Update app.py

app.py

@@ -1,21 +1,35 @@
-import gradio as gr
-import groq
+# Standard library imports
 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"))
@@ -29,47 +43,371 @@ if not os.path.exists(FAISS_INDEX_DIR):
 # Dictionary to store user-specific vectorstores
 user_vectorstores = {}
 
-# 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 */
+# 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;
+        }
+    }
 }
-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; }
+"""
+
+# 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>
 """
 
 # Function to process PDF files
@@ -109,8 +447,147 @@ 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):
+def generate_response(message, session_id, model_name, history, web_search_enabled=True):
     if not message:
         return history
     try:
@@ -121,8 +598,8 @@ def generate_response(message, session_id, model_name, history):
             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
-        if re.match(r'^/github\s+.+', message, re.IGNORECASE):
+        # 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):
             query = re.sub(r'^/github\s+', '', message, flags=re.IGNORECASE)
             repo_results = search_github_repos(query)
             if repo_results:
@@ -139,8 +616,8 @@ def generate_response(message, session_id, model_name, history):
                 history.append((message, "No GitHub repositories found for your query."))
                 return history
         
-        # Check if it's a Stack Overflow search
-        if re.match(r'^/stack\s+.+', message, re.IGNORECASE):
+        # 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):
             query = re.sub(r'^/stack\s+', '', message, flags=re.IGNORECASE)
             qa_results = search_stackoverflow(query)
             if qa_results:
@@ -433,110 +910,402 @@ def perform_stack_search(query, tag, sort_by):
     except Exception as e:
         return f"Error searching Stack Overflow: {str(e)}"
 
-# Gradio interface
-with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
+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:
     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="header">
-        <div class="header-title">Tech-Vision</div>
-        <div class="header-subtitle">Analyze technical documents with Groq's LLM API.</div>
+        <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>
     """)
-    with gr.Row(elem_classes="container"):
+        with gr.Row(elem_classes="neural-grid"):
         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")
+            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
             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")
-                        stats_display = gr.Markdown("No PDF uploaded yet", elem_classes="stats-box")
+                        pdf_stats = gr.Markdown("No PDF 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("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("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")
+                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")
     
+    # Chat interface
     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")
+            chatbot = gr.Chatbot(
+                height=400, 
+                show_copy_button=True, 
+                elem_classes="chat-container",
+                type="messages"  # Use the new messages format
+            )
             with gr.Row():
-                msg = gr.Textbox(show_label=False, placeholder="Ask about your document, type /github to search repos, or /stack to search Stack Overflow...", scale=5)
+                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")
                 send_btn = gr.Button("Send", scale=1)
-            clear_btn = gr.Button("Clear Conversation")
+                
+            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)
     
-    # Event Handlers
-    upload_button.click(
+    # Event Handlers for PDF processing
+    pdf_upload_button.click(
+        lambda x: ("pdf", x),
+        inputs=[pdf_file],
+        outputs=[file_type, file_status]
+    ).then(
         process_pdf,
         inputs=[pdf_file],
-        outputs=[current_session_id, pdf_status, pdf_state]
+        outputs=[current_session_id, file_status, pdf_state]
     ).then(
         update_pdf_viewer,
         inputs=[pdf_state],
-        outputs=[page_slider, pdf_image, stats_display]
+        outputs=[page_slider, pdf_image, pdf_stats]
     )
     
+    # 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],
@@ -549,44 +1318,59 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
         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,
-        [chatbot, current_session_id, pdf_status, pdf_state, page_slider, pdf_image, stats_display]
+    # 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]
+    )
+    
+    # Page navigation for PDF
     page_slider.change(
         update_image,
         inputs=[page_slider, pdf_state],
         outputs=[pdf_image]
     )
     
-    # 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]
+    # 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]
     )
 
-# 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>
-""")
+    # 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
 
 # Launch the app
 if __name__ == "__main__":
+    demo = create_cyberpunk_interface()
     demo.launch()