px;
-        transition: transform 0.3s ease;
-        
-        &:hover {
-            transform: translateZ(20px);
-            z-index: 2;
-        }
-    }
+# Custom CSS for Tech theme
+custom_css = """
+:root {
+    --primary-color: #4285F4;  /* Google Blue */
+    --secondary-color: #34A853;  /* Google Green */
+    --light-background: #F8F9FA;
+    --dark-text: #202124;
+    --white: #FFFFFF;
+    --border-color: #DADCE0;
+    --code-bg: #F1F3F4;
+    --code-text: #37474F;
+    --error-color: #EA4335;  /* Google Red */
+    --warning-color: #FBBC04;  /* Google Yellow */
 }
-"""
-
-# Compile SCSS to CSS
-def compile_scss():
-    try:
-        return sass.compile(string=CYBERPUNK_SCSS)
-    except sass.CompileError as e:
-        print(f"SCSS Compilation Error: {e}")
-        return ""
-
-# Advanced JavaScript for dynamic effects
-NEURAL_JS = """
-<script>
-class NeuralInterface {
-    constructor() {
-        this.initializeEffects();
-        this.setupEventListeners();
-    }
-
-    initializeEffects() {
-        this.setupGlitchEffects();
-        this.setupDataStreams();
-        this.setupHolographicEffects();
-    }
-
-    setupGlitchEffects() {
-        document.querySelectorAll('.neural-interface__header').forEach(element => {
-            setInterval(() => {
-                if (Math.random() < 0.1) {
-                    element.style.transform = `translate(${Math.random() * 4 - 2}px, ${Math.random() * 4 - 2}px)`;
-                    setTimeout(() => element.style.transform = 'none', 100);
-                }
-            }, 2000);
-        });
-    }
-
-    setupDataStreams() {
-        const canvas = document.createElement('canvas');
-        document.body.appendChild(canvas);
-        canvas.style.position = 'fixed';
-        canvas.style.top = '0';
-        canvas.style.left = '0';
-        canvas.style.width = '100%';
-        canvas.style.height = '100%';
-        canvas.style.pointerEvents = 'none';
-        canvas.style.zIndex = '1';
-        canvas.style.opacity = '0.1';
-
-        const ctx = canvas.getContext('2d');
-        const matrix = "ABCDEFGHIJKLMNOPQRSTUVWXYZ123456789@#$%^&*()*&^%";
-        const drops = [];
-
-        function initMatrix() {
-            canvas.width = window.innerWidth;
-            canvas.height = window.innerHeight;
-            const columns = canvas.width / 20;
-            for(let i = 0; i < columns; i++) drops[i] = 1;
-        }
-
-        function drawMatrix() {
-            ctx.fillStyle = 'rgba(0, 0, 0, 0.05)';
-            ctx.fillRect(0, 0, canvas.width, canvas.height);
-            ctx.fillStyle = '#0F0';
-            ctx.font = '15px monospace';
-            for(let i = 0; i < drops.length; i++) {
-                const text = matrix[Math.floor(Math.random() * matrix.length)];
-                ctx.fillText(text, i * 20, drops[i] * 20);
-                if(drops[i] * 20 > canvas.height && Math.random() > 0.975)
-                    drops[i] = 0;
-                drops[i]++;
-            }
-        }
-
-        window.addEventListener('resize', initMatrix);
-        initMatrix();
-        setInterval(drawMatrix, 50);
-    }
-
-    setupHolographicEffects() {
-        document.querySelectorAll('.neural-button').forEach(button => {
-            button.addEventListener('mousemove', e => {
-                const rect = button.getBoundingClientRect();
-                const x = e.clientX - rect.left;
-                const y = e.clientY - rect.top;
-                
-                button.style.setProperty('--x', `${x}px`);
-                button.style.setProperty('--y', `${y}px`);
-            });
-        });
-    }
-
-    setupEventListeners() {
-        document.addEventListener('click', e => {
-            if (e.target.closest('.neural-button')) {
-                this.createRippleEffect(e);
-            }
-        });
-    }
-
-    createRippleEffect(e) {
-        const button = e.target.closest('.neural-button');
-        const ripple = document.createElement('span');
-        ripple.classList.add('ripple');
-        button.appendChild(ripple);
-        
-        const rect = button.getBoundingClientRect();
-        const size = Math.max(rect.width, rect.height);
-        ripple.style.width = ripple.style.height = `${size}px`;
-        
-        const x = e.clientX - rect.left - size/2;
-        const y = e.clientY - rect.top - size/2;
-        ripple.style.left = `${x}px`;
-        ripple.style.top = `${y}px`;
-        
-        setTimeout(() => ripple.remove(), 600);
-    }
-}
-
-// Initialize Neural Interface
-document.addEventListener('DOMContentLoaded', () => {
-    new NeuralInterface();
-});
-</script>
+body { background-color: var(--light-background); font-family: 'Google Sans', 'Roboto', sans-serif; }
+.container { max-width: 1200px !important; margin: 0 auto !important; padding: 10px; }
+.header { background-color: var(--white); border-bottom: 1px solid var(--border-color); padding: 15px 0; margin-bottom: 20px; border-radius: 12px 12px 0 0; box-shadow: 0 1px 2px rgba(0,0,0,0.05); }
+.header-title { color: var(--primary-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: 8px !important; box-shadow: 0 1px 3px 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(--white) !important; border-radius: 18px 18px 4px 18px !important; padding: 12px 16px !important; margin-left: auto !important; max-width: 80% !important; }
+.message-bot { background-color: #F1F3F4 !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 1px 2px rgba(0,0,0,0.05) !important; }
+.send-btn { background-color: var(--primary-color) !important; border-radius: 24px !important; color: var(--white) !important; padding: 10px 20px !important; font-weight: 500 !important; }
+.clear-btn { background-color: #F1F3F4 !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: 8px !important; box-shadow: 0 1px 3px 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: 8px; box-shadow: 0 1px 2px rgba(0,0,0,0.05); }
+.stats-box { background-color: #E8F0FE; padding: 10px; border-radius: 8px; margin-top: 10px; }
+.tool-container { background-color: var(--white); border-radius: 8px; box-shadow: 0 1px 3px rgba(0,0,0,0.1); padding: 15px; margin-bottom: 20px; border: 1px solid var(--border-color); }
+.code-block { background-color: var(--code-bg); color: var(--code-text); padding: 12px; border-radius: 8px; font-family: 'Roboto Mono', monospace; overflow-x: auto; margin: 10px 0; border-left: 3px solid var(--primary-color); }
+.repo-card { border: 1px solid var(--border-color); padding: 15px; margin: 10px 0; border-radius: 8px; background-color: var(--white); }
+.repo-name { color: var(--primary-color); font-weight: bold; font-size: 1.1rem; margin-bottom: 5px; }
+.repo-description { color: var(--dark-text); font-size: 0.9rem; margin-bottom: 10px; }
+.repo-stats { display: flex; gap: 15px; color: #5F6368; font-size: 0.85rem; }
+.repo-stat { display: flex; align-items: center; gap: 5px; }
+.qa-card { border-left: 3px solid var(--secondary-color); padding: 10px 15px; margin: 15px 0; background-color: #F8F9FA; border-radius: 0 8px 8px 0; }
+.qa-title { font-weight: bold; color: var(--dark-text); margin-bottom: 5px; }
+.qa-body { color: var(--dark-text); font-size: 0.95rem; margin-bottom: 10px; }
+.qa-meta { display: flex; justify-content: space-between; color: #5F6368; font-size: 0.85rem; }
+.tag { background-color: #E8F0FE; color: var(--primary-color); padding: 4px 8px; border-radius: 4px; font-size: 0.8rem; margin-right: 5px; display: inline-block; }
 """
 
 # Function to process PDF files
@@ -447,147 +109,8 @@ 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}
 
-# New function to process Excel files
-def process_excel(excel_file):
-    if excel_file is None:
-        return None, "No file uploaded", {"data_preview": "", "total_sheets": 0, "total_rows": 0}
-    
-    try:
-        session_id = str(uuid.uuid4())
-        with tempfile.NamedTemporaryFile(suffix=".xlsx", delete=False) as temp_file:
-            temp_file.write(excel_file)
-            excel_path = temp_file.name
-        
-        # Read Excel file with pandas
-        excel_data = pd.ExcelFile(excel_path)
-        sheet_names = excel_data.sheet_names
-        all_texts = []
-        total_rows = 0
-        
-        # Process each sheet
-        for sheet in sheet_names:
-            df = pd.read_excel(excel_path, sheet_name=sheet)
-            total_rows += len(df)
-            
-            # Convert dataframe to text for vectorization
-            sheet_text = f"Sheet: {sheet}\n"
-            sheet_text += df.to_string(index=False)
-            all_texts.append(sheet_text)
-        
-        # Generate HTML preview of first sheet
-        first_df = pd.read_excel(excel_path, sheet_name=0)
-        preview_rows = min(10, len(first_df))
-        data_preview = first_df.head(preview_rows).to_html(classes="excel-preview-table", index=False)
-        
-        # Process for vectorstore
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-        chunks = text_splitter.create_documents(all_texts)
-        vectorstore = FAISS.from_documents(chunks, embeddings)
-        index_path = os.path.join(FAISS_INDEX_DIR, session_id)
-        vectorstore.save_local(index_path)
-        user_vectorstores[session_id] = vectorstore
-
-        os.unlink(excel_path)
-        excel_state = {"data_preview": data_preview, "total_sheets": len(sheet_names), "total_rows": total_rows}
-        return session_id, f"✅ Successfully processed {len(chunks)} text chunks from Excel file", excel_state
-    except Exception as e:
-        if "excel_path" in locals() and os.path.exists(excel_path):
-            os.unlink(excel_path)
-        return None, f"Error processing Excel file: {str(e)}", {"data_preview": "", "total_sheets": 0, "total_rows": 0}
-
-# Function to analyze image using SmolDocling
-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."
-
-# Function to handle different file types
-def process_file(file_data, file_type):
-    if file_data is None:
-        return None, "No file uploaded", None
-    
-    if file_type == "pdf":
-        return process_pdf(file_data)
-    elif file_type == "excel":
-        return process_excel(file_data)
-    elif file_type == "image":
-        # For image files, we'll just use them directly for analysis
-        # But we'll return a session ID to maintain consistency
-        session_id = str(uuid.uuid4())
-        return session_id, "✅ Image file ready for analysis", None
-    else:
-        return None, "Unsupported file type", None
-
-# Function for speech-to-text conversion
-def speech_to_text():
-    try:
-        r = sr.Recognizer()
-        with sr.Microphone() as source:
-            r.adjust_for_ambient_noise(source)
-            audio = r.listen(source)
-            text = r.recognize_google(audio)
-            return text
-    except sr.UnknownValueError:
-        return "Could not understand audio. Please try again."
-    except sr.RequestError as e:
-        return f"Error with speech recognition service: {e}"
-    except Exception as e:
-        return f"Error converting speech to text: {str(e)}"
-
-# Function for text-to-speech conversion
-def text_to_speech(text, history):
-    if not text or not history:
-        return None
-    
-    try:
-        # Get the last bot response
-        last_response = history[-1][1]
-        
-        # Convert text to speech
-        tts = pyttsx3.init()
-        tts.setProperty('rate', 150)
-        tts.setProperty('volume', 0.9)
-        tts.save_to_file(last_response, "temp_output.mp3")
-        tts.runAndWait()
-        
-        return "temp_output.mp3"
-    except Exception as e:
-        print(f"Error in text-to-speech: {e}")
-        return None
-
 # Function to generate chatbot responses with Tech theme
-def generate_response(message, session_id, model_name, history, web_search_enabled=True):
+def generate_response(message, session_id, model_name, history):
     if not message:
         return history
     try:
@@ -598,8 +121,8 @@ def generate_response(message, session_id, model_name, history, web_search_enabl
             if docs:
                 context = "\n\nRelevant information from uploaded PDF:\n" + "\n".join(f"- {doc.page_content}" for doc in docs)
         
-        # Check if it's a GitHub repo search and web search is enabled
-        if web_search_enabled and re.match(r'^/github\s+.+', message, re.IGNORECASE):
+        # Check if it's a GitHub repo search
+        if re.match(r'^/github\s+.+', message, re.IGNORECASE):
             query = re.sub(r'^/github\s+', '', message, flags=re.IGNORECASE)
             repo_results = search_github_repos(query)
             if repo_results:
@@ -616,8 +139,8 @@ def generate_response(message, session_id, model_name, history, web_search_enabl
                 history.append((message, "No GitHub repositories found for your query."))
                 return history
         
-        # Check if it's a Stack Overflow search and web search is enabled
-        if web_search_enabled and re.match(r'^/stack\s+.+', message, re.IGNORECASE):
+        # Check if it's a Stack Overflow search
+        if re.match(r'^/stack\s+.+', message, re.IGNORECASE):
             query = re.sub(r'^/stack\s+', '', message, flags=re.IGNORECASE)
             qa_results = search_stackoverflow(query)
             if qa_results:
@@ -910,402 +433,110 @@ def perform_stack_search(query, tag, sort_by):
     except Exception as e:
         return f"Error searching Stack Overflow: {str(e)}"
 
-def detect_language(file_extension):
-    """Map file extensions to programming languages"""
-    language_map = {
-        ".py": "Python",
-        ".js": "JavaScript",
-        ".java": "Java",
-        ".cpp": "C++",
-        ".c": "C",
-        ".cs": "C#",
-        ".php": "PHP",
-        ".rb": "Ruby",
-        ".go": "Go",
-        ".rs": "Rust",
-        ".swift": "Swift",
-        ".kt": "Kotlin",
-        ".ts": "TypeScript",
-        ".html": "HTML",
-        ".css": "CSS",
-        ".sql": "SQL",
-        ".r": "R",
-        ".m": "Objective-C/MATLAB",
-        ".h": "C/C++ Header",
-        ".hpp": "C++ Header",
-        ".jsx": "React JSX",
-        ".tsx": "React TSX",
-        ".vue": "Vue.js",
-        ".scala": "Scala",
-        ".pl": "Perl",
-        ".sh": "Shell Script",
-        ".bash": "Bash Script",
-        ".ps1": "PowerShell",
-        ".yaml": "YAML",
-        ".yml": "YAML",
-        ".json": "JSON",
-        ".xml": "XML",
-        ".toml": "TOML",
-        ".ini": "INI"
-    }
-    return language_map.get(file_extension.lower(), "Unknown")
-
-def analyze_code(code_file):
-    """Analyze code files and provide insights"""
-    if code_file is None:
-        return "No file uploaded. Please upload a code file to analyze."
-    
-    try:
-        # Get file extension
-        file_extension = os.path.splitext(code_file.name)[1]
-        language = detect_language(file_extension)
-        
-        # Read the file content
-        content = code_file.read().decode('utf-8', errors='ignore')
-        
-        # Basic code metrics
-        total_lines = len(content.splitlines())
-        blank_lines = len([line for line in content.splitlines() if not line.strip()])
-        code_lines = total_lines - blank_lines
-        
-        # Calculate complexity metrics
-        complexity_metrics = calculate_complexity(content, language)
-        
-        # Generate analysis using LLM
-        analysis_prompt = f"""Analyze this {language} code and provide insights about:
-1. Code structure and organization
-2. Potential improvements or best practices
-3. Security considerations
-4. Performance implications
-5. Maintainability factors
-
-Code metrics:
-- Total lines: {total_lines}
-- Code lines: {code_lines}
-- Blank lines: {blank_lines}
-{complexity_metrics}
-
-First 1000 characters of code:
-{content[:1000]}...
-"""
-        
-        completion = client.chat.completions.create(
-            model="llama3-70b-8192",
-            messages=[
-                {"role": "system", "content": "You are an expert code reviewer and technical architect."},
-                {"role": "user", "content": analysis_prompt}
-            ],
-            temperature=0.3,
-            max_tokens=1500
-        )
-        
-        # Format the analysis
-        analysis = f"""## Code Analysis Report
-
-**File Type:** {language}
-
-### Code Metrics
-- Total Lines: {total_lines}
-- Code Lines: {code_lines}
-- Blank Lines: {blank_lines}
-
-### Complexity Analysis
-{complexity_metrics}
-
-### Expert Analysis
-{completion.choices[0].message.content}
-
-### Recommendations
-1. Consider using a linter specific to {language}
-2. Review the security considerations mentioned above
-3. Consider automated testing to validate the code
-4. Document any complex algorithms or business logic
-"""
-        return analysis
-        
-    except Exception as e:
-        return f"Error analyzing code: {str(e)}\n\nPlease ensure the file is properly formatted and encoded."
-
-def calculate_complexity(content, language):
-    """Calculate various complexity metrics based on the language"""
-    try:
-        # Count function/method definitions
-        function_patterns = {
-            "Python": r"def\s+\w+\s*\(",
-            "JavaScript": r"function\s+\w+\s*\(|const\s+\w+\s*=\s*\([^)]*\)\s*=>",
-            "Java": r"(public|private|protected)?\s*\w+\s+\w+\s*\([^)]*\)\s*\{",
-            "C++": r"\w+\s+\w+\s*\([^)]*\)\s*\{",
-        }
-        
-        pattern = function_patterns.get(language, r"\w+\s+\w+\s*\([^)]*\)")
-        function_count = len(re.findall(pattern, content))
-        
-        # Calculate cyclomatic complexity (rough estimate)
-        decision_patterns = [
-            r"\bif\b",
-            r"\bwhile\b",
-            r"\bfor\b",
-            r"\bcase\b",
-            r"\bcatch\b",
-            r"\b&&\b",
-            r"\b\|\|\b"
-        ]
-        
-        decision_points = sum(len(re.findall(p, content)) for p in decision_patterns)
-        
-        # Estimate maintainability
-        avg_line_length = sum(len(line) for line in content.splitlines()) / len(content.splitlines()) if content.splitlines() else 0
-        
-        return f"""**Complexity Metrics:**
-- Estimated Function Count: {function_count}
-- Decision Points: {decision_points}
-- Average Line Length: {avg_line_length:.2f} characters
-- Cyclomatic Complexity Estimate: {decision_points + 1}
-"""
-    except Exception as e:
-        return f"Error calculating complexity: {str(e)}"
-
-def update_status_with_animation(status):
-    return f"""
-    <div class="status-message">
-        <div class="loading-container">
-            <div class="loading-bar"></div>
-        </div>
-        > {status}
-    </div>
-    """
-
-# Update the analysis results display
-def format_analysis_results(analysis):
-    return f"""
-    <div class="analysis-container">
-        <div class="analysis-header">> ANALYSIS COMPLETE</div>
-        {analysis}
-        <div class="loading-container">
-            <div class="loading-bar"></div>
-        </div>
-    </div>
-    """
-
-def format_code_metrics(metrics):
-    return f"""
-    <div class="metric-card">
-        <div style="color: var(--neon-yellow);">SYSTEM METRICS</div>
-        <div style="margin-top: 10px;">
-            {metrics}
-        </div>
-    </div>
-    """
-
-# Add cyberpunk UI sound effects
-def play_interface_sound(sound_type):
-    sounds = {
-        "hover": "hover.mp3",
-        "click": "click.mp3",
-        "success": "success.mp3",
-        "error": "error.mp3"
-    }
-    return gr.Audio(value=sounds.get(sound_type), autoplay=True, visible=False)
-
-# Create the Gradio interface with advanced cyberpunk styling
-def create_cyberpunk_interface():
-    css = compile_scss()
-    
-    with gr.Blocks(css=css, head=NEURAL_JS) as demo:
+# 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})
-    excel_state = gr.State({"data_preview": "", "total_sheets": 0, "total_rows": 0})
-    file_type = gr.State("none")
-    audio_status = gr.State("Ready")
-    
     gr.HTML("""
-        <div class="neural-interface">
-            <div class="neural-interface__header">TECH-VISION_v3.0</div>
-            <div class="neural-status">
-                <div class="neural-status__indicator"></div>
-                <div class="neural-status__text">SYSTEM ONLINE</div>
-        </div>
+    <div class="header">
+        <div class="header-title">Tech-Vision</div>
+        <div class="header-subtitle">Analyze technical documents with Groq's LLM API.</div>
     </div>
     """)
-        with gr.Row(elem_classes="neural-grid"):
+    with gr.Row(elem_classes="container"):
         with gr.Column(scale=1, min_width=300):
-            with gr.Tabs():
-                with gr.TabItem("[SYS:SCAN] Code Analysis"):
-                    gr.HTML("""
-                    <div class="upload-container">
-                        <div style="color: var(--neon-blue); margin-bottom: 10px;">
-                            > INITIATE CODE SCAN
-                        </div>
-                    """)
-                    code_file = gr.File(
-                        label="UPLOAD SOURCE CODE",
-                        file_types=[".py", ".js", ".java", ".cpp", ".c", ".cs", ".php", ".rb", 
-                                  ".go", ".rs", ".swift", ".kt", ".ts", ".html", ".css", 
-                                  ".sql", ".r", ".m", ".h", ".hpp", ".jsx", ".tsx", 
-                                  ".vue", ".scala", ".pl", ".sh", ".bash", ".ps1",
-                                  ".yaml", ".yml", ".json", ".xml", ".toml", ".ini"],
-                        type="binary"
-                    )
-                    gr.HTML("</div>")
-                    code_analyze_btn = gr.Button("INITIATE ANALYSIS", elem_classes="primary-btn")
-                
-                with gr.TabItem("PDF"):
-                    pdf_file = gr.File(label="Upload PDF Document", file_types=[".pdf"], type="binary")
-                    pdf_upload_button = gr.Button("Process PDF", variant="primary")
-                
-                with gr.TabItem("Excel"):
-                    excel_file = gr.File(label="Upload Excel File", file_types=[".xlsx", ".xls"], type="binary")
-                    excel_upload_button = gr.Button("Process Excel", variant="primary")
-                
-                with gr.TabItem("Image"):
-                    image_input = gr.File(
-                        label="Upload Image", 
-                        file_types=["image"],
-                        type="filepath"
-                    )
-                    analyze_btn = gr.Button("Analyze Image")
-            
-            file_status = gr.Markdown("No file uploaded yet")
-            
-            # Model selector
+            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"
             )
-        
+            
+            # Tech Tools Section
+            gr.Markdown("### Developer Tools", elem_classes="tool-title")
+            with gr.Group(elem_classes="tool-container"):
+                with gr.Tabs():
+                    with gr.TabItem("GitHub Search"):
+                        repo_query = gr.Textbox(label="Search Query", placeholder="Enter keywords to search for repositories")
+                        with gr.Row():
+                            language = gr.Dropdown(
+                                choices=["any", "JavaScript", "Python", "Java", "C++", "TypeScript", "Go", "Rust", "PHP", "C#"],
+                                value="any",
+                                label="Language"
+                            )
+                            min_stars = gr.Dropdown(
+                                choices=["0", "10", "50", "100", "1000", "10000"],
+                                value="0",
+                                label="Min Stars"
+                            )
+                        sort_by = gr.Dropdown(
+                            choices=["stars", "forks", "updated"],
+                            value="stars",
+                            label="Sort By"
+                        )
+                        repo_search_btn = gr.Button("Search Repositories")
+                    
+                    with gr.TabItem("Stack Overflow"):
+                        stack_query = gr.Textbox(label="Search Query", placeholder="Enter your technical question")
+                        with gr.Row():
+                            tag = gr.Dropdown(
+                                choices=["any", "python", "javascript", "java", "c++", "react", "node.js", "android", "ios", "sql"],
+                                value="any",
+                                label="Tag"
+                            )
+                            so_sort_by = gr.Dropdown(
+                                choices=["votes", "newest", "activity"],
+                                value="votes",
+                                label="Sort By"
+                            )
+                        so_search_btn = gr.Button("Search Stack Overflow")
+                    
+                    with gr.TabItem("Code Explainer"):
+                        code_input = gr.Textbox(
+                            label="Code to Explain", 
+                            placeholder="Paste your code here...",
+                            lines=10
+                        )
+                        explain_btn = gr.Button("Explain Code")
+                
         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")
-                        pdf_stats = gr.Markdown("No PDF uploaded yet", elem_classes="stats-box")
+                        stats_display = gr.Markdown("No PDF uploaded yet", elem_classes="stats-box")
                 
-                with gr.TabItem("Excel Viewer"):
-                    excel_preview = gr.HTML(label="Excel Preview", elem_classes="file-preview")
-                    excel_stats = gr.Markdown("No Excel file uploaded yet", elem_classes="stats-box")
+                with gr.TabItem("GitHub Results"):
+                    repo_results = gr.Markdown("Search for repositories to see results here")
                 
-                with gr.TabItem("Image Analysis"):
-                    image_preview = gr.Image(label="Image Preview", type="pil")
-                    image_analysis_results = gr.Markdown("Upload an image and click 'Analyze Image' to see analysis results")
-                
-                with gr.TabItem("Code Analysis Results"):
-                    analysis_results = gr.Markdown("Upload a code file and click 'Analyze Code' to see analysis results")
-                    with gr.Row():
-                        copy_btn = gr.Button("📋 Copy Analysis")
-                        export_btn = gr.Button("📥 Export Report")
-    
-    # Audio visualization elements
-    with gr.Row(elem_classes="container"):
-        with gr.Column():
-            audio_vis = gr.HTML("""
-            <div class="audio-visualization">
-                <div class="audio-bar" style="height: 5px;"></div>
-                <div class="audio-bar" style="height: 12px;"></div>
-                <div class="audio-bar" style="height: 18px;"></div>
-                <div class="audio-bar" style="height: 15px;"></div>
-                <div class="audio-bar" style="height: 10px;"></div>
-                <div class="audio-bar" style="height: 20px;"></div>
-                <div class="audio-bar" style="height: 14px;"></div>
-                <div class="audio-bar" style="height: 8px;"></div>
-            </div>
-            """, visible=False)
-            audio_status_display = gr.Markdown("", elem_classes="audio-status")
+                with gr.TabItem("Stack Overflow Results"):
+                    stack_results = gr.Markdown("Search for questions to see results here")
+                    
+                with gr.TabItem("Code Explanation"):
+                    code_explanation = gr.Markdown("Paste your code and click 'Explain Code' to see an explanation here")
     
-    # Chat interface
     with gr.Row(elem_classes="container"):
         with gr.Column(scale=2, min_width=600):
-            chatbot = gr.Chatbot(
-                height=400, 
-                show_copy_button=True, 
-                elem_classes="chat-container",
-                type="messages"  # Use the new messages format
-            )
+            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 document or click the microphone to speak...", 
-                    scale=5
-                )
-                voice_btn = gr.Button("🎤", elem_classes="voice-btn")
+                msg = gr.Textbox(show_label=False, placeholder="Ask about your document, type /github to search repos, or /stack to search Stack Overflow...", scale=5)
                 send_btn = gr.Button("Send", scale=1)
-                
-            with gr.Row(elem_classes="audio-controls"):
-                clear_btn = gr.Button("Clear Conversation")
-                speak_btn = gr.Button("🔊 Speak Response", elem_classes="speak-btn")
-                audio_player = gr.Audio(label="Response Audio", type="filepath", visible=False)
+            clear_btn = gr.Button("Clear Conversation")
     
-    # Event Handlers for PDF processing
-    pdf_upload_button.click(
-        lambda x: ("pdf", x),
-        inputs=[pdf_file],
-        outputs=[file_type, file_status]
-    ).then(
+    # Event Handlers
+    upload_button.click(
         process_pdf,
         inputs=[pdf_file],
-        outputs=[current_session_id, file_status, pdf_state]
+        outputs=[current_session_id, pdf_status, pdf_state]
     ).then(
         update_pdf_viewer,
         inputs=[pdf_state],
-        outputs=[page_slider, pdf_image, pdf_stats]
+        outputs=[page_slider, pdf_image, stats_display]
     )
     
-    # Event Handlers for Excel processing
-    def update_excel_preview(state):
-        if not state:
-            return "", "No Excel file uploaded yet"
-        preview = state.get("data_preview", "")
-        sheets = state.get("total_sheets", 0)
-        rows = state.get("total_rows", 0)
-        stats = f"**Excel Statistics:**\nSheets: {sheets}\nTotal Rows: {rows}"
-        return preview, stats
-    
-    excel_upload_button.click(
-        lambda x: ("excel", x),
-        inputs=[excel_file],
-        outputs=[file_type, file_status]
-    ).then(
-        process_excel,
-        inputs=[excel_file],
-        outputs=[current_session_id, file_status, excel_state]
-    ).then(
-        update_excel_preview,
-        inputs=[excel_state],
-        outputs=[excel_preview, excel_stats]
-    )
-    
-    # Event Handlers for Image Analysis
-    analyze_btn.click(
-        lambda x: ("image", x),
-        inputs=[image_input],
-        outputs=[file_type, file_status]
-    ).then(
-        analyze_image,
-        inputs=[image_input],
-        outputs=[image_analysis_results]
-    ).then(
-        lambda x: Image.open(x) if x else None,
-        inputs=[image_input],
-        outputs=[image_preview]
-    )
-    
-    # Event Handlers for Code Analysis
-    code_analyze_btn.click(
-        update_status_with_animation,
-        inputs=[],
-        outputs=[file_status]
-    ).then(
-        analyze_code,
-        inputs=[code_file],
-        outputs=[analysis_results]
-    ).then(
-        format_analysis_results,
-        inputs=[analysis_results],
-        outputs=[analysis_results]
-    )
-    
-    # Chat message handling
     msg.submit(
         generate_response,
         inputs=[msg, current_session_id, model_dropdown, chatbot],
@@ -1318,59 +549,44 @@ def create_cyberpunk_interface():
         outputs=[chatbot]
     ).then(lambda: "", None, [msg])
     
-    # Improved speech-to-text with visual feedback
-    voice_btn.click(
-        speech_to_text,
-        inputs=[audio_status],
-        outputs=[audio_status_display, audio_vis, msg]
-    )
-    
-    # Improved text-to-speech with visual feedback
-    speak_btn.click(
-        text_to_speech,
-        inputs=[audio_status, chatbot],
-        outputs=[audio_status_display, audio_vis, audio_player]
-    ).then(
-        lambda x: gr.update(visible=True) if x else gr.update(visible=False),
-        inputs=[audio_player],
-        outputs=[audio_player]
+    clear_btn.click(
+        lambda: ([], None, "No PDF uploaded yet", {"page_images": [], "total_pages": 0, "total_words": 0}, 0, None, "No PDF uploaded yet"),
+        None,
+        [chatbot, current_session_id, pdf_status, pdf_state, page_slider, pdf_image, stats_display]
     )
     
-    # Page navigation for PDF
     page_slider.change(
         update_image,
         inputs=[page_slider, pdf_state],
         outputs=[pdf_image]
     )
     
-    # Clear conversation and reset UI
-    clear_btn.click(
-        lambda: (
-            [], None, "No file uploaded yet", 
-            {"page_images": [], "total_pages": 0, "total_words": 0},
-            {"data_preview": "", "total_sheets": 0, "total_rows": 0},
-            "none", 0, None, "No PDF uploaded yet", "", 
-            "No Excel file uploaded yet", None, 
-            "Upload an image and click 'Analyze Image' to see results", None,
-            gr.update(visible=False), "Ready"
-        ),
-        None,
-        [chatbot, current_session_id, file_status, pdf_state, excel_state, 
-         file_type, page_slider, pdf_image, pdf_stats, excel_preview, 
-         excel_stats, image_preview, image_analysis_results, audio_player, 
-         audio_vis, audio_status_display]
+    # Tech tool handlers
+    repo_search_btn.click(
+        perform_repo_search,
+        inputs=[repo_query, language, sort_by, min_stars],
+        outputs=[repo_results]
+    )
+    
+    so_search_btn.click(
+        perform_stack_search,
+        inputs=[stack_query, tag, so_sort_by],
+        outputs=[stack_results]
+    )
+    
+    explain_btn.click(
+        explain_code,
+        inputs=[code_input],
+        outputs=[code_explanation]
     )
 
-    # Add footer with creator 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>
-    """)
-
-    return demo
+# 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 = create_cyberpunk_interface()
-    demo.launch() 
+    demo.launch()