Update app.py

app.py

@@ -1,815 +1,155 @@
 import gradio as gr
 import requests
 import os
-import tempfile
-import asyncio
-import aiohttp
 from PyPDF2 import PdfReader
 from sentence_transformers import SentenceTransformer
 import numpy as np
 from sklearn.metrics.pairwise import cosine_similarity
-import logging
-from typing import List, Dict, Tuple, Optional
-import json
-from datetime import datetime
-import hashlib
-import pickle
-from pathlib import Path
 
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+# Constants
+CHUNK_SIZE = 300
+MODEL_NAME = "all-MiniLM-L6-v2"
+TOGETHER_API_KEY = os.getenv("TOGETHER_API_KEY")
+SERPER_API_KEY = os.getenv("SERPER_API_KEY")
+
+# Load sentence embedding model
+model = SentenceTransformer(MODEL_NAME)
+
+# Global state
+doc_chunks, doc_embeddings = [], []
+
+# --- Text Extraction from PDF ---
+def extract_pdf_text(file_obj):
+    """Extracts and joins text from all pages of a PDF."""
+    reader = PdfReader(file_obj)
+    return "\n".join([page.extract_text() for page in reader.pages if page.extract_text()])
+
+# --- Chunk Text ---
+def split_text(text, size=CHUNK_SIZE):
+    """Splits text into fixed-size word chunks."""
+    words = text.split()
+    return [" ".join(words[i:i + size]) for i in range(0, len(words), size)]
+
+# --- File Upload Handling ---
+def handle_file_upload(file):
+    """Processes the uploaded PDF and caches its embeddings."""
+    global doc_chunks, doc_embeddings
+    if not file:
+        return "⚠️ Please upload a file.", gr.update(visible=False)
 
-# Configuration
-class Config:
-    TOGETHER_API_KEY = os.environ.get("TOGETHER_API_KEY")
-    SERPER_API_KEY = os.environ.get("SERPER_API_KEY")
-    MODEL_NAME = "all-MiniLM-L6-v2"
-    CHUNK_SIZE = 400
-    CHUNK_OVERLAP = 50
-    MAX_TOKENS = 1024
-    TEMPERATURE = 0.7
-    TOP_K_CHUNKS = 5
-    CACHE_DIR = Path("./cache")
-    
-    def __init__(self):
-        self.CACHE_DIR.mkdir(exist_ok=True)
-
-config = Config()
-
-class DocumentProcessor:
-    """Advanced document processing with caching and optimization"""
-    
-    def __init__(self):
-        self.model = SentenceTransformer(config.MODEL_NAME)
-        self.doc_chunks = []
-        self.doc_embeddings = []
-        self.document_metadata = {}
-        
-    def extract_text_from_pdf(self, file_obj) -> str:
-        """Extract text from PDF with error handling"""
-        try:
-            reader = PdfReader(file_obj)
-            text_parts = []
-            
-            for page_num, page in enumerate(reader.pages):
-                page_text = page.extract_text()
-                if page_text.strip():
-                    text_parts.append(f"[Page {page_num + 1}] {page_text}")
-                    
-            full_text = "\n".join(text_parts)
-            logger.info(f"Extracted {len(full_text)} characters from PDF")
-            return full_text
-            
-        except Exception as e:
-            logger.error(f"PDF extraction error: {str(e)}")
-            raise ValueError(f"Failed to process PDF: {str(e)}")
-    
-    def create_intelligent_chunks(self, text: str) -> List[str]:
-        """Create overlapping chunks with sentence boundary awareness"""
-        sentences = text.split('. ')
-        chunks = []
-        current_chunk = ""
-        
-        for sentence in sentences:
-            test_chunk = current_chunk + sentence + ". "
-            
-            if len(test_chunk.split()) <= config.CHUNK_SIZE:
-                current_chunk = test_chunk
-            else:
-                if current_chunk:
-                    chunks.append(current_chunk.strip())
-                current_chunk = sentence + ". "
-        
-        if current_chunk:
-            chunks.append(current_chunk.strip())
-            
-        # Add overlap between chunks
-        overlapped_chunks = []
-        for i, chunk in enumerate(chunks):
-            overlapped_chunks.append(chunk)
-            
-            # Add overlapping chunk if not the last one
-            if i < len(chunks) - 1:
-                overlap_words = chunk.split()[-config.CHUNK_OVERLAP:]
-                next_words = chunks[i + 1].split()[:config.CHUNK_OVERLAP]
-                overlap_chunk = " ".join(overlap_words + next_words)
-                overlapped_chunks.append(overlap_chunk)
-        
-        return overlapped_chunks
-    
-    def generate_document_hash(self, file_obj) -> str:
-        """Generate hash for document caching"""
-        file_obj.seek(0)
-        content = file_obj.read()
-        file_obj.seek(0)
-        return hashlib.md5(content).hexdigest()
-    
-    def load_cached_embeddings(self, doc_hash: str) -> Optional[Tuple[List[str], np.ndarray]]:
-        """Load cached embeddings if available"""
-        cache_file = config.CACHE_DIR / f"{doc_hash}.pkl"
-        if cache_file.exists():
-            try:
-                with open(cache_file, 'rb') as f:
-                    return pickle.load(f)
-            except Exception as e:
-                logger.warning(f"Failed to load cache: {e}")
-        return None
-    
-    def save_embeddings_to_cache(self, doc_hash: str, chunks: List[str], embeddings: np.ndarray):
-        """Save embeddings to cache"""
-        cache_file = config.CACHE_DIR / f"{doc_hash}.pkl"
-        try:
-            with open(cache_file, 'wb') as f:
-                pickle.dump((chunks, embeddings), f)
-        except Exception as e:
-            logger.warning(f"Failed to save cache: {e}")
-    
-    def process_document(self, file_obj) -> Tuple[str, bool]:
-        """Process uploaded document with caching"""
-        try:
-            doc_hash = self.generate_document_hash(file_obj)
-            
-            # Try to load from cache first
-            cached_data = self.load_cached_embeddings(doc_hash)
-            if cached_data:
-                self.doc_chunks, self.doc_embeddings = cached_data
-                logger.info(f"Loaded {len(self.doc_chunks)} chunks from cache")
-                return f"✅ Successfully loaded {len(self.doc_chunks)} chunks from cache!", True
-            
-            # Process document
-            text = self.extract_text_from_pdf(file_obj)
-            self.doc_chunks = self.create_intelligent_chunks(text)
-            
-            # Generate embeddings
-            logger.info("Generating embeddings...")
-            self.doc_embeddings = self.model.encode(
-                self.doc_chunks, 
-                batch_size=32, 
-                show_progress_bar=True,
-                convert_to_numpy=True
-            )
-            
-            # Save to cache
-            self.save_embeddings_to_cache(doc_hash, self.doc_chunks, self.doc_embeddings)
-            
-            # Store metadata
-            self.document_metadata = {
-                'hash': doc_hash,
-                'chunks_count': len(self.doc_chunks),
-                'processed_at': datetime.now().isoformat(),
-                'total_characters': len(text)
-            }
-            
-            return f"✅ Successfully processed {len(self.doc_chunks)} chunks from your document!", True
-            
-        except Exception as e:
-            logger.error(f"Document processing error: {str(e)}")
-            return f"❌ Error processing document: {str(e)}", False
-    
-    def retrieve_relevant_chunks(self, query: str, top_k: int = None) -> Tuple[str, List[float]]:
-        """Retrieve most relevant chunks with similarity scores"""
-        if not self.doc_chunks:
-            return "", []
-            
-        top_k = top_k or config.TOP_K_CHUNKS
-        query_embedding = self.model.encode([query])
-        
-        similarities = cosine_similarity(query_embedding, self.doc_embeddings)[0]
-        top_indices = np.argsort(similarities)[::-1][:top_k]
-        
-        relevant_chunks = []
-        scores = []
-        
-        for idx in top_indices:
-            if similarities[idx] > 0.1:  # Minimum similarity threshold
-                relevant_chunks.append(self.doc_chunks[idx])
-                scores.append(similarities[idx])
-        
-        context = "\n\n---\n\n".join(relevant_chunks)
-        return context, scores
-
-class LLMService:
-    """Enhanced LLM service with multiple providers and error handling"""
-    
-    @staticmethod
-    async def call_together_ai_async(context: str, question: str, system_prompt: str = None) -> str:
-        """Async call to Together AI API"""
-        url = "https://api.together.xyz/v1/chat/completions"
-        headers = {
-            "Authorization": f"Bearer {config.TOGETHER_API_KEY}",
-            "Content-Type": "application/json"
-        }
-        
-        system_msg = system_prompt or """You are an intelligent AI assistant specializing in document analysis and web research. 
-        Provide comprehensive, accurate, and well-structured responses based on the given context. 
-        Use bullet points, numbered lists, and clear formatting when appropriate. 
-        If the context doesn't contain enough information, clearly state what's missing."""
-        
-        messages = [
-            {"role": "system", "content": system_msg},
-            {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {question}\n\nPlease provide a detailed and helpful response."}
-        ]
-        
-        data = {
-            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
-            "messages": messages,
-            "temperature": config.TEMPERATURE,
-            "max_tokens": config.MAX_TOKENS,
-            "top_p": 0.9,
-            "repetition_penalty": 1.1
-        }
-        
-        async with aiohttp.ClientSession() as session:
-            async with session.post(url, headers=headers, json=data) as response:
-                if response.status == 200:
-                    result = await response.json()
-                    return result["choices"][0]["message"]["content"]
-                else:
-                    raise Exception(f"API call failed with status {response.status}")
-    
-    @staticmethod
-    def call_together_ai_sync(context: str, question: str, system_prompt: str = None) -> str:
-        """Synchronous wrapper for Together AI API"""
-        try:
-            loop = asyncio.new_event_loop()
-            asyncio.set_event_loop(loop)
-            return loop.run_until_complete(
-                LLMService.call_together_ai_async(context, question, system_prompt)
-            )
-        except Exception as e:
-            logger.error(f"LLM API error: {str(e)}")
-            return f"❌ Sorry, I encountered an error while generating the response: {str(e)}"
-
-class WebSearchService:
-    """Enhanced web search with multiple sources and caching"""
-    
-    @staticmethod
-    def search_web(query: str, num_results: int = 5) -> str:
-        """Enhanced web search with better formatting"""
-        try:
-            url = "https://google.serper.dev/search"
-            headers = {"X-API-KEY": config.SERPER_API_KEY}
-            payload = {
-                "q": query,
-                "num": num_results,
-                "type": "search"
-            }
-            
-            response = requests.post(url, json=payload, headers=headers, timeout=10)
-            response.raise_for_status()
-            
-            data = response.json()
-            results = data.get("organic", [])
-            
-            if not results:
-                return "No search results found for your query."
-            
-            formatted_results = []
-            for i, result in enumerate(results[:num_results], 1):
-                title = result.get('title', 'No title')
-                link = result.get('link', '')
-                snippet = result.get('snippet', 'No description available')
-                
-                formatted_results.append(f"""
-**Result {i}: {title}**
-URL: {link}
-Summary: {snippet}
-""")
-            
-            return "\n".join(formatted_results)
-            
-        except Exception as e:
-            logger.error(f"Web search error: {str(e)}")
-            return f"❌ Search failed: {str(e)}"
-
-# Global instances
-doc_processor = DocumentProcessor()
-llm_service = LLMService()
-search_service = WebSearchService()
-
-# Enhanced UI Functions
-def process_uploaded_file(file):
-    """Process uploaded file with enhanced feedback"""
-    if file is None:
-        return "⚠️ No file selected", gr.update(visible=False), gr.update(visible=False)
-    
     try:
-        status, success = doc_processor.process_document(file)
-        
-        if success:
-            metadata = doc_processor.document_metadata
-            info_text = f"""📄 **Document Successfully Loaded**
-📊 Chunks: {metadata.get('chunks_count', 'N/A')}
-📝 Characters: {metadata.get('total_characters', 'N/A'):,}
-⏰ Processed: {metadata.get('processed_at', 'N/A')[:19]}
-🔍 Ready for questions!"""
-            
-            return status, gr.update(visible=True, value=info_text), gr.update(visible=True)
-        else:
-            return status, gr.update(visible=False), gr.update(visible=False)
-            
+        text = extract_pdf_text(file)
+        doc_chunks = split_text(text)
+        doc_embeddings = model.encode(doc_chunks)
+        return f"✅ Processed {len(doc_chunks)} chunks.", gr.update(visible=True, value=f"{len(doc_chunks)} chunks ready.")
     except Exception as e:
-        error_msg = f"❌ Processing Error: {str(e)}"
-        return error_msg, gr.update(visible=False), gr.update(visible=False)
-
-def answer_question(question: str, source: str, history: List[List[str]], use_advanced: bool = False):
-    """Enhanced question answering with better context and formatting"""
+        return f"❌ Failed to process file: {e}", gr.update(visible=False)
+
+# --- Semantic Retrieval ---
+def get_top_chunks(query, k=3):
+    """Finds top-k relevant chunks using cosine similarity."""
+    query_emb = model.encode([query])
+    sims = cosine_similarity(query_emb, doc_embeddings)[0]
+    indices = np.argsort(sims)[::-1][:k]
+    return "\n\n".join([doc_chunks[i] for i in indices])
+
+# --- Call LLM via Together API ---
+def call_together_ai(context, question):
+    """Calls Mixtral LLM from Together API."""
+    url = "https://api.together.xyz/v1/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {TOGETHER_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant answering from the given context."},
+            {"role": "user", "content": f"Context: {context}\n\nQuestion: {question}"}
+        ],
+        "temperature": 0.7,
+        "max_tokens": 512
+    }
+    res = requests.post(url, headers=headers, json=payload)
+    return res.json()["choices"][0]["message"]["content"]
+
+# --- Serper Web Search ---
+def fetch_web_snippets(query):
+    """Performs a web search via Serper API."""
+    url = "https://google.serper.dev/search"
+    headers = {"X-API-KEY": SERPER_API_KEY}
+    res = requests.post(url, json={"q": query}, headers=headers).json()
+    return "\n".join([
+        f"🔹 [{r['title']}]({r['link']})\n{r['snippet']}" for r in res.get("organic", [])[:3]
+    ])
+
+# --- Main Chat Logic ---
+def respond_to_query(question, source, history):
+    """Handles query processing and LLM interaction."""
     if not question.strip():
         return history, ""
-    
-    # Add user question to history
-    history = history + [[question, None]]
-    
+
+    history.append([question, None])
+
     try:
         if source == "🌐 Web Search":
-            context = search_service.search_web(question, num_results=5)
-            source_info = "🌐 **Source:** Real-time Web Search"
-            system_prompt = """You are a web research assistant. Analyze the search results and provide a comprehensive answer. 
-            Cite specific sources when possible and organize information clearly."""
-            
-        elif source == "📄 Uploaded Document":
-            if not doc_processor.doc_chunks:
-                answer = "❌ Please upload a PDF document first to use this feature."
+            context = fetch_web_snippets(question)
+            source_note = "🌐 Web Search"
+        elif source == "📄 Uploaded File":
+            if not doc_chunks:
+                answer = "⚠️ Please upload a PDF document first."
                 history[-1][1] = answer
                 return history, ""
-                
-            context, similarity_scores = doc_processor.retrieve_relevant_chunks(question)
-            source_info = f"📄 **Source:** Uploaded Document ({len(similarity_scores)} relevant sections found)"
-            system_prompt = """You are a document analysis assistant. Based on the provided document excerpts, 
-            give a detailed and accurate answer. If information is incomplete, clearly state what's missing."""
-            
+            context = get_top_chunks(question)
+            source_note = "📄 Uploaded Document"
         else:
-            answer = "❌ Please select a valid knowledge source."
-            history[-1][1] = answer
-            return history, ""
-        
-        if not context.strip():
-            answer = "❌ No relevant information found for your question."
-            history[-1][1] = answer
+            history[-1][1] = "❌ Invalid knowledge source selected."
             return history, ""
-        
-        # Generate response using LLM
-        llm_response = llm_service.call_together_ai_sync(context, question, system_prompt)
-        
-        # Format final answer
-        timestamp = datetime.now().strftime("%H:%M:%S")
-        formatted_answer = f"""{source_info}
-⏰ **Generated at:** {timestamp}
 
-{llm_response}
-
----
-💡 *Tip: Try asking follow-up questions for more details!*"""
-        
-        history[-1][1] = formatted_answer
+        answer = call_together_ai(context, question)
+        history[-1][1] = f"**{source_note}**\n\n{answer}"
         return history, ""
-        
     except Exception as e:
-        error_msg = f"""❌ **Error Occurred**
-🔍 **Details:** {str(e)}
-💡 **Suggestion:** Please check your API keys and try again.
-
-If the problem persists, try:
-- Rephrasing your question
-- Checking your internet connection
-- Ensuring API keys are properly configured"""
-        
-        history[-1][1] = error_msg
+        history[-1][1] = f"❌ Error: {e}"
         return history, ""
 
-def clear_chat():
-    """Clear chat history"""
-    return []
-
-def get_sample_questions(source):
-    """Provide sample questions based on source"""
-    if source == "🌐 Web Search":
-        return [
-            "What are the latest developments in AI technology?",
-            "Current weather in major cities",
-            "Recent news about renewable energy",
-            "What's trending in technology today?"
-        ]
-    else:
-        return [
-            "What is the main topic of this document?",
-            "Summarize the key points",
-            "What are the conclusions?",
-            "Explain the methodology used"
-        ]
-
-# Enhanced CSS with modern design
-enhanced_css = """
-/* Global Styles */
-.gradio-container {
-    max-width: 1400px !important;
-    margin: auto !important;
-    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-}
-
-/* Header Styles */
-.main-header {
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-    padding: 2rem;
-    border-radius: 20px;
-    margin-bottom: 2rem;
-    box-shadow: 0 10px 30px rgba(102, 126, 234, 0.3);
-}
-
-.header-title {
-    color: white;
-    font-size: 3rem;
-    font-weight: 800;
-    text-align: center;
-    margin-bottom: 0.5rem;
-    text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
-}
-
-.header-subtitle {
-    color: rgba(255,255,255,0.9);
-    font-size: 1.3rem;
-    text-align: center;
-    font-weight: 300;
-}
+# --- Clear Chat ---
+def clear_chat(): return []
 
-/* Card Styles */
-.control-card {
-    background: white;
-    border-radius: 15px;
-    padding: 1.5rem;
-    box-shadow: 0 5px 20px rgba(0,0,0,0.1);
-    border: 1px solid #e2e8f0;
-    margin-bottom: 1rem;
-}
-
-.chat-card {
-    background: white;
-    border-radius: 15px;
-    padding: 1.5rem;
-    box-shadow: 0 5px 20px rgba(0,0,0,0.1);
-    border: 1px solid #e2e8f0;
-    min-height: 600px;
-}
-
-/* Source Selection */
-.source-selector {
-    background: linear-gradient(135deg, #84fab0 0%, #8fd3f4 100%);
-    border-radius: 12px;
-    padding: 1rem;
-    margin: 1rem 0;
-}
-
-.source-selector label {
-    color: #2d3748 !important;
-    font-weight: 600 !important;
-    font-size: 1.1rem !important;
-}
-
-/* File Upload */
-.upload-zone {
-    background: linear-gradient(135deg, #ffecd2 0%, #fcb69f 100%);
-    border: 3px dashed #ff8a65;
-    border-radius: 15px;
-    padding: 2rem;
-    text-align: center;
-    transition: all 0.3s ease;
-    cursor: pointer;
-}
-
-.upload-zone:hover {
-    transform: translateY(-3px);
-    box-shadow: 0 8px 25px rgba(255, 138, 101, 0.3);
-    border-color: #ff7043;
-}
-
-/* Status Boxes */
-.status-success {
-    background: linear-gradient(135deg, #84fab0 0%, #8fd3f4 100%);
-    border: none;
-    border-radius: 12px;
-    padding: 1rem;
-    color: #2d3748;
-    font-weight: 500;
-}
-
-.status-info {
-    background: linear-gradient(135deg, #a8edea 0%, #fed6e3 100%);
-    border: none;
-    border-radius: 12px;
-    padding: 1rem;
-    color: #2d3748;
-    font-weight: 500;
-}
-
-/* Chat Interface */
-.chat-container {
-    background: #f8fafc;
-    border-radius: 12px;
-    border: 1px solid #e2e8f0;
-    min-height: 500px;
-}
-
-/* Input Styles */
-.question-input {
-    border-radius: 12px;
-    border: 2px solid #cbd5e0;
-    padding: 1rem;
-    font-size: 1rem;
-    transition: all 0.3s ease;
-}
-
-.question-input:focus {
-    border-color: #667eea;
-    box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1);
-}
-
-/* Button Styles */
-.btn-primary {
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-    border: none;
-    border-radius: 12px;
-    padding: 0.75rem 1.5rem;
-    font-weight: 600;
-    color: white;
-    transition: all 0.3s ease;
-}
-
-.btn-primary:hover {
-    transform: translateY(-2px);
-    box-shadow: 0 8px 25px rgba(102, 126, 234, 0.4);
-}
-
-.btn-secondary {
-    background: linear-gradient(135deg, #ffecd2 0%, #fcb69f 100%);
-    border: none;
-    border-radius: 12px;
-    padding: 0.75rem 1.5rem;
-    font-weight: 600;
-    color: #2d3748;
-    transition: all 0.3s ease;
-}
+# --- UI Design ---
+css = """
+.gradio-container { max-width: 1100px !important; margin: auto; }
+h1, h2, h3 { text-align: center; }
+"""
 
-.btn-secondary:hover {
-    transform: translateY(-2px);
-    box-shadow: 0 8px 25px rgba(252, 182, 159, 0.4);
-}
+with gr.Blocks(css=css, theme=gr.themes.Soft(), title="🔍 AI RAG Assistant") as demo:
 
-/* Advanced Settings */
-.advanced-panel {
-    background: linear-gradient(135deg, #e0c3fc 0%, #9bb5ff 100%);
-    border-radius: 12px;
-    padding: 1.5rem;
-    margin: 1rem 0;
-}
+    gr.HTML("<h1>🤖 AI Chat with RAG Capabilities</h1><h3>Ask questions from PDFs or real-time web search</h3>")
 
-/* Footer */
-.footer-info {
-    background: #2d3748;
-    color: white;
-    padding: 2rem;
-    border-radius: 15px;
-    text-align: center;
-    margin-top: 2rem;
-}
+    with gr.Row():
+        with gr.Column(scale=1):
+            source = gr.Radio(["🌐 Web Search", "📄 Uploaded File"], label="Knowledge Source", value="🌐 Web Search")
+            file = gr.File(label="Upload PDF", file_types=[".pdf"])
+            status = gr.Textbox(label="Status", interactive=False)
+            doc_info = gr.Textbox(label="Chunks Info", visible=False, interactive=False)
 
-/* Animations */
-@keyframes fadeIn {
-    from { opacity: 0; transform: translateY(20px); }
-    to { opacity: 1; transform: translateY(0); }
-}
+        with gr.Column(scale=2):
+            chatbot = gr.Chatbot(label="Chat", height=500)
+            query = gr.Textbox(placeholder="Type your question here...", lines=2)
+            with gr.Row():
+                send = gr.Button("Send")
+                clear = gr.Button("Clear")
 
-.animate-in {
-    animation: fadeIn 0.6s ease-out;
-}
+    with gr.Accordion("ℹ️ Info", open=False):
+        gr.Markdown("- Web Search fetches latest online results\n- PDF mode retrieves answers from your document\n- Uses Mixtral model from Together AI")
 
-/* Responsive Design */
-@media (max-width: 768px) {
-    .header-title {
-        font-size: 2rem;
-    }
-    
-    .header-subtitle {
-        font-size: 1rem;
-    }
-    
-    .control-card, .chat-card {
-        padding: 1rem;
-    }
-}
-"""
+    gr.HTML("<div style='text-align:center; font-size:0.9em; color:gray;'>Built with 💡 Gradio, Serper, and Together AI</div>")
 
-# Build Enhanced Gradio Interface
-def create_enhanced_interface():
-    with gr.Blocks(
-        css=enhanced_css, 
-        theme=gr.themes.Soft(
-            primary_hue="blue",
-            secondary_hue="purple",
-            neutral_hue="slate"
-        ), 
-        title="🤖 Advanced RAG Chatbot"
-    ) as demo:
-        
-        # Header Section
-        gr.HTML("""
-            <div class="main-header animate-in">
-                <div class="header-title">🤖 Advanced RAG Intelligence System</div>
-                <div class="header-subtitle">
-                    Next-generation AI assistant powered by advanced retrieval-augmented generation
-                </div>
-            </div>
-        """)
-        
-        with gr.Row():
-            # Left Panel - Controls
-            with gr.Column(scale=1, elem_classes=["control-card"]):
-                
-                # Knowledge Source Selection
-                gr.HTML("<h3 style='color: #4a5568; margin-bottom: 1rem;'>🎯 Knowledge Source</h3>")
-                source_choice = gr.Radio(
-                    ["🌐 Web Search", "📄 Uploaded Document"], 
-                    label="Select Your Information Source",
-                    value="🌐 Web Search",
-                    elem_classes=["source-selector"]
-                )
-                
-                # Document Upload Section  
-                gr.HTML("<h3 style='color: #4a5568; margin: 2rem 0 1rem 0;'>📁 Document Processing</h3>")
-                
-                file_input = gr.File(
-                    label="Upload PDF Document",
-                    file_types=[".pdf"],
-                    elem_classes=["upload-zone"]
-                )
-                
-                file_status = gr.Textbox(
-                    label="Processing Status",
-                    interactive=False,
-                    elem_classes=["status-success"],
-                    visible=True
-                )
-                
-                document_info = gr.Textbox(
-                    label="Document Information",
-                    interactive=False,
-                    elem_classes=["status-info"],
-                    visible=False,
-                    lines=6
-                )
-                
-                # Quick Actions
-                gr.HTML("<h3 style='color: #4a5568; margin: 2rem 0 1rem 0;'>⚡ Quick Actions</h3>")
-                
-                sample_questions_display = gr.HTML("""
-                    <div style='background: #f7fafc; padding: 1rem; border-radius: 8px; border-left: 4px solid #667eea;'>
-                        <strong>💡 Sample Questions for Web Search:</strong><br>
-                        • What are the latest AI breakthroughs?<br>
-                        • Current tech industry trends<br>
-                        • Recent scientific discoveries<br>
-                        • Today's market updates
-                    </div>
-                """)
-            
-            # Right Panel - Chat Interface
-            with gr.Column(scale=2, elem_classes=["chat-card"]):
-                gr.HTML("<h3 style='color: #4a5568; margin-bottom: 1rem;'>💬 Intelligent Conversation</h3>")
-                
-                chatbot = gr.Chatbot(
-                    label="AI Assistant",
-                    height=500,
-                    elem_classes=["chat-container"],
-                    bubble_full_width=False,
-                    show_label=False,
-                    avatar_images=("👤", "🤖")
-                )
-                
-                with gr.Row():
-                    question_input = gr.Textbox(
-                        label="Your Question",
-                        placeholder="Ask me anything... (Press Enter or click Send)",
-                        lines=2,
-                        scale=4,
-                        elem_classes=["question-input"]
-                    )
-                    
-                    with gr.Column(scale=1, min_width=120):
-                        send_btn = gr.Button(
-                            "🚀 Send", 
-                            variant="primary", 
-                            size="lg",
-                            elem_classes=["btn-primary"]
-                        )
-                        clear_btn = gr.Button(
-                            "🗑️ Clear", 
-                            variant="secondary", 
-                            size="lg",
-                            elem_classes=["btn-secondary"]
-                        )
-        
-        # Advanced Settings Panel
-        with gr.Accordion("⚙️ Advanced Configuration", open=False, elem_classes=["advanced-panel"]):
-            with gr.Row():
-                with gr.Column():
-                    gr.HTML("""
-                        <div style='background: white; padding: 1.5rem; border-radius: 12px; margin: 1rem 0;'>
-                            <h4>🔧 System Features</h4>
-                            <ul style='line-height: 1.8;'>
-                                <li><strong>🌐 Real-time Web Search:</strong> Live internet data retrieval</li>
-                                <li><strong>📄 Document Intelligence:</strong> Advanced PDF processing with semantic chunking</li>
-                                <li><strong>🧠 Neural Embeddings:</strong> Sentence-BERT powered similarity matching</li>
-                                <li><strong>⚡ Smart Caching:</strong> Optimized performance with intelligent storage</li>
-                            </ul>
-                        </div>
-                    """)
-                
-                with gr.Column():
-                    gr.HTML("""
-                        <div style='background: white; padding: 1.5rem; border-radius: 12px; margin: 1rem 0;'>
-                            <h4>🤖 AI Capabilities</h4>
-                            <ul style='line-height: 1.8;'>
-                                <li><strong>Language Model:</strong> Mixtral-8x7B-Instruct</li>
-                                <li><strong>Context Understanding:</strong> Advanced semantic retrieval</li>
-                                <li><strong>Multi-source Fusion:</strong> Combined web + document insights</li>
-                                <li><strong>Error Recovery:</strong> Robust fallback mechanisms</li>
-                            </ul>
-                        </div>
-                    """)
-        
-        # Footer with Credits
-        gr.HTML("""
-            <div class="footer-info">
-                <h4>🚀 Technical Architecture</h4>
-                <p>Built with cutting-edge AI technologies: Together AI • Serper API • Sentence Transformers • Advanced RAG Pipeline</p>
-                <p style='margin-top: 1rem; opacity: 0.8;'>
-                    💡 Engineered for optimal performance and user experience • 
-                    🔒 Secure and scalable architecture • 
-                    🎯 Production-ready implementation
-                </p>
-            </div>
-        """)
-        
-        # Event Handlers with Enhanced Logic
-        file_input.change(
-            fn=process_uploaded_file,
-            inputs=[file_input],
-            outputs=[file_status, document_info, gr.update()]
-        )
-        
-        question_input.submit(
-            fn=answer_question,
-            inputs=[question_input, source_choice, chatbot],
-            outputs=[chatbot, question_input]
-        )
-        
-        send_btn.click(
-            fn=answer_question,
-            inputs=[question_input, source_choice, chatbot],
-            outputs=[chatbot, question_input]
-        )
-        
-        clear_btn.click(
-            fn=clear_chat,
-            inputs=[],
-            outputs=[chatbot]
-        )
-        
-        # Dynamic sample questions update
-        def update_sample_questions(source):
-            if source == "🌐 Web Search":
-                return gr.HTML("""
-                    <div style='background: #f0fff4; padding: 1rem; border-radius: 8px; border-left: 4px solid #48bb78;'>
-                        <strong>💡 Sample Questions for Web Search:</strong><br>
-                        • What are the latest AI breakthroughs?<br>
-                        • Current cryptocurrency market trends<br>
-                        • Recent climate change developments<br>
-                        • Today's technology news
-                    </div>
-                """)
-            else:
-                return gr.HTML("""
-                    <div style='background: #fef5e7; padding: 1rem; border-radius: 8px; border-left: 4px solid #ed8936;'>
-                        <strong>💡 Sample Questions for Documents:</strong><br>
-                        • Summarize the main findings<br>
-                        • What methodology was used?<br>
-                        • List the key conclusions<br>
-                        • Explain the technical details
-                    </div>
-                """)
-        
-        source_choice.change(
-            fn=update_sample_questions,
-            inputs=[source_choice],
-            outputs=[sample_questions_display]
-        )
-    
-    return demo
+    # Bind events
+    file.change(handle_file_upload, inputs=file, outputs=[status, doc_info])
+    query.submit(respond_to_query, inputs=[query, source, chatbot], outputs=[chatbot, query])
+    send.click(respond_to_query, inputs=[query, source, chatbot], outputs=[chatbot, query])
+    clear.click(clear_chat, outputs=[chatbot])
 
-# Launch Application
 if __name__ == "__main__":
-    demo.launch(
-        share=True,
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True
-    )
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)