Update interface.py

interface.py

@@ -2,297 +2,107 @@ import gradio as gr
 import requests
 import time
 import os
-import json
-from typing import Optional, Tuple, List
 
+# Use localhost for HF Spaces since both services run in the same container
+API_BASE_URL = "http://localhost:8000"
 
-# ==================== API CONFIGURATION ====================
-
-# For Hugging Face Spaces, both Gradio and FastAPI run in same container
-API_BASE_URL = os.getenv("API_BASE_URL", "http://localhost:8000")
-MAX_RETRIES = 3
-RETRY_DELAY = 5
-
-# ==================== UTILITY FUNCTIONS ====================
-
-def check_api_health(max_attempts=3) -> Tuple[bool, dict]:
-    """Check API health with retry logic"""
-    for attempt in range(max_attempts):
-        try:
-            response = requests.get(f"{API_BASE_URL}/health_check", timeout=10)
-            if response.status_code == 200:
-                return True, response.json()
-        except Exception as e:
-            if attempt < max_attempts - 1:
-                time.sleep(2)
-    return False, {}
-
-def make_request_with_retry(method: str, endpoint: str, **kwargs) -> requests.Response:
-    """Make API request with retry logic"""
-    url = f"{API_BASE_URL}{endpoint}"
-    
-    for attempt in range(MAX_RETRIES):
-        try:
-            if method.upper() == "GET":
-                response = requests.get(url, **kwargs)
-            elif method.upper() == "POST":
-                response = requests.post(url, **kwargs)
-            elif method.upper() == "DELETE":
-                response = requests.delete(url, **kwargs)
-            else:
-                raise ValueError(f"Unsupported method: {method}")
-            
-            if response.status_code in [200, 201]:
-                return response
-            elif response.status_code == 500 and attempt < MAX_RETRIES - 1:
-                time.sleep(RETRY_DELAY)
-                continue
-            else:
-                response.raise_for_status()
-                
-        except requests.exceptions.ConnectionError:
-            if attempt < MAX_RETRIES - 1:
-                time.sleep(RETRY_DELAY)
-            else:
-                raise gr.Error("❌ Cannot connect to API. Please refresh and try again.", duration=10)
-        except requests.exceptions.Timeout:
-            if attempt < MAX_RETRIES - 1:
-                time.sleep(RETRY_DELAY)
-            else:
-                raise gr.Error("⏱️ Request timeout. Please try again.", duration=10)
-        except Exception as e:
-            if attempt == MAX_RETRIES - 1:
-                raise gr.Error(f"❌ Error: {str(e)}", duration=10)
-    
-    raise gr.Error("❌ Maximum retries exceeded. Please try again later.", duration=10)
-
-# ==================== DOCUMENT UPLOAD FUNCTIONS ====================
-
-def upload_single_document(file, collection_name: str, progress=gr.Progress()) -> Tuple[str, dict]:
-    """Upload single document with progress tracking"""
-    if not file:
-        raise gr.Error("❌ Please select a file to upload", duration=5)
-    
-    if not collection_name:
-        collection_name = "default"
-    
+def extract_links(url):
+    """Extract links from the given URL"""
+    endpoint = f"{API_BASE_URL}/extract_links"
+    payload = {"url": url}
     try:
-        progress(0.1, desc="📤 Uploading document...")
-        
-        # Handle file path (Gradio returns file path as string or file object)
-        file_path = file.name if hasattr(file, 'name') else file
-        
-        # Ensure file exists
-        if not os.path.exists(file_path):
-            raise gr.Error(f"❌ File not found: {file_path}", duration=5)
-        
-        with open(file_path, 'rb') as f:
-            files = {
-                'file': (os.path.basename(file_path), f, 'application/octet-stream')
-            }
-            params = {'collection_name': collection_name}
-            
-            progress(0.4, desc="🔄 Processing document...")
-            response = make_request_with_retry(
-                "POST",
-                "/upload_document",
-                files=files,
-                params=params,
-                timeout=180
-            )
-        
-        progress(1, desc="✅ Upload complete!")
-        
-        result = response.json()
-        status_msg = f"""✅ Successfully uploaded '{result['filename']}'
-📊 Created {result['chunks_created']} chunks
-🗂️ Collection: {collection_name}
-📁 File Type: {result['file_type']}"""
-        
-        return status_msg, result
-        
-    except gr.Error:
-        raise
-    except Exception as e:
-        raise gr.Error(f"❌ Upload failed: {str(e)}", duration=10)
+        response = requests.post(endpoint, json=payload, timeout=30)
+        if response.status_code == 200:
+            return response.json()["unique_links"]
+        else:
+            raise Exception(f"Failed to extract links: {response.text}")
+    except requests.exceptions.RequestException as e:
+        raise Exception(f"Connection error: {str(e)}")
 
-def upload_multiple_documents(files, collection_name: str, progress=gr.Progress()) -> Tuple[str, dict]:
-    """Upload multiple documents with progress tracking"""
-    if not files or len(files) == 0:
-        raise gr.Error("❌ Please select files to upload", duration=5)
-    
-    if not collection_name:
-        collection_name = "default"
-    
+def extract_text(urls):
+    """Extract text from URLs"""
+    endpoint = f"{API_BASE_URL}/extract_text"
     try:
-        progress(0.1, desc=f"📤 Uploading {len(files)} documents...")
-        
-        files_to_upload = []
-        for idx, file in enumerate(files):
-            file_path = file.name if hasattr(file, 'name') else file
-            
-            # Check if file exists
-            if not os.path.exists(file_path):
-                continue
-                
-            with open(file_path, 'rb') as f:
-                file_content = f.read()
-                files_to_upload.append(
-                    ('files', (os.path.basename(file_path), file_content, 'application/octet-stream'))
-                )
-            progress((idx + 1) / len(files) * 0.5, desc=f"Reading file {idx + 1}/{len(files)}...")
-        
-        if not files_to_upload:
-            raise gr.Error("❌ No valid files found to upload", duration=5)
-        
-        progress(0.5, desc="🔄 Processing all documents...")
-        params = {'collection_name': collection_name}
-        response = make_request_with_retry(
-            "POST",
-            "/upload_multiple_documents",
-            files=files_to_upload,
-            params=params,
-            timeout=300
-        )
-        
-        progress(1, desc="✅ All uploads complete!")
-        
-        result = response.json()
-        status_msg = f"""✅ Successfully uploaded {result['successful_uploads']} files
-❌ Failed: {result['failed_uploads']}
-🗂️ Collection: {collection_name}"""
-        
-        return status_msg, result
-        
-    except gr.Error:
-        raise
-    except Exception as e:
-        raise gr.Error(f"❌ Upload failed: {str(e)}", duration=10)
+        response = requests.post(endpoint, json=urls, timeout=60)
+        if response.status_code == 200:
+            return response.json()["file_saved"]
+        else:
+            raise Exception(f"Failed to extract text: {response.text}")
+    except requests.exceptions.RequestException as e:
+        raise Exception(f"Connection error: {str(e)}")
 
-def query_rag_documents(query: str, collection_name: str, top_k: int, progress=gr.Progress()) -> Tuple[str, str, dict]:
-    """Query documents with progress tracking"""
-    if not query:
-        raise gr.Error("❌ Please enter a query", duration=5)
-    
-    if not collection_name:
-        raise gr.Error("❌ Please select a collection", duration=5)
-    
+def perform_rag(file_path, prompt):
+    """Perform RAG on the extracted text"""
+    endpoint = f"{API_BASE_URL}/rag"
+    payload = {"file_path": file_path, "prompt": prompt}
     try:
-        progress(0.3, desc="🔍 Searching documents...")
-        
-        response = make_request_with_retry(
-            "POST",
-            "/query_documents",
-            json={
-                "query": query,
-                "collection_name": collection_name,
-                "top_k": top_k
-            },
-            timeout=60
-        )
-        
-        progress(0.8, desc="🤖 Generating answer...")
-        result = response.json()
-        
-        progress(1, desc="✅ Complete!")
-        
-        if "I couldn't find this information" in result['answer']:
-            status_msg = "⚠️ No relevant information found in documents"
+        response = requests.post(endpoint, json=payload, timeout=60)
+        if response.status_code == 200:
+            return response.json()
         else:
-            status_msg = f"✅ Found relevant information from {len(result['sources'])} sources"
-        
-        answer_text = f"**Query:** {result['query']}\n\n**Answer:** {result['answer']}"
-        
-        return status_msg, answer_text, result['sources']
-        
-    except gr.Error:
-        raise
-    except Exception as e:
-        raise gr.Error(f"❌ Query failed: {str(e)}", duration=10)
+            raise Exception(f"Failed to perform RAG: {response.text}")
+    except requests.exceptions.RequestException as e:
+        raise Exception(f"Connection error: {str(e)}")
 
-def list_all_collections() -> Tuple[str, dict, gr.Dropdown]:
-    """List all collections with error handling"""
+def check_api_health():
+    """Check if FastAPI is running"""
     try:
-        response = make_request_with_retry("GET", "/list_collections", timeout=10)
-        result = response.json()
-        collections = result['collections']
-        
-        if not collections:
-            return "📂 No collections found. Upload documents to create a collection.", None, gr.Dropdown(choices=["default"], value="default")
-        
-        summary = f"📊 **Total Collections:** {len(collections)}\n\n"
-        for col in collections:
-            summary += f"🗂️ **{col['collection_name']}**\n"
-            summary += f"   - Chunks: {col['total_chunks']}\n"
-            summary += f"   - Dimension: {col['dimension']}\n\n"
-        
-        collection_names = [col['collection_name'] for col in collections]
-        
-        return summary, result, gr.Dropdown(choices=collection_names, value=collection_names[0] if collection_names else "default")
-        
-    except Exception as e:
-        raise gr.Error(f"❌ Failed to list collections: {str(e)}", duration=10)
+        response = requests.get(f"{API_BASE_URL}/", timeout=5)
+        return response.status_code == 200
+    except:
+        return False
 
-def delete_collection(collection_name: str) -> Tuple[str, str, dict, gr.Dropdown]:
-    """Delete collection with confirmation"""
-    if not collection_name:
-        raise gr.Error("❌ Please select a collection to delete", duration=5)
+def process_multiple_links(url, prompt):
+    """Process multiple links from a webpage"""
+    if not url or not prompt:
+        return "❌ Error: Please provide both URL and prompt", "", ""
+    
+    if not check_api_health():
+        return "❌ Error: FastAPI service is not available. Please wait a moment and try again.", "", ""
     
     try:
-        response = make_request_with_retry(
-            "DELETE",
-            f"/delete_collection/{collection_name}",
-            timeout=10
-        )
-        
-        status = f"✅ Successfully deleted collection '{collection_name}'"
+        links = extract_links(url)
+        sample_links = links[:5]
+        file_path = extract_text(sample_links)
+        result = perform_rag(file_path, prompt)
         
-        # Refresh collections list
-        summary, result, dropdown = list_all_collections()
+        status_msg = f"✅ Processed {len(sample_links)} pages from {len(links)} total links found"
+        response_text = f"**Query:** {result['user_query']}\n\n**Response:** {result['assistant_response']}"
+        sources_text = result['sources']
         
-        return status, summary, result, dropdown
+        return status_msg, response_text, sources_text
         
     except Exception as e:
-        raise gr.Error(f"❌ Failed to delete collection: {str(e)}", duration=10)
+        return f"❌ Error: {str(e)}", "", ""
 
-def get_system_health() -> Tuple[str, dict]:
-    """Get system health information"""
+def process_homepage_only(url, prompt):
+    """Process homepage content only"""
+    if not url or not prompt:
+        return "❌ Error: Please provide both URL and prompt", "", ""
+    
+    if not check_api_health():
+        return "❌ Error: FastAPI service is not available. Please wait a moment and try again.", "", ""
+    
     try:
-        is_healthy, health_data = check_api_health()
+        file_path = extract_text([url])
+        result = perform_rag(file_path, prompt)
         
-        if not is_healthy:
-            raise gr.Error("❌ System is offline. Please refresh the page.", duration=None)
+        status_msg = "✅ Processed homepage content"
+        response_text = f"**Query:** {result['user_query']}\n\n**Response:** {result['assistant_response']}"
+        sources_text = result['sources']
         
-        health_summary = f"""🟢 **System Status: Healthy**
-
-📊 **Configuration:**
-- Supabase: {'✅ Configured' if health_data.get('supabase_configured') else '❌ Not Configured'}
-- Groq API: {'✅ Configured' if health_data.get('groq_configured') else '❌ Not Configured'}
-- Embedding Model: {health_data.get('embedding_model', 'N/A')}
-
-📁 **Vector Stores:**
-- Total Collections: {health_data.get('vector_stores', 0)}
-- Total Chunks: {health_data.get('total_chunks', 0)}
-- Storage Path: {health_data.get('persistent_storage', 'N/A')}
-
-📚 **Available Collections:**
-{', '.join(health_data.get('collections', [])) if health_data.get('collections') else 'None'}
-"""
-        return health_summary, health_data
+        return status_msg, response_text, sources_text
         
-    except gr.Error:
-        raise
     except Exception as e:
-        raise gr.Error(f"❌ Health check failed: {str(e)}", duration=10)
-
-# ==================== GRADIO UI ====================
+        return f"❌ Error: {str(e)}", "", ""
 
-# Custom CSS
+# Dark theme custom CSS with animations
 custom_css = """
 @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700&display=swap');
 * {
     font-family: 'Inter', sans-serif !important;
 }
+/* Dark animated gradient background */
 .gradio-container {
     background: linear-gradient(-45deg, #0f0c29, #302b63, #24243e, #1a1a2e);
     background-size: 400% 400%;
@@ -303,6 +113,7 @@ custom_css = """
     50% { background-position: 100% 50%; }
     100% { background-position: 0% 50%; }
 }
+/* Main container with dark glassmorphism */
 .main-container {
     backdrop-filter: blur(20px);
     background: rgba(20, 20, 30, 0.85);
@@ -313,9 +124,16 @@ custom_css = """
     animation: fadeInUp 0.8s ease;
 }
 @keyframes fadeInUp {
-    from { opacity: 0; transform: translateY(30px); }
-    to { opacity: 1; transform: translateY(0); }
+    from {
+        opacity: 0;
+        transform: translateY(30px);
+    }
+    to {
+        opacity: 1;
+        transform: translateY(0);
+    }
 }
+/* Animated title with neon glow */
 .animated-title {
     background: linear-gradient(135deg, #00f2fe 0%, #4facfe 50%, #00c6ff 100%);
     background-size: 200% 200%;
@@ -334,6 +152,7 @@ custom_css = """
     50% { background-position: 100% 50%; }
     100% { background-position: 0% 50%; }
 }
+/* Floating animation for icons */
 .floating-icon {
     animation: float 3s ease-in-out infinite;
     display: inline-block;
@@ -342,6 +161,7 @@ custom_css = """
     0%, 100% { transform: translateY(0px); }
     50% { transform: translateY(-10px); }
 }
+/* Dark input fields with neon borders */
 textarea, input[type="text"] {
     font-size: 1.1rem !important;
     border-radius: 12px !important;
@@ -356,11 +176,14 @@ textarea:focus, input[type="text"]:focus {
     transform: translateY(-2px);
     background: rgba(35, 35, 50, 0.95) !important;
 }
+/* Dark labels */
 label {
     font-weight: 600 !important;
     color: #b0b0b0 !important;
     font-size: 1.1rem !important;
+    transition: all 0.3s ease;
 }
+/* Neon buttons with hover effects */
 .gr-button {
     background: linear-gradient(135deg, #00f2fe 0%, #4facfe 100%) !important;
     color: #0a0a0f !important;
@@ -375,224 +198,346 @@ label {
 .gr-button:hover {
     transform: translateY(-3px) !important;
     box-shadow: 0 6px 25px rgba(0, 242, 254, 0.6) !important;
+    filter: brightness(1.1);
 }
+.gr-button:active {
+    transform: translateY(-1px) !important;
+}
+/* Dark output boxes with glassmorphism */
 .output-box {
     background: rgba(30, 30, 45, 0.95) !important;
     border-radius: 16px !important;
     border: 1px solid rgba(0, 242, 254, 0.2) !important;
     backdrop-filter: blur(10px);
+    animation: slideIn 0.5s ease;
     box-shadow: 0 4px 15px rgba(0, 0, 0, 0.3) !important;
     color: #e0e0e0 !important;
     padding: 1.5rem !important;
-    min-height: 150px !important;
+    min-height: 200px !important;
+}
+/* Markdown container styling */
+.output-box .prose, .output-box .markdown {
+    font-size: 1.2rem !important;
+    line-height: 1.8 !important;
+}
+/* Markdown styling */
+.output-box h1 {
+    color: #00f2fe !important;
+    margin-top: 1.5rem !important;
+    margin-bottom: 1rem !important;
+    font-size: 2rem !important;
+}
+.output-box h2 {
+    color: #00f2fe !important;
+    margin-top: 1.5rem !important;
+    margin-bottom: 0.75rem !important;
+    font-size: 1.6rem !important;
+}
+.output-box h3 {
+    color: #4facfe !important;
+    margin-top: 1rem !important;
+    margin-bottom: 0.5rem !important;
+    font-size: 1.3rem !important;
+}
+.output-box p {
+    color: #e0e0e0 !important;
+    line-height: 1.9 !important;
+    margin-bottom: 1.2rem !important;
+    font-size: 1.15rem !important;
 }
 .output-box strong {
     color: #4facfe !important;
     font-weight: 600 !important;
+    font-size: 1.2rem !important;
+}
+.output-box code {
+    background: rgba(0, 242, 254, 0.15) !important;
+    padding: 3px 8px !important;
+    border-radius: 6px !important;
+    color: #00f2fe !important;
+    font-size: 1.1rem !important;
+    border: 1px solid rgba(0, 242, 254, 0.3) !important;
+}
+.output-box ul, .output-box ol {
+    font-size: 1.15rem !important;
+    line-height: 1.8 !important;
+    margin-left: 1.5rem !important;
+    color: #e0e0e0 !important;
+}
+.output-box li {
+    margin-bottom: 0.5rem !important;
+}
+/* Enhanced response area glow effect */
+.output-box:hover {
+    border-color: rgba(0, 242, 254, 0.4) !important;
+    box-shadow: 0 6px 25px rgba(0, 242, 254, 0.3) !important;
+    transition: all 0.3s ease !important;
+}
+/* JSON viewer styling */
+.output-box pre {
+    background: rgba(20, 20, 30, 0.9) !important;
+    border-radius: 10px !important;
+    padding: 1.5rem !important;
+    overflow-x: auto !important;
+    border: 1px solid rgba(0, 242, 254, 0.3) !important;
+    font-size: 1.05rem !important;
+    line-height: 1.6 !important;
+}
+.output-box .json-holder {
+    background: rgba(20, 20, 30, 0.9) !important;
+    border-radius: 10px !important;
+    padding: 1.5rem !important;
+    font-size: 1.05rem !important;
+}
+.output-box .json-key {
+    color: #00f2fe !important;
+    font-weight: 600 !important;
+}
+.output-box .json-string {
+    color: #90ee90 !important;
+}
+.output-box .json-number {
+    color: #ffa07a !important;
+}
+/* Special styling for AI response area */
+div[data-testid="markdown"] {
+    background: rgba(35, 35, 50, 0.6) !important;
+    padding: 1.5rem !important;
+    border-radius: 12px !important;
+    border: 1px solid rgba(0, 242, 254, 0.15) !important;
+}
+@keyframes slideIn {
+    from {
+        opacity: 0;
+        transform: translateX(-20px);
+    }
+    to {
+        opacity: 1;
+        transform: translateX(0);
+    }
+}
+/* Dark tab styling */
+.tab-nav button {
+    border-radius: 12px !important;
+    font-weight: 600 !important;
+    transition: all 0.3s ease !important;
+    font-size: 1.05rem !important;
+    background: rgba(30, 30, 45, 0.6) !important;
+    color: #b0b0b0 !important;
+    border: 1px solid rgba(0, 242, 254, 0.2) !important;
+}
+.tab-nav button:hover {
+    background: rgba(40, 40, 55, 0.8) !important;
+    border-color: rgba(0, 242, 254, 0.4) !important;
+}
+.tab-nav button[aria-selected="true"] {
+    background: linear-gradient(135deg, #00f2fe 0%, #4facfe 100%) !important;
+    color: #0a0a0f !important;
+    box-shadow: 0 4px 15px rgba(0, 242, 254, 0.4) !important;
+    border: none !important;
+}
+/* Info cards with pulse animation */
+.info-card {
+    animation: pulse 2s ease-in-out infinite;
+}
+@keyframes pulse {
+    0%, 100% { transform: scale(1); }
+    50% { transform: scale(1.02); }
+}
+/* Dark example box styling */
+.example-box {
+    background: linear-gradient(135deg, rgba(0, 242, 254, 0.1) 0%, rgba(79, 172, 254, 0.1) 100%);
+    border-radius: 16px;
+    padding: 1.5rem;
+    border-left: 4px solid #00f2fe;
+    margin-top: 2rem;
+    transition: all 0.3s ease;
+    animation: fadeIn 1s ease;
+}
+.example-box:hover {
+    transform: translateX(5px);
+    box-shadow: 0 5px 20px rgba(0, 242, 254, 0.2);
+}
+.example-box h3, .example-box p {
+    color: #e0e0e0 !important;
+}
+@keyframes fadeIn {
+    from { opacity: 0; }
+    to { opacity: 1; }
+}
+/* Dark note box with shimmer effect */
+.note-box {
+    background: linear-gradient(135deg, rgba(0, 242, 254, 0.08) 0%, rgba(79, 172, 254, 0.08) 100%);
+    border-radius: 12px;
+    padding: 1rem;
+    border-left: 4px solid #00f2fe;
+    margin-top: 1rem;
+    position: relative;
+    overflow: hidden;
+}
+.note-box::before {
+    content: '';
+    position: absolute;
+    top: 0;
+    left: -100%;
+    width: 100%;
+    height: 100%;
+    background: linear-gradient(90deg, transparent, rgba(0, 242, 254, 0.2), transparent);
+    animation: shimmer 3s infinite;
+}
+.note-box p {
+    color: #00c6ff !important;
+    position: relative;
+    z-index: 1;
+}
+@keyframes shimmer {
+    0% { left: -100%; }
+    100% { left: 100%; }
+}
+/* Dark subtitle animation */
+.subtitle {
+    color: #b0b0b0;
+    font-size: 1.3rem;
+    text-align: center;
+    margin-bottom: 2rem;
+    animation: fadeInDown 1s ease;
+}
+@keyframes fadeInDown {
+    from {
+        opacity: 0;
+        transform: translateY(-20px);
+    }
+    to {
+        opacity: 1;
+        transform: translateY(0);
+    }
+}
+/* Loading spinner styles */
+.loading {
+    display: inline-block;
+    width: 20px;
+    height: 20px;
+    border: 3px solid rgba(0, 242, 254, 0.3);
+    border-radius: 50%;
+    border-top-color: #00f2fe;
+    animation: spin 1s ease-in-out infinite;
+}
+@keyframes spin {
+    to { transform: rotate(360deg); }
+}
+/* Neon glow effect on containers */
+.main-container::before {
+    content: '';
+    position: absolute;
+    top: -2px;
+    left: -2px;
+    right: -2px;
+    bottom: -2px;
+    background: linear-gradient(45deg, #00f2fe, #4facfe, #00f2fe);
+    border-radius: 24px;
+    z-index: -1;
+    opacity: 0.3;
+    filter: blur(10px);
+    animation: neonPulse 3s ease-in-out infinite;
+}
+@keyframes neonPulse {
+    0%, 100% { opacity: 0.3; }
+    50% { opacity: 0.5; }
 }
 """
 
-# Build interface
-with gr.Blocks(css=custom_css, theme=gr.themes.Soft(), title="RAG Document Assistant") as interface:
+# Main interface with tabs
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as interface:
     gr.HTML("""
         <div class="main-container">
             <h1 class="animated-title">
-                <span class="floating-icon">📄</span> RAG Document Assistant
+                <span class="floating-icon">🌐</span> Web RAG System
             </h1>
-            <p class="subtitle" style="color: #b0b0b0; font-size: 1.3rem; text-align: center; margin-bottom: 2rem;">
-                Upload documents (PDF/Markdown/TXT) and ask questions using AI-powered retrieval
+            <p class="subtitle">
+                Extract content from web pages and ask questions using AI-powered retrieval
             </p>
         </div>
     """)
     
     with gr.Tabs() as tabs:
-        # Document Upload Tab
-        with gr.Tab("📄 Document Upload & Query"):
-            gr.Markdown("""
-            ### Upload PDF or Markdown documents and query them using RAG
-            - Supports **PDF**, **Markdown**, and **TXT** files
-            - Documents are chunked and stored in FAISS vector database
-            - Organize documents into collections for better management
-            """)
-            
+        with gr.Tab("🔗 Multiple Links Analysis"):
             with gr.Row():
                 with gr.Column():
-                    gr.Markdown("#### 📤 Upload Documents")
-                    collection_name_upload = gr.Textbox(
-                        label="🗂️ Collection Name",
-                        placeholder="default",
-                        value="default"
-                    )
-                    
-                    with gr.Tab("Single File"):
-                        file_upload_single = gr.File(
-                            label="📁 Select Document (PDF/Markdown/TXT)",
-                            file_types=[".pdf", ".md", ".txt"]
-                        )
-                        upload_btn_single = gr.Button("📤 Upload Single Document", variant="primary")
-                        upload_status_single = gr.Textbox(label="📊 Upload Status", elem_classes="output-box")
-                        upload_result_single = gr.JSON(label="📋 Upload Details", elem_classes="output-box")
-                    
-                    with gr.Tab("Multiple Files"):
-                        file_upload_multi = gr.File(
-                            label="📁 Select Documents (PDF/Markdown/TXT)",
-                            file_count="multiple",
-                            file_types=[".pdf", ".md", ".txt"]
-                        )
-                        upload_btn_multi_doc = gr.Button("📤 Upload Multiple Documents", variant="primary")
-                        upload_status_multi = gr.Textbox(label="📊 Upload Status", elem_classes="output-box")
-                        upload_result_multi = gr.JSON(label="📋 Upload Details", elem_classes="output-box")
-                
-                with gr.Column():
-                    gr.Markdown("#### 🔍 Query Documents")
-                    
-                    refresh_btn = gr.Button("🔄 Refresh Collections", variant="secondary")
-                    
-                    collection_dropdown = gr.Dropdown(
-                        label="🗂️ Select Collection",
-                        choices=["default"],
-                        value="default"
+                    url_input_multi = gr.Textbox(
+                        label="🌍 Website URL", 
+                        placeholder="https://example.com",
+                        elem_classes="output-box"
                     )
-                    
-                    query_input = gr.Textbox(
-                        label="💭 Your Question",
-                        placeholder="Ask a question about your documents...",
-                        lines=3
+                    prompt_input_multi = gr.Textbox(
+                        label="💭 Your Question", 
+                        placeholder="What is this website about?", 
+                        lines=3,
+                        elem_classes="output-box"
                     )
-                    
-                    top_k_slider = gr.Slider(
-                        minimum=1,
-                        maximum=10,
-                        value=3,
-                        step=1,
-                        label="📊 Number of Sources (top-k)"
-                    )
-                    
-                    query_btn = gr.Button("🔍 Search Documents", variant="primary")
-                    
-                    query_status = gr.Textbox(label="📊 Query Status", elem_classes="output-box")
-                    query_response = gr.Markdown(label="🤖 AI Answer", elem_classes="output-box")
-                    query_sources = gr.JSON(label="📚 Source Citations", elem_classes="output-box")
-            
-            # Connect buttons
-            upload_btn_single.click(
-                fn=upload_single_document,
-                inputs=[file_upload_single, collection_name_upload],
-                outputs=[upload_status_single, upload_result_single]
-            )
-            
-            upload_btn_multi_doc.click(
-                fn=upload_multiple_documents,
-                inputs=[file_upload_multi, collection_name_upload],
-                outputs=[upload_status_multi, upload_result_multi]
-            )
-            
-            query_btn.click(
-                fn=query_rag_documents,
-                inputs=[query_input, collection_dropdown, top_k_slider],
-                outputs=[query_status, query_response, query_sources]
-            )
-            
-            def refresh_collections():
-                _, _, dropdown = list_all_collections()
-                return dropdown
-            
-            refresh_btn.click(
-                fn=refresh_collections,
-                outputs=[collection_dropdown]
-            )
-            
-            gr.HTML("""
-                <div class="example-box" style="background: linear-gradient(135deg, rgba(0, 242, 254, 0.1) 0%, rgba(79, 172, 254, 0.1) 100%); border-radius: 16px; padding: 1.5rem; border-left: 4px solid #00f2fe; margin-top: 2rem;">
-                    <h3 style="margin-top: 0; font-size: 1.4rem; color: #e0e0e0;">
-                        <span class="floating-icon">💡</span> Example Usage
-                    </h3>
-                    <p style="font-size: 1.1rem; color: #e0e0e0;"><strong>1.</strong> Upload your PDF/Markdown documents to a collection</p>
-                    <p style="font-size: 1.1rem; color: #e0e0e0;"><strong>2.</strong> Ask questions like: "What are the main findings?" or "Summarize the methodology"</p>
-                    <p style="font-size: 1.1rem; color: #e0e0e0;"><strong>3.</strong> System returns answers with source citations</p>
-                </div>
-            """)
-        
-        # Collection Management Tab
-        with gr.Tab("🗂️ Collection Management"):
-            gr.Markdown("### Manage Your Document Collections")
+                    submit_btn_multi = gr.Button("✨ Analyze Multiple Links", variant="primary")
             
             with gr.Row():
                 with gr.Column():
-                    list_btn = gr.Button("📋 List All Collections", variant="primary")
-                    collections_output = gr.Markdown(label="📊 Collections Overview", elem_classes="output-box")
-                    collections_json = gr.JSON(label="📋 Detailed Information", elem_classes="output-box")
-                
-                with gr.Column():
-                    gr.Markdown("#### 🗑️ Delete Collection")
-                    collection_to_delete = gr.Dropdown(label="🗂️ Select Collection to Delete", choices=["default"])
-                    delete_btn = gr.Button("🗑️ Delete Collection", variant="stop")
-                    delete_status = gr.Textbox(label="📊 Status", elem_classes="output-box")
+                    status_output_multi = gr.Textbox(label="📊 Status", elem_classes="output-box")
+                    response_output_multi = gr.Markdown(label="🤖 AI Response", elem_classes="output-box")
+                    sources_output_multi = gr.JSON(label="📚 Sources", elem_classes="output-box")
             
-            list_btn.click(
-                fn=list_all_collections,
-                outputs=[collections_output, collections_json, collection_to_delete]
+            submit_btn_multi.click(
+                fn=process_multiple_links,
+                inputs=[url_input_multi, prompt_input_multi],
+                outputs=[status_output_multi, response_output_multi, sources_output_multi]
             )
-            
-            delete_btn.click(
-                fn=delete_collection,
-                inputs=[collection_to_delete],
-                outputs=[delete_status, collections_output, collections_json, collection_to_delete]
-            )
-            
-            gr.HTML("""
-                <div class="note-box" style="background: linear-gradient(135deg, rgba(0, 242, 254, 0.08) 0%, rgba(79, 172, 254, 0.08) 100%); border-radius: 12px; padding: 1rem; border-left: 4px solid #00f2fe; margin-top: 1rem;">
-                    <p style="margin: 0; font-size: 1.05rem; color: #00c6ff;">
-                        ⚠️ <strong>Warning:</strong> Deleting a collection is permanent and cannot be undone. All documents in the collection will be removed.
-                    </p>
-                </div>
-            """)
         
-        # System Health Tab
-        with gr.Tab("⚙️ System Health"):
-            gr.Markdown("### System Status & Configuration")
+        with gr.Tab("🏠 Homepage Only Analysis"):
+            with gr.Row():
+                with gr.Column():
+                    url_input_home = gr.Textbox(
+                        label="🌍 Website URL", 
+                        placeholder="https://example.com",
+                        elem_classes="output-box"
+                    )
+                    prompt_input_home = gr.Textbox(
+                        label="💭 Your Question", 
+                        placeholder="What is this website about?", 
+                        lines=3,
+                        elem_classes="output-box"
+                    )
+                    submit_btn_home = gr.Button("✨ Analyze Homepage", variant="primary")
             
-            health_check_btn = gr.Button("🔍 Check System Health", variant="primary")
-            health_output = gr.Markdown(label="🟢 System Status", elem_classes="output-box")
-            health_json = gr.JSON(label="📋 Detailed Configuration", elem_classes="output-box")
+            with gr.Row():
+                with gr.Column():
+                    status_output_home = gr.Textbox(label="📊 Status", elem_classes="output-box")
+                    response_output_home = gr.Markdown(label="🤖 AI Response", elem_classes="output-box")
+                    sources_output_home = gr.JSON(label="📚 Sources", elem_classes="output-box")
             
-            health_check_btn.click(
-                fn=get_system_health,
-                outputs=[health_output, health_json]
+            submit_btn_home.click(
+                fn=process_homepage_only,
+                inputs=[url_input_home, prompt_input_home],
+                outputs=[status_output_home, response_output_home, sources_output_home]
             )
-            
-            gr.HTML("""
-                <div class="example-box" style="background: linear-gradient(135deg, rgba(0, 242, 254, 0.1) 0%, rgba(79, 172, 254, 0.1) 100%); border-radius: 16px; padding: 1.5rem; border-left: 4px solid #00f2fe; margin-top: 2rem;">
-                    <h3 style="margin-top: 0; font-size: 1.4rem; color: #e0e0e0;">
-                        <span class="floating-icon">📊</span> Health Check Information
-                    </h3>
-                    <p style="font-size: 1.1rem; color: #e0e0e0;"><strong>Supabase:</strong> Cloud storage for documents (optional)</p>
-                    <p style="font-size: 1.1rem; color: #e0e0e0;"><strong>Groq API:</strong> LLM for generating answers</p>
-                    <p style="font-size: 1.1rem; color: #e0e0e0;"><strong>Vector Stores:</strong> FAISS collections for document embeddings</p>
-                </div>
-            """)
     
     gr.HTML("""
-        <div class="note-box" style="margin-top: 2rem; background: linear-gradient(135deg, rgba(0, 242, 254, 0.08) 0%, rgba(79, 172, 254, 0.08) 100%); border-radius: 12px; padding: 1rem; border-left: 4px solid #00f2fe;">
-            <p style="margin: 0; font-size: 1.05rem; color: #00c6ff;">
-                ℹ️ <strong>Note:</strong> This app features automatic retry logic and progress tracking. 
-                If you encounter errors, the system will automatically retry. Large files may take longer to process.
+        <div class="example-box">
+            <h3 style="margin-top: 0; font-size: 1.4rem;">
+                <span class="floating-icon">💡</span> Example Usage
+            </h3>
+            <p style="font-size: 1.1rem;"><strong>URL:</strong> https://openai.com</p>
+            <p style="font-size: 1.1rem;"><strong>Question:</strong> What are the main products and services offered?</p>
+        </div>
+        
+        <div class="note-box">
+            <p style="margin: 0; font-size: 1.05rem;">
+                ℹ️ <strong>Note:</strong> If you encounter connection errors, please wait a moment for the system to initialize and try again.
             </p>
         </div>
     """)
 
-# Launch configuration for Hugging Face Spaces
+# Launch the interface
 if __name__ == "__main__":
-    # Check API health on startup
-    is_healthy, _ = check_api_health(max_attempts=5)
-    
-    if not is_healthy:
-        print("⚠️ Warning: API is not responding. The app will launch but may not work correctly.")
-    
     interface.launch(
         server_name="0.0.0.0",
         server_port=7860,
         share=False,
         show_error=True,
-        show_api=False
-    )
+        quiet=False
+    )