Update interface.py

interface.py

@@ -3,264 +3,355 @@ 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
 
-# ==================== EXISTING FUNCTIONS ====================
-
-def extract_links(url):
-    """Extract links from the given URL"""
-    endpoint = f"{API_BASE_URL}/extract_links"
-    payload = {"url": url}
-    try:
-        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)}")
+# ==================== 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 extract_text(urls):
-    """Extract text from URLs"""
-    endpoint = f"{API_BASE_URL}/extract_text"
-    try:
-        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 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)
 
+# ==================== WEB SCRAPING FUNCTIONS ====================
 
-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}
+def extract_links(url: str, progress=gr.Progress()) -> List[str]:
+    """Extract links from URL with progress tracking"""
+    if not url:
+        raise gr.Error("❌ Please provide a URL", duration=5)
+    
+    progress(0, desc="Connecting to website...")
+    
     try:
-        response = requests.post(endpoint, json=payload, timeout=60)
-        if response.status_code == 200:
-            return response.json()
-        else:
-            raise Exception(f"Failed to perform RAG: {response.text}")
-    except requests.exceptions.RequestException as e:
-        raise Exception(f"Connection error: {str(e)}")
-
+        response = make_request_with_retry(
+            "POST",
+            "/extract_links",
+            json={"url": url},
+            timeout=30
+        )
+        progress(1, desc="Links extracted!")
+        return response.json()["unique_links"]
+    except Exception as e:
+        raise gr.Error(f"❌ Failed to extract links: {str(e)}", duration=10)
 
-def check_api_health():
-    """Check if FastAPI is running with enhanced health check"""
+def extract_text(urls: List[str], progress=gr.Progress()) -> str:
+    """Extract text from URLs with progress tracking"""
+    progress(0, desc="Starting text extraction...")
+    
     try:
-        response = requests.get(f"{API_BASE_URL}/health_check", timeout=5)
-        return response.status_code == 200, response.json() if response.status_code == 200 else {}
-    except:
-        return False, {}
+        response = make_request_with_retry(
+            "POST",
+            "/extract_text",
+            json=urls,
+            timeout=120
+        )
+        progress(1, desc="Text extraction complete!")
+        return response.json()["file_saved"]
+    except Exception as e:
+        raise gr.Error(f"❌ Failed to extract text: {str(e)}", duration=10)
 
+def perform_rag(file_path: str, prompt: str, progress=gr.Progress()) -> dict:
+    """Perform RAG with progress tracking"""
+    progress(0.3, desc="Analyzing content...")
+    
+    try:
+        response = make_request_with_retry(
+            "POST",
+            "/rag",
+            json={"file_path": file_path, "prompt": prompt},
+            timeout=60
+        )
+        progress(1, desc="Analysis complete!")
+        return response.json()
+    except Exception as e:
+        raise gr.Error(f"❌ Failed to perform RAG: {str(e)}", duration=10)
 
-def process_multiple_links(url, prompt):
-    """Process multiple links from a webpage"""
+def process_multiple_links(url: str, prompt: str, progress=gr.Progress()) -> Tuple[str, str, dict]:
+    """Process multiple links with comprehensive progress tracking"""
     if not url or not prompt:
-        return "❌ Error: Please provide both URL and prompt", "", ""
-    
-    is_healthy, _ = check_api_health()
-    if not is_healthy:
-        return "❌ Error: FastAPI service is not available. Please wait a moment and try again.", "", ""
+        raise gr.Error("❌ Please provide both URL and prompt", duration=5)
     
     try:
-        links = extract_links(url)
+        progress(0, desc="🔍 Extracting links from webpage...")
+        links = extract_links(url, progress)
+        
+        progress(0.3, desc=f"📄 Found {len(links)} links. Processing top 5...")
         sample_links = links[:5]
-        file_path = extract_text(sample_links)
-        result = perform_rag(file_path, prompt)
+        file_path = extract_text(sample_links, progress)
+        
+        progress(0.7, desc="🤖 Generating AI response...")
+        result = perform_rag(file_path, prompt, progress)
+        
+        progress(1, desc="✅ Complete!")
         
         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_msg, response_text, sources_text
+        return status_msg, response_text, result['sources']
         
+    except gr.Error:
+        raise
     except Exception as e:
-        return f"❌ Error: {str(e)}", "", ""
+        raise gr.Error(f"❌ Processing error: {str(e)}", duration=10)
 
-
-def process_homepage_only(url, prompt):
-    """Process homepage content only"""
+def process_homepage_only(url: str, prompt: str, progress=gr.Progress()) -> Tuple[str, str, dict]:
+    """Process homepage only with progress tracking"""
     if not url or not prompt:
-        return "❌ Error: Please provide both URL and prompt", "", ""
-    
-    is_healthy, _ = check_api_health()
-    if not is_healthy:
-        return "❌ Error: FastAPI service is not available. Please wait a moment and try again.", "", ""
+        raise gr.Error("❌ Please provide both URL and prompt", duration=5)
     
     try:
-        file_path = extract_text([url])
-        result = perform_rag(file_path, prompt)
+        progress(0.2, desc="📄 Extracting homepage content...")
+        file_path = extract_text([url], progress)
+        
+        progress(0.6, desc="🤖 Generating AI response...")
+        result = perform_rag(file_path, prompt, progress)
+        
+        progress(1, desc="✅ Complete!")
         
         status_msg = "✅ Processed homepage content"
         response_text = f"**Query:** {result['user_query']}\n\n**Response:** {result['assistant_response']}"
-        sources_text = result['sources']
         
-        return status_msg, response_text, sources_text
+        return status_msg, response_text, result['sources']
         
+    except gr.Error:
+        raise
     except Exception as e:
-        return f"❌ Error: {str(e)}", "", ""
+        raise gr.Error(f"❌ Processing error: {str(e)}", duration=10)
 
+# ==================== DOCUMENT UPLOAD FUNCTIONS ====================
 
-# ==================== NEW RAG DOCUMENT FUNCTIONS ====================
-
-def upload_single_document(file, collection_name):
-    """Upload a single document (PDF/Markdown) to RAG system"""
+def upload_single_document(file, collection_name: str, progress=gr.Progress()) -> Tuple[str, dict]:
+    """Upload single document with progress tracking"""
     if not file:
-        return "❌ Error: Please select a file to upload", None
+        raise gr.Error("❌ Please select a file to upload", duration=5)
     
     if not collection_name:
         collection_name = "default"
     
     try:
-        endpoint = f"{API_BASE_URL}/upload_document"
-        with open(file.name, 'rb') as f:
-            files = {'file': (os.path.basename(file.name), f, 'application/pdf')}
-            params = {'collection_name': collection_name}
-            response = requests.post(endpoint, files=files, params=params, timeout=120)
+        progress(0.1, desc="📤 Uploading document...")
         
-        if response.status_code == 200:
-            result = response.json()
-            status_msg = f"✅ Successfully uploaded '{result['filename']}'\n📊 Created {result['chunks_created']} chunks\n🗂️ Collection: {collection_name}\n📁 File Type: {result['file_type']}"
-            return status_msg, result
-        else:
-            return f"❌ Error: {response.text}", None
+        with open(file.name if hasattr(file, 'name') else file, 'rb') as f:
+            files = {'file': (os.path.basename(file.name if hasattr(file, 'name') else file), f)}
+            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:
-        return f"❌ Error: {str(e)}", None
-
+        raise gr.Error(f"❌ Upload failed: {str(e)}", duration=10)
 
-def upload_multiple_documents(files, collection_name):
-    """Upload multiple documents at once"""
+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:
-        return "❌ Error: Please select files to upload", None
+        raise gr.Error("❌ Please select files to upload", duration=5)
     
     if not collection_name:
         collection_name = "default"
     
     try:
-        endpoint = f"{API_BASE_URL}/upload_multiple_documents"
-        files_to_upload = []
+        progress(0.1, desc=f"📤 Uploading {len(files)} documents...")
         
-        for file in files:
-            with open(file.name, 'rb') as f:
+        files_to_upload = []
+        for idx, file in enumerate(files):
+            file_path = file.name if hasattr(file, 'name') else file
+            with open(file_path, 'rb') as f:
                 file_content = f.read()
                 files_to_upload.append(
-                    ('files', (os.path.basename(file.name), file_content, 'application/pdf'))
+                    ('files', (os.path.basename(file_path), file_content))
                 )
+            progress((idx + 1) / len(files) * 0.5, desc=f"Reading file {idx + 1}/{len(files)}...")
         
+        progress(0.5, desc="🔄 Processing all documents...")
         params = {'collection_name': collection_name}
-        response = requests.post(endpoint, files=files_to_upload, params=params, timeout=180)
+        response = make_request_with_retry(
+            "POST",
+            "/upload_multiple_documents",
+            files=files_to_upload,
+            params=params,
+            timeout=300
+        )
         
-        if response.status_code == 200:
-            result = response.json()
-            status_msg = f"✅ Successfully uploaded {result['successful_uploads']} files\n❌ Failed: {result['failed_uploads']}\n🗂️ Collection: {collection_name}"
-            return status_msg, result
-        else:
-            return f"❌ Error: {response.text}", None
-            
+        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:
-        return f"❌ Error: {str(e)}", None
+        raise gr.Error(f"❌ Upload failed: {str(e)}", duration=10)
 
-
-def query_rag_documents(query, collection_name, top_k):
-    """Query documents in a collection"""
+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:
-        return "❌ Error: Please enter a query", "", None
+        raise gr.Error("❌ Please enter a query", duration=5)
     
     if not collection_name:
-        return "❌ Error: Please select a collection", "", None
+        raise gr.Error("❌ Please select a collection", duration=5)
     
     try:
-        endpoint = f"{API_BASE_URL}/query_documents"
-        payload = {
-            "query": query,
-            "collection_name": collection_name,
-            "top_k": top_k
-        }
-        response = requests.post(endpoint, json=payload, timeout=60)
-        
-        if response.status_code == 200:
-            result = response.json()
-            
-            if "I couldn't find this information" in result['answer']:
-                status_msg = "⚠️ No relevant information found in documents"
-            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']
+        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"
         else:
-            return f"❌ Error: {response.text}", "", None
-            
+            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:
-        return f"❌ Error: {str(e)}", "", None
+        raise gr.Error(f"❌ Query failed: {str(e)}", duration=10)
 
-
-def list_all_collections():
-    """List all available collections"""
+def list_all_collections() -> Tuple[str, dict, gr.Dropdown]:
+    """List all collections with error handling"""
     try:
-        endpoint = f"{API_BASE_URL}/list_collections"
-        response = requests.get(endpoint, timeout=10)
+        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")
         
-        if response.status_code == 200:
-            result = response.json()
-            collections = result['collections']
-            
-            if not collections:
-                return "📂 No collections found. Upload documents to create a collection.", None, []
-            
-            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, collection_names
-        else:
-            return f"❌ Error: {response.text}", None, []
-            
     except Exception as e:
-        return f"❌ Error: {str(e)}", None, []
+        raise gr.Error(f"❌ Failed to list collections: {str(e)}", duration=10)
 
-
-def delete_collection(collection_name):
-    """Delete a collection"""
+def delete_collection(collection_name: str) -> Tuple[str, str, dict, gr.Dropdown]:
+    """Delete collection with confirmation"""
     if not collection_name:
-        return "❌ Error: Please select a collection to delete"
+        raise gr.Error("❌ Please select a collection to delete", duration=5)
     
     try:
-        endpoint = f"{API_BASE_URL}/delete_collection/{collection_name}"
-        response = requests.delete(endpoint, timeout=10)
+        response = make_request_with_retry(
+            "DELETE",
+            f"/delete_collection/{collection_name}",
+            timeout=10
+        )
+        
+        status = f"✅ Successfully deleted collection '{collection_name}'"
+        
+        # Refresh collections list
+        summary, result, dropdown = list_all_collections()
+        
+        return status, summary, result, dropdown
         
-        if response.status_code == 200:
-            return f"✅ Successfully deleted collection '{collection_name}'"
-        else:
-            return f"❌ Error: {response.text}"
-            
     except Exception as e:
-        return f"❌ Error: {str(e)}"
+        raise gr.Error(f"❌ Failed to delete collection: {str(e)}", duration=10)
 
-
-def get_system_health():
-    """Get comprehensive system health information"""
+def get_system_health() -> Tuple[str, dict]:
+    """Get system health information"""
     try:
         is_healthy, health_data = check_api_health()
         
         if not is_healthy:
-            return "❌ System Offline", None
+            raise gr.Error("❌ System is offline. Please refresh the page.", duration=None)
         
-        health_summary = f"""
-🟢 **System Status: Healthy**
+        health_summary = f"""🟢 **System Status: Healthy**
 
 📊 **Configuration:**
 - Supabase: {'✅ Configured' if health_data.get('supabase_configured') else '❌ Not Configured'}
@@ -270,21 +361,26 @@ def get_system_health():
 📁 **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
         
+    except gr.Error:
+        raise
     except Exception as e:
-        return f"❌ Error: {str(e)}", None
-
+        raise gr.Error(f"❌ Health check failed: {str(e)}", duration=10)
 
-# ==================== DARK THEME CSS ====================
+# ==================== GRADIO UI ====================
 
+# Custom CSS (same as before)
 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%;
@@ -295,7 +391,6 @@ 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);
@@ -306,16 +401,9 @@ 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%;
@@ -334,7 +422,6 @@ 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;
@@ -343,7 +430,6 @@ 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;
@@ -358,14 +444,11 @@ 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;
@@ -380,236 +463,43 @@ 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: 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;
+    min-height: 150px !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;
-}
-/* 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;
-}
-/* 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);
-    }
 }
 """
 
-
-# ==================== MAIN INTERFACE ====================
-
-with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as interface:
+# Build interface
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft(), title="RAG Assistant") as interface:
     gr.HTML("""
         <div class="main-container">
             <h1 class="animated-title">
-                <span class="floating-icon">🌐</span> Advanced Web & Document RAG System
+                <span class="floating-icon">🌐</span> Advanced RAG Assistant
             </h1>
-            <p class="subtitle">
-                Extract from web pages, upload documents (PDF/Markdown), and ask questions using AI-powered retrieval
+            <p class="subtitle" style="color: #b0b0b0; font-size: 1.3rem; text-align: center; margin-bottom: 2rem;">
+                Upload documents (PDF/Markdown) or extract from web pages - Ask questions using AI-powered retrieval
             </p>
         </div>
     """)
     
     with gr.Tabs() as tabs:
-        # ==================== WEB URL TABS (EXISTING) ====================
-        
+        # Web Scraping Tabs
         with gr.Tab("🔗 Multiple Links Analysis"):
             with gr.Row():
                 with gr.Column():
-                    url_input_multi = gr.Textbox(
-                        label="🌍 Website URL", 
-                        placeholder="https://example.com",
-                        elem_classes="output-box"
-                    )
-                    prompt_input_multi = gr.Textbox(
-                        label="💭 Your Question", 
-                        placeholder="What is this website about?", 
-                        lines=3,
-                        elem_classes="output-box"
-                    )
+                    url_input_multi = gr.Textbox(label="🌍 Website URL", placeholder="https://example.com")
+                    prompt_input_multi = gr.Textbox(label="💭 Your Question", placeholder="What is this website about?", lines=3)
                     submit_btn_multi = gr.Button("✨ Analyze Multiple Links", variant="primary")
             
             with gr.Row():
@@ -627,17 +517,8 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as interface:
         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"
-                    )
+                    url_input_home = gr.Textbox(label="🌍 Website URL", placeholder="https://example.com")
+                    prompt_input_home = gr.Textbox(label="💭 Your Question", placeholder="What is this website about?", lines=3)
                     submit_btn_home = gr.Button("✨ Analyze Homepage", variant="primary")
             
             with gr.Row():
@@ -652,31 +533,28 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as interface:
                 outputs=[status_output_home, response_output_home, sources_output_home]
             )
         
-        # ==================== DOCUMENT UPLOAD TAB (NEW) ====================
-        
+        # Document Upload Tab
         with gr.Tab("📄 Document Upload & Query"):
             gr.Markdown("""
             ### Upload PDF or Markdown documents and query them using RAG
             - Supports **PDF** and **Markdown** files
-            - Documents are chunked and stored in vector database (FAISS)
-            - Organize documents into collections
+            - Documents are chunked and stored in FAISS vector database
+            - Organize documents into collections for better management
             """)
             
             with gr.Row():
                 with gr.Column():
                     gr.Markdown("#### 📤 Upload Documents")
                     collection_name_upload = gr.Textbox(
-                        label="🗂️ Collection Name", 
+                        label="🗂️ Collection Name",
                         placeholder="default",
-                        value="default",
-                        elem_classes="output-box"
+                        value="default"
                     )
                     
                     with gr.Tab("Single File"):
                         file_upload_single = gr.File(
                             label="📁 Select Document (PDF/Markdown)",
-                            file_types=[".pdf", ".md", ".txt"],
-                            elem_classes="output-box"
+                            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")
@@ -686,31 +564,27 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as interface:
                         file_upload_multi = gr.File(
                             label="📁 Select Documents (PDF/Markdown)",
                             file_count="multiple",
-                            file_types=[".pdf", ".md", ".txt"],
-                            elem_classes="output-box"
+                            file_types=[".pdf", ".md", ".txt"]
                         )
-                        upload_btn_multi = gr.Button("📤 Upload Multiple Documents", variant="primary")
+                        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 button to update collection list
                     refresh_btn = gr.Button("🔄 Refresh Collections", variant="secondary")
                     
                     collection_dropdown = gr.Dropdown(
                         label="🗂️ Select Collection",
                         choices=["default"],
-                        value="default",
-                        elem_classes="output-box"
+                        value="default"
                     )
                     
                     query_input = gr.Textbox(
                         label="💭 Your Question",
                         placeholder="Ask a question about your documents...",
-                        lines=3,
-                        elem_classes="output-box"
+                        lines=3
                     )
                     
                     top_k_slider = gr.Slider(
@@ -718,8 +592,7 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as interface:
                         maximum=10,
                         value=3,
                         step=1,
-                        label="📊 Number of Sources (top-k)",
-                        elem_classes="output-box"
+                        label="📊 Number of Sources (top-k)"
                     )
                     
                     query_btn = gr.Button("🔍 Search Documents", variant="primary")
@@ -728,57 +601,37 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as interface:
                     query_response = gr.Markdown(label="🤖 AI Answer", elem_classes="output-box")
                     query_sources = gr.JSON(label="📚 Source Citations", elem_classes="output-box")
             
-            # Upload button actions
+            # 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.click(
+            upload_btn_multi_doc.click(
                 fn=upload_multiple_documents,
                 inputs=[file_upload_multi, collection_name_upload],
                 outputs=[upload_status_multi, upload_result_multi]
             )
             
-            # Query button action
             query_btn.click(
                 fn=query_rag_documents,
                 inputs=[query_input, collection_dropdown, top_k_slider],
                 outputs=[query_status, query_response, query_sources]
             )
             
-            # Refresh collections function
             def refresh_collections():
-                _, _, collection_names = list_all_collections()
-                if not collection_names:
-                    collection_names = ["default"]
-                return gr.Dropdown.update(choices=collection_names)
+                _, _, dropdown = list_all_collections()
+                return dropdown
             
             refresh_btn.click(
                 fn=refresh_collections,
-                inputs=[],
                 outputs=[collection_dropdown]
             )
-            
-            gr.HTML("""
-                <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>1.</strong> Upload your PDF/Markdown documents to a collection</p>
-                    <p style="font-size: 1.1rem;"><strong>2.</strong> Ask questions like: "What are the main findings?" or "Summarize the methodology"</p>
-                    <p style="font-size: 1.1rem;"><strong>3.</strong> System returns answers with source citations</p>
-                </div>
-            """)
-        
-        # ==================== COLLECTION MANAGEMENT TAB (NEW) ====================
         
+        # Collection Management Tab
         with gr.Tab("🗂️ Collection Management"):
-            gr.Markdown("""
-            ### Manage Your Document Collections
-            View, analyze, and delete document collections stored in the vector database.
-            """)
+            gr.Markdown("### Manage Your Document Collections")
             
             with gr.Row():
                 with gr.Column():
@@ -788,56 +641,24 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as interface:
                 
                 with gr.Column():
                     gr.Markdown("#### 🗑️ Delete Collection")
-                    collection_to_delete = gr.Dropdown(
-                        label="🗂️ Select Collection to Delete",
-                        choices=["default"],
-                        elem_classes="output-box"
-                    )
+                    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")
             
-            # List collections action
-            def list_and_update_dropdown():
-                summary, result, collection_names = list_all_collections()
-                if not collection_names:
-                    collection_names = ["default"]
-                return summary, result, gr.Dropdown.update(choices=collection_names)
-            
             list_btn.click(
-                fn=list_and_update_dropdown,
-                inputs=[],
+                fn=list_all_collections,
                 outputs=[collections_output, collections_json, collection_to_delete]
             )
             
-            # Delete collection action
-            def delete_and_refresh(collection_name):
-                status = delete_collection(collection_name)
-                summary, result, collection_names = list_all_collections()
-                if not collection_names:
-                    collection_names = ["default"]
-                return status, summary, result, gr.Dropdown.update(choices=collection_names)
-            
             delete_btn.click(
-                fn=delete_and_refresh,
+                fn=delete_collection,
                 inputs=[collection_to_delete],
                 outputs=[delete_status, collections_output, collections_json, collection_to_delete]
             )
-            
-            gr.HTML("""
-                <div class="note-box">
-                    <p style="margin: 0; font-size: 1.05rem;">
-                        ⚠️ <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 (NEW) ====================
         
+        # System Health Tab
         with gr.Tab("⚙️ System Health"):
-            gr.Markdown("""
-            ### System Status & Configuration
-            Monitor system health, API connections, and vector database statistics.
-            """)
+            gr.Markdown("### System Status & Configuration")
             
             health_check_btn = gr.Button("🔍 Check System Health", variant="primary")
             health_output = gr.Markdown(label="🟢 System Status", elem_classes="output-box")
@@ -845,38 +666,30 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as interface:
             
             health_check_btn.click(
                 fn=get_system_health,
-                inputs=[],
                 outputs=[health_output, health_json]
             )
-            
-            gr.HTML("""
-                <div class="example-box">
-                    <h3 style="margin-top: 0; font-size: 1.4rem;">
-                        <span class="floating-icon">📊</span> Health Check Information
-                    </h3>
-                    <p style="font-size: 1.1rem;"><strong>Supabase:</strong> Cloud storage for documents and extracted text</p>
-                    <p style="font-size: 1.1rem;"><strong>Groq API:</strong> LLM for generating answers</p>
-                    <p style="font-size: 1.1rem;"><strong>Vector Stores:</strong> FAISS collections for document embeddings</p>
-                </div>
-            """)
     
-    # Footer
     gr.HTML("""
-        <div class="note-box" style="margin-top: 2rem;">
-            <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.
-                The system supports both web scraping and document upload for comprehensive question-answering capabilities.
+        <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.
             </p>
         </div>
     """)
 
-
-# Launch the interface
+# Launch configuration for Hugging Face Spaces
 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,
-        quiet=False
+        show_api=False
     )