Upload app.py

app.py

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+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer
+from huggingface_hub import hf_hub_download
+import gradio as gr
+import requests
+import re
+import time
+import sys
+import logging
+import urllib3  # Import urllib3 to handle warnings
+
+# --- Suppress specific noisy asyncio errors on shutdown ---
+if sys.version_info >= (3, 10):
+    logging.getLogger("asyncio").setLevel(logging.WARNING)
+
+# --- Suppress InsecureRequestWarning ---
+# This is expected behavior for a Phishing Detector as we often scan sites with invalid SSL
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
+# --- import your architecture ---
+# 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 = "dungeon29/deberta-lstm-detect-phishing"       
+CKPT_NAME = "pytorch_model.bin"               
+MODEL_NAME = "microsoft/deberta-base"         # base tokenizer/backbone
+LABELS = ["benign", "phishing"]               # adjust to your classes
+
+# 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)
+
+ckpt_path = hf_hub_download(repo_id=REPO_ID, filename=CKPT_NAME)
+checkpoint = torch.load(ckpt_path, map_location=device)
+
+# 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:
+    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("✅ Load weights successfully!")
+         
+except Exception as e:
+    print(f"❌ Error when loading weights: {e}")
+    raise e
+
+model.to(device).eval()
+
+# --------- Initialize RAG & LLM ----------
+print("Initializing RAG Engine (LangChain)...")
+rag_engine = RAGEngine()
+print("RAG Engine ready.")
+
+print("Initializing Qwen3-0.6B(GGUF) LLM (LangChain)...")
+# Pass vector_store to LLMClient for RetrievalQA
+llm_client = LLMClient(vector_store=rag_engine.vector_store)
+print("LLM ready.")
+
+# --------- Helper functions ----------
+def is_url(text):
+    """Check if text is a URL"""
+    url_pattern = re.compile(
+        r'^https?://'  # http:// or https://
+        r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+[A-Z]{2,6}\.?|'  # domain...
+        r'localhost|'  # localhost...
+        r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})'  # ...or ip
+        r'(?::\d+)?'  # optional port
+        r'(?:/?|[/?]\S+)$', re.IGNORECASE)
+    return url_pattern.match(text) is not None
+
+def fetch_html_content(url, timeout=10):
+    """Fetch HTML content from URL (Raw HTML for Model) - Optimized with curl_cffi"""
+    try:
+        from curl_cffi import requests as cffi_requests
+        
+        # Impersonate Chrome to bypass basic anti-bots (Cloudflare, etc.)
+        response = cffi_requests.get(
+            url, 
+            impersonate="chrome", 
+            timeout=timeout
+        )
+        
+        # raise_for_status() equivalent
+        if response.status_code >= 400:
+            return None, f"Request error: {response.status_code}"
+            
+        return response.text, response.status_code
+        
+    except Exception as e:
+        # Fallback to standard requests if curl_cffi fails (unlikely) or simple error
+        return None, f"Fetch error: {str(e)}"
+
+def predict_single_text(text, text_type="text"):
+    """Predict for a single text input"""
+    # Tokenize
+    # Increased max_length to 512 to capture more HTML content
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        padding=True,
+        max_length=512 
+    )
+    # 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
+                has_attention = True
+            else:
+                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
+
+def combine_predictions(url_probs, html_probs, url_weight=0.3, html_weight=0.7):
+    """Combine URL and HTML content predictions"""
+    combined_probs = [
+        url_weight * url_probs[0] + html_weight * html_probs[0],  # benign
+        url_weight * url_probs[1] + html_weight * html_probs[1]   # phishing
+    ]
+    return combined_probs
+
+# --------- Inference function ----------
+def predict_fn(text: str):
+    if not text or not text.strip():
+        return "<div style='color: red; padding: 20px; text-align: center;'>⚠️ Please enter a URL or text to analyze.</div>"
+
+    # 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 (Raw HTML now)
+            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]  # 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 for 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)
+    
+    detailed_analysis = f"""
+<div style="font-family: Arial, sans-serif; max-width: 800px; margin: 0 auto; background: #1e1e1e; padding: 20px; border-radius: 15px;">
+<div style="background: linear-gradient(135deg, {'#8b0000' if predicted_class == 'phishing' else '#006400'} 0%, {'#dc143c' if predicted_class == 'phishing' else '#228b22'} 100%); padding: 25px; border-radius: 20px; color: white; text-align: center; margin-bottom: 20px; box-shadow: 0 8px 32px rgba(0,0,0,0.5); border: 2px solid {'#ff4444' if predicted_class == 'phishing' else '#44ff44'};">
+    <h2 style="margin: 0 0 10px 0; font-size: 28px; color: white;">🔍 {analysis_type}</h2>
+    <div style="font-size: 36px; font-weight: bold; margin: 10px 0; color: white;">
+        {predicted_class.upper()}
+    </div>
+    <div style="font-size: 18px; color: #f0f0f0;">
+        Confidence: {confidence:.1%}
+    </div>
+    <div style="margin-top: 15px; font-size: 14px; color: #e0e0e0;">
+        {'This appears to be a phishing attempt!' if predicted_class == 'phishing' else '✅ This appears to be legitimate content.'}
+    </div>
+</div>
+"""
+
+    if fetch_status:
+        detailed_analysis += f"""
+<div style="background: #2d2d2d; padding: 15px; border-radius: 10px; margin: 15px 0; border-left: 4px solid #4caf50; color: #e0e0e0;">
+    <strong>Fetch Status:</strong> {fetch_status}
+</div>
+"""
+    
+    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()  # 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"""
+## Top important tokens:
+<div style="background: #2d2d2d; padding: 15px; border-radius: 10px; margin: 15px 0; border-left: 4px solid #4caf50; color: #e0e0e0;">
+    <strong>Analysis Info:</strong> Found {len(token_analysis)} important tokens out of {len(tokens)} total tokens
+</div>
+<div style="font-family: Arial, sans-serif;">
+"""
+        
+        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: 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>
+"""
+        
+        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):
+        prediction_result = {LABELS[i]: float(probs[i]) for i in range(len(LABELS))}
+    else:
+        prediction_result = {f"class_{i}": float(p) for i, p in enumerate(probs)}
+    
+    return prediction_result, detailed_analysis
+
+# --------- RAG Inference function ----------
+def rag_predict_fn(text: str, model_selection: str):
+    if not text or not text.strip():
+        return "Please enter text to analyze."
+    
+    start_time = time.time()
+    
+    # Check if input is a URL
+    input_text = text.strip()
+    is_link = is_url(input_text)
+    
+    analysis_context = input_text
+    status_msg = ""
+    
+    analysis_context = input_text
+    status_msg = ""
+    
+    # 1. Direct URL Input
+    if is_link:
+        target_url = input_text
+        print(f"🌐 Detected Direct URL: {target_url}")
+        fetched_content, status = fetch_html_content(target_url)
+        
+        if fetched_content:
+            truncated_content = fetched_content[:4000] 
+            analysis_context = f"URL: {target_url}\n\nWebsite Content:\n{truncated_content}\n..."
+            status_msg = f"✅ Successfully fetched content from {target_url} (Status: {status})."
+        else:
+            analysis_context = f"URL: {target_url}\n\n(Could not fetch website content. Error: {status})"
+            status_msg = f"⚠️ Failed to fetch URL content: {status}"
+    
+    # 2. Text with Embedded URL
+    else:
+        # Regex to find the first URL in text
+        url_pattern = re.compile(r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+')
+        match = url_pattern.search(input_text)
+        
+        if match:
+            target_url = match.group(0)
+            print(f"📧 Detected Embedded URL: {target_url}")
+            fetched_content, status = fetch_html_content(target_url)
+            
+            if fetched_content:
+                truncated_content = fetched_content[:4000]
+                analysis_context = f"Input Text:\n{input_text}\n\n---\nExtracted URL Context ({target_url}):\n{truncated_content}\n..."
+                status_msg = f"✅ Found & Fetched embedded URL: {target_url} (Status: {status})"
+            else:
+                status_msg = f"⚠️ Found embedded URL {target_url} but could not fetch content: {status}"
+        else:
+            status_msg = "📝 Analyzing raw text input (No active links found)."
+
+
+    # Call LLM (which now handles retrieval internally via LangChain)
+    # Ensure llm_client is global or passed correctly. It is initialized globally in app.py
+    response = llm_client.analyze(analysis_context, model_selection=model_selection)
+    
+    end_time = time.time()
+    elapsed_time = end_time - start_time
+    
+    # Parse LLM Response (New Format)
+    classification = "UNKNOWN"
+    confidence = "N/A"
+    explanation = response
+    
+    # Simple parsing logic
+    lines = response.split('\n')
+    for line in lines:
+        line = line.strip()
+        if line.upper().startswith("CLASSIFICATION:"):
+            classification = line.split(":", 1)[1].strip().upper()
+        elif line.upper().startswith("CONFIDENCE SCORE:"):
+            confidence = line.split(":", 1)[1].strip()
+        elif line.upper().startswith("EXPLANATION:"):
+            explanation = line.split(":", 1)[1].strip()
+    
+    # If explanation is still the full response, try to clean it up if other fields were found
+    if classification != "UNKNOWN" and explanation == response:
+         # Fallback
+         pass
+
+    # Determine Color/Icon
+    if "PHISHING" in classification:
+        label = "PHISHING"
+        color_grad = "linear-gradient(135deg, #ff4b1f 0%, #ff9068 100%)"
+        icon = "⛔"
+        border_col = "#ff4b1f"
+    elif "BENIGN" in classification:
+        label = "BENIGN" 
+        color_grad = "linear-gradient(135deg, #11998e 0%, #38ef7d 100%)"
+        icon = "✅"
+        border_col = "#11998e"
+    else:
+        label = "UNCERTAIN"
+        color_grad = "linear-gradient(135deg, #f8b500 0%, #fceabb 100%)"
+        icon = "⚠️"
+        border_col = "#f8b500"
+
+    # HTML Output
+    html_output = f"""
+<div style="font-family: 'Segoe UI', Roboto, Helvetica, Arial, sans-serif; max-width: 800px; margin: 0 auto; background: #1e1e1e; padding: 25px; border-radius: 16px; box-shadow: 0 10px 30px rgba(0,0,0,0.5); border: 1px solid #333;">
+    <div style="background: {color_grad}; padding: 30px; border-radius: 12px; color: white; text-align: center; margin-bottom: 25px; box-shadow: 0 4px 15px rgba(0,0,0,0.3); position: relative; overflow: hidden;">
+        <div style="position: relative; z-index: 2;">
+            <h2 style="margin: 0 0 5px 0; font-size: 42px; font-weight: 800; letter-spacing: 1px; text-shadow: 0 2px 4px rgba(0,0,0,0.2);">{icon} {label}</h2>
+            <div style="font-size: 24px; font-weight: 600; opacity: 0.95; margin-bottom: 15px;">Confidence: {confidence}</div>
+            <div style="background: rgba(0,0,0,0.2); padding: 15px; border-radius: 8px; text-align: left; font-size: 16px; line-height: 1.5; backdrop-filter: blur(5px);">
+                <strong>Explanation:</strong><br>
+                {explanation}
+            </div>
+        </div>
+    </div>
+    
+    <div style="display: flex; justify-content: space-between; align-items: center; color: #888; font-size: 13px; padding: 0 10px;">
+        <div>
+            ⏱️ Processing Time: <b>{elapsed_time:.2f}s</b>
+        </div>
+        <div>
+            🛡️ CyberGuard AI Analysis via {model_selection}
+        </div>
+    </div>
+    
+    <div style="background: #2d2d2d; padding: 15px; border-radius: 8px; margin-top: 20px; border-left: 4px solid {border_col}; color: #ccc; font-size: 14px;">
+        <strong>Input Status:</strong> {status_msg}<br>
+        <span style="font-size: 12px; opacity: 0.7;">AI can make mistakes. Always verify critical URLs manually.</span>
+    </div>
+</div>
+"""
+    
+    return html_output
+
+# --------- Gradio UI ----------
+css_style="""
+.gradio-container {
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    background-color: #1e1e1e !important;
+    color: #ffffff !important;
+}
+/* Customize Buttons */
+.gradio-container button.primary, .gradio-container button.secondary {
+    background-color: #4a4a4a !important;
+    color: #ffffff !important;
+    border: 1px solid #666 !important;
+}
+.gradio-container button.primary:hover, .gradio-container button.secondary:hover {
+    background-color: #5a5a5a !important;
+    color: #ffffff !important;
+}
+/* Customize Textboxes (Inputs) */
+.gradio-container textarea, .gradio-container input {
+    background-color: #3d3d3d !important;
+    color: #ffffff !important;
+    border: 1px solid #666 !important;
+}
+/* Customize Blocks/Panels */
+.gradio-container .block {
+    background-color: #2d2d2d !important;
+    border: 1px solid #444 !important;
+}
+"""
+with gr.Blocks() as demo:
+    gr.HTML(f"<style>{css_style}</style>")
+    gr.Markdown("# 🛡️ Phishing Detector")
+    
+    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():
+                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 **Qwen3** + **LangChain** to explain *why* a message is suspicious.
+            
+            """)
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    # Model Selection
+                    model_selector = gr.Radio(
+                        choices=["Qwen3-32B (API)", "Qwen3-4B (Local)"],
+                        value="Qwen3-32B (API)",
+                        label="Model Selection",
+                        info="Select 'API' for better reasoning (requires Internet) or 'Local' for offline privacy."
+                    )
+                    
+                    rag_input = gr.Textbox(
+                        label="Suspicious Text/URL",
+                        placeholder="Paste the email content or URL here...",
+                        lines=5
+                    )
+                    
+                    btn_rag = gr.Button("🤖 Ask AI Assistant", variant="primary")
+                    
+                    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):
+                    # Changed from gr.Markdown to gr.HTML for custom styling
+                    rag_output = gr.HTML(label="AI Analysis")
+            
+            btn_rag.click(fn=rag_predict_fn, inputs=[rag_input, model_selector], outputs=rag_output)
+
+if __name__ == "__main__":
+    demo.launch(ssr_mode=False)