Update app.py

app.py

@@ -6,83 +6,70 @@ import gradio as gr
 import requests
 import re
 from urllib.parse import urlparse
-from bs4 import BeautifulSoup
 import time
-import joblib
 import os
 
 # --- import your architecture ---
-from model import DeBERTaLSTMClassifier
+# Make sure this file is in the repo (e.g., models/deberta_lstm_classifier.py)
+# and update the import path accordingly.
+from model import DeBERTaLSTMClassifier  # <-- your class
 
 # --- Import RAG modules ---
 from rag_engine import RAGEngine
 from llm_client import LLMClient
 
 # --------- Config ----------
-REPO_ID = "khoa-done/phishing-detector"       # HF repo that holds the checkpoint
-CKPT_NAME = "deberta_lstm_checkpoint.pt"      # the .pt file name
+REPO_ID = "dungeon29/phishing-deberta-lstm"       # HF repo that holds the checkpoint
+CKPT_NAME = "pytorch_model.bin"               # the .pt file name
 MODEL_NAME = "microsoft/deberta-base"         # base tokenizer/backbone
 LABELS = ["benign", "phishing"]               # adjust to your classes
 
-# --------- Load DeBERTa model/tokenizer once (global) ----------
-print("🔷 Loading DeBERTa Model...")
+# If your checkpoint contains hyperparams, you can fetch them like:
+# checkpoint.get("config") or checkpoint.get("model_args")
+# and pass into DeBERTaLSTMClassifier(**model_args)
+
+# --------- Load model/tokenizer once (global) ----------
 device = "cuda" if torch.cuda.is_available() else "cpu"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 
-# Check if checkpoint exists locally, otherwise download from HF
-if os.path.exists(CKPT_NAME):
-    print(f"📂 Found local checkpoint: {CKPT_NAME}")
-    ckpt_path = CKPT_NAME
-else:
-    print(f"⬇️ Downloading checkpoint {CKPT_NAME} from HF Hub...")
-    try:
-        ckpt_path = hf_hub_download(repo_id=REPO_ID, filename=CKPT_NAME)
-    except Exception as e:
-        print(f"⚠️ Could not download from HF: {e}")
-        print("🔄 Trying fallback to pytorch_model.bin...")
-        ckpt_path = hf_hub_download(repo_id=REPO_ID, filename="pytorch_model.bin")
-
+ckpt_path = hf_hub_download(repo_id=REPO_ID, filename=CKPT_NAME)
 checkpoint = torch.load(ckpt_path, map_location=device)
 
-# Initialize model
-if isinstance(checkpoint, dict):
-    model_args = checkpoint.get("model_args", {})
-else:
-    model_args = {}
+# If you saved hyperparams in the checkpoint, use them:
+model_args = checkpoint.get("model_args", {})  # e.g., {"lstm_hidden":256, "num_labels":2, ...}
 model = DeBERTaLSTMClassifier(**model_args)
 
 # Load weights
 try:
-    if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint:
-        state_dict = checkpoint["model_state_dict"]
-    elif isinstance(checkpoint, dict):
-        state_dict = checkpoint
-    else:
-        state_dict = checkpoint
+    state_dict = torch.load(ckpt_path, map_location=device)
+    
+    # Xử lý nếu file lưu dạng checkpoint đầy đủ (có key "model_state_dict")
+    if "model_state_dict" in state_dict:
+        state_dict = state_dict["model_state_dict"]
 
     model.load_state_dict(state_dict, strict=False)
     
+    # Kiểm tra layer attention
     if hasattr(model, 'attention') and 'attention.weight' not in state_dict:
          print("⚠️ Loaded model without attention layer, using newly initialized attention weights")
     else:
-         print("✅ DeBERTa Weights loaded successfully!")
+         print("✅ Load weights successfully!")
          
 except Exception as e:
     print(f"❌ Error when loading weights: {e}")
-    # Don't raise, allow app to start even if model fails (for debugging)
+    raise e
 
 model.to(device).eval()
 
 # --------- Initialize RAG & LLM ----------
-print("🔷 Initializing RAG Engine (LangChain)...")
+print("Initializing RAG Engine (LangChain)...")
 rag_engine = RAGEngine()
-print("✅ RAG Engine ready.")
+print("RAG Engine ready.")
 
-print("🔷 Initializing Qwen2.5-1.5B LLM (LangChain)...")
+print("Initializing Qwen2.5-3B LLM (LangChain)...")
 # Pass vector_store to LLMClient for RetrievalQA
 llm_client = LLMClient(vector_store=rag_engine.vector_store)
-print("✅ LLM ready.")
-
+print("LLM ready.")
 
 # --------- Helper functions ----------
 def is_url(text):
@@ -104,7 +91,7 @@ def fetch_html_content(url, timeout=10):
         }
         response = requests.get(url, headers=headers, timeout=timeout, verify=False)
         response.raise_for_status()
-                
+        
         return response.text, response.status_code
     except requests.exceptions.RequestException as e:
         return None, f"Request error: {str(e)}"
@@ -113,6 +100,7 @@ def fetch_html_content(url, timeout=10):
 
 def predict_single_text(text, text_type="text"):
     """Predict for a single text input"""
+    # Tokenize
     inputs = tokenizer(
         text,
         return_tensors="pt",
@@ -120,11 +108,14 @@ def predict_single_text(text, text_type="text"):
         padding=True,
         max_length=256
     )
+    # DeBERTa typically doesn't use token_type_ids
     inputs.pop("token_type_ids", None)
+    # Move to device
     inputs = {k: v.to(device) for k, v in inputs.items()}
 
     with torch.no_grad():
         try:
+            # Try to get predictions with attention weights
             result = model(**inputs, return_attention=True)
             if isinstance(result, tuple) and len(result) == 3:
                 logits, attention_weights, deberta_attentions = result
@@ -133,11 +124,13 @@ def predict_single_text(text, text_type="text"):
                 logits = result
                 has_attention = False
         except TypeError:
+            # Fallback for older model without return_attention parameter
             logits = model(**inputs)
             has_attention = False
             
         probs = F.softmax(logits, dim=-1).squeeze(0).tolist()
 
+    # Get tokens for visualization
     tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'].squeeze(0).tolist())
     
     return probs, tokens, has_attention, attention_weights if has_attention else None
@@ -150,38 +143,54 @@ def combine_predictions(url_probs, html_probs, url_weight=0.3, html_weight=0.7):
     ]
     return combined_probs
 
-# --------- DeBERTa Inference function ----------
+# --------- Inference function ----------
 def predict_fn(text: str):
     if not text or not text.strip():
         return {"error": "Please enter a URL or text."}, ""
 
     # Check if input is URL
     if is_url(text.strip()):
+        # Process URL
         url = text.strip()
+        
+        # Get prediction for URL itself
         url_probs, url_tokens, url_has_attention, url_attention = predict_single_text(url, "URL")
+        
+        # Try to fetch HTML content
         html_content, status = fetch_html_content(url)
         
         if html_content:
+            # Get prediction for HTML content
             html_probs, html_tokens, html_has_attention, html_attention = predict_single_text(html_content, "HTML")
+            
+            # Combine predictions
             combined_probs = combine_predictions(url_probs, html_probs)
+            
+            # Use combined probabilities but show analysis for both
             probs = combined_probs
-            tokens = url_tokens + ["[SEP]"] + html_tokens[:50]
+            tokens = url_tokens + ["[SEP]"] + html_tokens[:50]  # Limit HTML tokens for display
             has_attention = url_has_attention or html_has_attention
             attention_weights = url_attention if url_has_attention else html_attention
+            
             analysis_type = "Combined URL + HTML Analysis"
             fetch_status = f"✅ Successfully fetched HTML content (Status: {status})"
+            
         else:
+            # Fallback to URL-only analysis
             probs = url_probs
             tokens = url_tokens
             has_attention = url_has_attention
             attention_weights = url_attention
+            
             analysis_type = "URL-only Analysis"
             fetch_status = f"⚠️ Could not fetch HTML content: {status}"
     else:
+        # Process as regular text
         probs, tokens, has_attention, attention_weights = predict_single_text(text, "text")
         analysis_type = "Text Analysis"
         fetch_status = ""
 
+    # Create detailed analysis
     predicted_class = "phishing" if probs[1] > probs[0] else "benign"
     confidence = max(probs)
     
@@ -210,13 +219,20 @@ def predict_fn(text: str):
     
     if has_attention and attention_weights is not None:
         attention_scores = attention_weights.squeeze(0).tolist()
+        
         token_analysis = []
         for i, (token, score) in enumerate(zip(tokens, attention_scores)):
+            # More lenient filtering - include more tokens for text analysis
             if token not in ['[CLS]', '[SEP]', '[PAD]', '<s>', '</s>'] and len(token.strip()) > 0 and score > 0.005:  
-                clean_token = token.replace(' ', '').replace('Ġ', '').strip()
-                if clean_token:
-                    token_analysis.append({'token': clean_token, 'importance': score, 'position': i})
+                clean_token = token.replace(' ', '').replace('Ġ', '').strip()  # Handle different tokenizer prefixes
+                if clean_token:  # Only add if token has content after cleaning
+                    token_analysis.append({
+                        'token': clean_token,
+                        'importance': score,
+                        'position': i
+                    })
         
+        # Sort by importance
         token_analysis.sort(key=lambda x: x['importance'], reverse=True)
         
         detailed_analysis += f"""
@@ -226,20 +242,94 @@ def predict_fn(text: str):
 </div>
 <div style="font-family: Arial, sans-serif;">
 """
-        for i, token_info in enumerate(token_analysis[:10]):
+        
+        for i, token_info in enumerate(token_analysis[:10]):  # Top 10 tokens
             bar_width = int(token_info['importance'] * 100)
             color = "#ff4444" if predicted_class == "phishing" else "#44ff44"
+            
             detailed_analysis += f"""
 <div style="margin: 8px 0; display: flex; align-items: center; background: #2d2d2d; padding: 8px; border-radius: 8px; border-left: 4px solid {color};">
-    <div style="width: 30px; text-align: right; margin-right: 10px; font-weight: bold; color: #ffffff;">{i+1}.</div>
-    <div style="width: 120px; margin-right: 10px; font-weight: bold; color: #e0e0e0; text-align: right;">{token_info['token']}</div>
+    <div style="width: 30px; text-align: right; margin-right: 10px; font-weight: bold; color: #ffffff;">
+        {i+1}.
+    </div>
+    <div style="width: 120px; margin-right: 10px; font-weight: bold; color: #e0e0e0; text-align: right;">
+        {token_info['token']}
+    </div>
     <div style="width: 300px; background-color: #404040; border-radius: 10px; overflow: hidden; margin-right: 10px; border: 1px solid #555;">
         <div style="width: {bar_width}%; background-color: {color}; height: 20px; border-radius: 10px; transition: width 0.3s ease;"></div>
     </div>
-    <div style="color: #cccccc; font-size: 12px; font-weight: bold;">{token_info['importance']:.1%}</div>
+    <div style="color: #cccccc; font-size: 12px; font-weight: bold;">
+        {token_info['importance']:.1%}
+    </div>
 </div>
 """
+        
         detailed_analysis += "</div>\n"
+        
+        detailed_analysis += f"""
+## Detailed analysis:
+<div style="font-family: Arial, sans-serif; background: linear-gradient(135deg, #1a237e 0%, #3949ab 100%); padding: 20px; border-radius: 15px; color: white; margin: 15px 0; border: 2px solid #3f51b5;">
+    <h3 style="margin: 0 0 15px 0; color: white;">Statistical Overview</h3>
+    <div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 15px;">
+        <div style="background: rgba(255,255,255,0.1); padding: 15px; border-radius: 10px; border: 1px solid rgba(255,255,255,0.2);">
+            <div style="font-size: 24px; font-weight: bold; color: white;">{len([t for t in tokens if t not in ['[CLS]', '[SEP]', '[PAD]']])}</div>
+            <div style="font-size: 14px; color: #e0e0e0;">Total tokens</div>
+        </div>
+        <div style="background: rgba(255,255,255,0.1); padding: 15px; border-radius: 10px; border: 1px solid rgba(255,255,255,0.2);">
+            <div style="font-size: 24px; font-weight: bold, color: white;">{len([t for t in token_analysis if t['importance'] > 0.05])}</div>
+            <div style="font-size: 14px, color: #e0e0e0;">High impact tokens (>5%)</div>
+        </div>
+    </div>
+</div>
+<div style="font-family: Arial, sans-serif; margin: 15px 0; background: #2d2d2d; padding: 20px; border-radius: 15px; border: 1px solid #555;">
+    <h3 style="color: #ffffff; margin-bottom: 15px;"> Prediction Confidence</h3>
+    <div style="display: flex; justify-content: space-between; margin-bottom: 10px;">
+        <span style="font-weight: bold; color: #ff4444;">Phishing</span>
+        <span style="font-weight: bold; color: #44ff44;">Benign</span>
+    </div>
+    <div style="width: 100%; background-color: #404040; border-radius: 25px; overflow: hidden; height: 30px; border: 1px solid #666;">
+        <div style="width: {probs[1]*100:.1f}%; background: linear-gradient(90deg, #ff4444 0%, #ff6666 100%); height: 100%; display: flex; align-items: center; justify-content: center; color: white; font-weight: bold; font-size: 14px;">
+            {probs[1]:.1%}
+        </div>
+    </div>
+    <div style="margin-top: 10px; text-align: center; color: #cccccc; font-size: 14px;">
+        Benign: {probs[0]:.1%}
+    </div>
+</div>
+"""
+    else:
+        # Fallback analysis without attention weights
+        detailed_analysis += f"""
+<div style="background: linear-gradient(135deg, #1a237e 0%, #3949ab 100%); padding: 20px; border-radius: 15px; color: white; margin: 15px 0; border: 2px solid #3f51b5;">
+    <h3 style="margin: 0 0 15px 0; color: white;">Basic Analysis</h3>
+    <div style="display: grid; grid-template-columns: repeat(3, 1fr); gap: 15px;">
+        <div style="background: rgba(255,255,255,0.1); padding: 15px; border-radius: 10px; text-align: center; border: 1px solid rgba(255,255,255,0.2);">
+            <div style="font-size: 24px; font-weight: bold; color: white;">{probs[1]:.1%}</div>
+            <div style="font-size: 14px; color: #e0e0e0;">Phishing</div>
+        </div>
+        <div style="background: rgba(255,255,255,0.1); padding: 15px; border-radius: 10px; text-align: center; border: 1px solid rgba(255,255,255,0.2);">
+            <div style="font-size: 24px; font-weight: bold; color: white;">{probs[0]:.1%}</div>
+            <div style="font-size: 14px; color: #e0e0e0;">Benign</div>
+        </div>
+        <div style="background: rgba(255,255,255,0.1); padding: 15px; border-radius: 10px; text-align: center; border: 1px solid rgba(255,255,255,0.2);">
+            <div style="font-size: 24px; font-weight: bold; color: white;">{len([t for t in tokens if t not in ['[CLS]', '[SEP]', '[PAD]']])}</div>
+            <div style="font-size: 14px; color: #e0e0e0;">Tokens</div>
+        </div>
+    </div>
+</div>
+<div style="font-family: Arial, sans-serif; margin: 15px 0; background: #2d2d2d; padding: 20px; border-radius: 15px; border: 1px solid #555;">
+    <h3 style="color: #ffffff; margin: 0 0 15px 0;">🔤 Tokens in text:</h3>
+    <div style="display: flex; flex-wrap: wrap; gap: 8px;">""" + ''.join([f'<span style="background: #404040; color: #64b5f6; padding: 4px 8px; border-radius: 15px; font-size: 12px; border: 1px solid #666;">{token.replace(" ", "")}</span>' for token in tokens if token not in ['[CLS]', '[SEP]', '[PAD]']]) + f"""</div>
+    <div style="margin-top: 15px; padding: 10px; background: #3d2914; border-radius: 8px; border-left: 4px solid #ff9800;">
+        <strong style="color: #ffcc02;">Debug info:</strong> <span style="color: #e0e0e0;">Found {len(tokens)} total tokens, {len([t for t in tokens if t not in ['[CLS]', '[SEP]', '[PAD]']])} content tokens</span>
+    </div>
+</div>
+<div style="background: #3d2914; padding: 15px; border-radius: 10px; border-left: 4px solid #ff9800; margin: 15px 0;">
+    <p style="margin: 0; color: #ffcc02; font-size: 14px;">
+        <strong>Note:</strong> Detailed attention weights analysis is not available for the current model.
+    </p>
+</div>
+"""
 
     # Build label->prob mapping for Gradio Label output
     if len(LABELS) == len(probs):
@@ -266,7 +356,7 @@ def rag_predict_fn(text: str):
         
         if fetched_content:
             # Limit content length to avoid token overflow
-            truncated_content = fetched_content[:1500] 
+            truncated_content = fetched_content[:4000] 
             analysis_context = f"URL: {input_text}\n\nWebsite Content:\n{truncated_content}\n..."
             print(f"✅ Successfully fetched {len(fetched_content)} chars from URL.")
         else:
@@ -278,11 +368,12 @@ def rag_predict_fn(text: str):
     
     return response
 
+# --------- Refresh Knowledge Base function ----------
 def refresh_kb():
     return rag_engine.refresh_knowledge_base()
 
 # --------- Gradio UI ----------
-css_style = """
+css_style="""
 .gradio-container {
     font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
     background-color: #1e1e1e !important;
@@ -308,84 +399,98 @@ css_style = """
 }
 .gradio-button:hover {
     background-color: #5a5a5a !important;
+    color: #ffffff !important;
+    border: 1px solid #666 !important;
 }
 """
-
-# Tab 1: DeBERTa
-deberta_interface = gr.Interface(
-    fn=predict_fn,
-    inputs=gr.Textbox(label="URL or text", placeholder="Example: http://suspicious-site.example or paste any text"),
-    outputs=[
-        gr.Label(label="Prediction result"),
-        gr.Markdown(label="Detailed token analysis")
-    ],
-    title="Phishing Detector (DeBERTa + LSTM)",
-    description="""
-    Enter a URL or text for analysis.
-    **Features:**
-    - **URL Analysis**: For URLs, the system will fetch HTML content and combine both URL and content analysis
-    - **Combined Prediction**: Uses weighted combination of URL structure and webpage content analysis  
-    - **Visual Analysis**: Predict phishing/benign probability with visual charts
-    - **Token Importance**: Display the most important tokens in classification
-    - **Detailed Insights**: Comprehensive analysis of the impact of each token
-    - **Dark Theme**: Beautiful interface with colorful charts optimized for dark themes
-    """,
-    examples=[
-        ["http://rendmoiunserviceeee.com"],
-        ["https://www.google.com"],
-        ["Dear customer, your account has been suspended. Click here to verify your identity immediately."],
-        ["https://mail-secure-login-verify.example/path?token=suspicious"],
-        ["http://paypaI-security-update.net/login"],
-        ["Your package has been delivered successfully. Thank you for using our service."],
-        ["https://github.com/user/repo"]
-    ]
-)
-
-# Tab 2: LLM + RAG
-with gr.Blocks() as rag_interface:
-    gr.Markdown("# 🤖 AI Assistant (RAG)")
-    gr.Markdown("""
-    **AI Assistant** uses **Qwen2.5-1.5B** + **LangChain** to explain *why* a message is suspicious.
+with gr.Blocks() as demo:
+    gr.Markdown("# 🛡️ Phishing Detector (DeBERTa + LSTM + RAG)")
     
-    **Features:**
-    - 🌐 Multilingual support (English + Vietnamese)
-    - 📚 Knowledge Base retrieval (Auto-sync)
-    - � **Auto-Fetch URL**: Automatically reads website content for analysis
-    """)
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            rag_input = gr.Textbox(
-                label="Suspicious Text/URL",
-                placeholder="Paste the email content or URL here...",
-                lines=5
-            )
+    with gr.Tabs():
+        # --- Tab 1: Standard Detection ---
+        with gr.TabItem("🔍 Standard Detection"):
+            gr.Markdown("""
+            Enter a URL or text for analysis using the DeBERTa + LSTM model.
+            
+            **Features:**
+            - **URL Analysis**: For URLs, the system will fetch HTML content and combine both URL and content analysis
+            - **Combined Prediction**: Uses weighted combination of URL structure and webpage content analysis
+            - **Visual Analysis**: Predict phishing/benign probability with visual charts
+            - **Token Importance**: Display the most important tokens in classification
+            - **Detailed Insights**: Comprehensive analysis of the impact of each token
+            
+            **How it works for URLs:**
+            1. Analyze the URL structure itself
+            2. Fetch the webpage HTML content
+            3. Analyze the webpage content
+            4. Combine both results for final prediction (30% URL + 70% content)
+            """)
+            
             with gr.Row():
-                btn_rag = gr.Button("🤖 Ask AI Assistant", variant="primary")
-                btn_refresh = gr.Button("♻️ Refresh Knowledge Base")
+                with gr.Column(scale=2):
+                    input_box = gr.Textbox(
+                        label="URL or text", 
+                        placeholder="Example: http://suspicious-site.example or paste any text",
+                        lines=3
+                    )
+                    btn_submit = gr.Button("🔍 Analyze", variant="primary")
+                    
+                    gr.Examples(
+                        examples=[
+                            ["http://rendmoiunserviceeee.com"],
+                            ["https://www.google.com"],
+                            ["Dear customer, your account has been suspended. Click here to verify your identity immediately."],
+                            ["https://mail-secure-login-verify.example/path?token=suspicious"],
+                            ["http://paypaI-security-update.net/login"],
+                            ["Your package has been delivered successfully. Thank you for using our service."],
+                            ["https://github.com/user/repo"],
+                            ["Dear customer, your account has been suspended. Click here to verify."],
+                        ],
+                        inputs=input_box
+                    )
+                    
+                with gr.Column(scale=3):
+                    output_html = gr.HTML(label="Analysis Result")
+
+            btn_submit.click(fn=predict_fn, inputs=input_box, outputs=output_html)
+
+        # --- Tab 2: LLM + RAG Analysis ---
+        with gr.TabItem("🤖 AI Assistant (RAG)"):
+            gr.Markdown("""
+            **AI Assistant** uses **Qwen2.5-3B** + **LangChain** to explain *why* a message is suspicious.
             
-            gr.Examples(
-                examples=[
-                    ["Your PayPal account has been suspended. Click http://paypal-verify.com to unlock."],
-                    ["Tài khoản ngân hàng của bạn bị khóa. Nhấn vào đây để mở khóa ngay."],
-                    ["Your package is ready for delivery. Track here: https://fedex-track.com"],
-                ],
-                inputs=rag_input
-            )
-        
-        with gr.Column(scale=1):
-            rag_output = gr.Markdown(label="AI Analysis")
-            refresh_output = gr.Markdown(label="Status")
-    
-    btn_rag.click(fn=rag_predict_fn, inputs=[rag_input], outputs=rag_output)
-    btn_refresh.click(fn=refresh_kb, inputs=[], outputs=refresh_output)
-
-
-# Combine Tabs
-demo = gr.TabbedInterface(
-    [deberta_interface, rag_interface],
-    ["DeBERTa + LSTM", "AI Assistant (RAG)"]
-)
+            **Features:**
+            - 🌐 Multilingual support (English + Vietnamese)
+            - 📚 Knowledge Base retrieval (Auto-sync)
+            - 🚀 No rate limits (self-hosted)
+            """)
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    rag_input = gr.Textbox(
+                        label="Suspicious Text/URL",
+                        placeholder="Paste the email content or URL here...",
+                        lines=5
+                    )
+                    with gr.Row():
+                        btn_rag = gr.Button("🤖 Ask AI Assistant", variant="primary")
+                        btn_refresh = gr.Button("♻️ Refresh Knowledge Base")
+                    
+                    gr.Examples(
+                        examples=[
+                            ["Your PayPal account has been suspended. Click http://paypal-verify.com to unlock."],
+                            ["Tài khoản ngân hàng của bạn bị khóa. Nhấn vào đây để mở khóa ngay."],
+                            ["Your package is ready for delivery. Track here: https://fedex-track.com"],
+                        ],
+                        inputs=rag_input
+                    )
+                
+                with gr.Column(scale=1):
+                    rag_output = gr.Markdown(label="AI Analysis")
+                    refresh_output = gr.Markdown(label="Status")
+            
+            btn_rag.click(fn=rag_predict_fn, inputs=[rag_input], outputs=rag_output)
+            btn_refresh.click(fn=refresh_kb, inputs=[], outputs=refresh_output)
 
 if __name__ == "__main__":
     demo.launch(ssr_mode=False)