app.py

import gradio as gr
from googleapiclient.discovery import build
import google.generativeai as genai
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import re
import os
import numpy as np
import json
import sqlite3
from datetime import datetime
import hashlib
import io
import os
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request
from googleapiclient.discovery import build
from googleapiclient.http import MediaIoBaseDownload, MediaIoBaseUpload

GOOGLE_API_KEY = "AIzaSyASwqVh3ELFVKH-W3WuHtmjg3XgtwjJQKg"
SEARCH_ENGINE_ID = "f34f8a4816771488b"
GEMINI_API_KEY = "AIzaSyBXInbM_CAtquC9f80_DoAMQx6E2vE1lC8"
MODEL_PATH = "./vietnamese_fake_news_model"

genai.configure(api_key=GEMINI_API_KEY)

# Knowledge Base Configuration
KNOWLEDGE_BASE_DB = "knowledge_base.db"
CONFIDENCE_THRESHOLD = 0.95  # 95% Gemini confidence threshold for RAG knowledge base
ENABLE_KNOWLEDGE_BASE_SEARCH = True  # Enable knowledge base search with training data

# Enhanced RAG System Configuration
ENABLE_ENHANCED_RAG = True  # Enable enhanced RAG system for Google Drive
RAG_CONFIDENCE_THRESHOLD = 0.95  # 95% threshold for saving to RAG

# Cloud Storage Configuration
USE_CLOUD_STORAGE = False  # Disabled - using Enhanced RAG system instead (no duplicates)
CLOUD_STORAGE_TYPE = "google_drive"  # Options: "google_drive", "google_cloud", "local"
GOOGLE_DRIVE_FILE_ID = None  # Will be set when file is created

# Load Google Drive file ID if it exists
try:
    if os.path.exists('google_drive_file_id.txt'):
        with open('google_drive_file_id.txt', 'r') as f:
            GOOGLE_DRIVE_FILE_ID = f.read().strip()
        print(f"📁 Loaded Google Drive file ID: {GOOGLE_DRIVE_FILE_ID}")
except Exception as e:
    print(f"Could not load Google Drive file ID: {e}")
GOOGLE_CLOUD_BUCKET = "your-bucket-name"  # For Google Cloud Storage

print("Loading the DistilBERT model we trained...")
try:
    if os.path.exists(MODEL_PATH):
        tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
        model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
        print("DistilBERT model loaded successfully!")
    else:
        print(f"Model directory '{MODEL_PATH}' not found!")
        print("Our custom model isn't available, trying a backup model...")
        try:
            tokenizer = AutoTokenizer.from_pretrained("distilbert-base-multilingual-cased")
            model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-multilingual-cased", num_labels=2)
            print("Fallback DistilBERT model loaded successfully!")
        except Exception as fallback_error:
            print(f"Fallback model also failed: {fallback_error}")
            tokenizer = None
            model = None
except Exception as e:
    print(f"Error loading DistilBERT model: {e}")
    print("Something went wrong, trying the backup model...")
    try:
        tokenizer = AutoTokenizer.from_pretrained("distilbert-base-multilingual-cased")
        model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-multilingual-cased", num_labels=2)
        print("Fallback DistilBERT model loaded successfully!")
    except Exception as fallback_error:
        print(f"Fallback model also failed: {fallback_error}")
        tokenizer = None
        model = None

# --- CLOUD STORAGE FUNCTIONS ---
def get_google_drive_service():
    """Get authenticated Google Drive service for Hugging Face Spaces"""
    try:
        SCOPES = ['https://www.googleapis.com/auth/drive.file']
        creds = None
        
        # Check if running on Hugging Face Spaces
        import os
        is_hf_space = os.getenv('SPACE_ID') is not None
        
        if is_hf_space:
            # For Hugging Face Spaces, use environment variables
            client_id = os.getenv('GOOGLE_CLIENT_ID')
            client_secret = os.getenv('GOOGLE_CLIENT_SECRET')
            refresh_token = os.getenv('GOOGLE_REFRESH_TOKEN')
            
            if client_id and client_secret and refresh_token:
                creds = Credentials.from_authorized_user_info({
                    'client_id': client_id,
                    'client_secret': client_secret,
                    'refresh_token': refresh_token,
                    'token_uri': 'https://oauth2.googleapis.com/token'
                }, SCOPES)
            else:
                print("⚠️ Google Drive credentials not found in Hugging Face secrets")
                return None
        else:
            # For local development, use files
            if os.path.exists('token.json'):
                creds = Credentials.from_authorized_user_file('token.json', SCOPES)
            
            # If no valid credentials, request authorization
            if not creds or not creds.valid:
                if creds and creds.expired and creds.refresh_token:
                    creds.refresh(Request())
                else:
                    if os.path.exists('credentials.json'):
                        flow = InstalledAppFlow.from_client_secrets_file(
                            'credentials.json', SCOPES)
                        creds = flow.run_local_server(port=0)
                    else:
                        print("⚠️ credentials.json not found for local development")
                        return None
                
                # Save credentials for next run
                with open('token.json', 'w') as token:
                    token.write(creds.to_json())
        
        return build('drive', 'v3', credentials=creds)
    except Exception as e:
        print(f"Error setting up Google Drive: {e}")
        return None

def upload_to_google_drive(data, filename="knowledge_base.json"):
    """Upload knowledge base data to Google Drive"""
    try:
        service = get_google_drive_service()
        if not service:
            return None
        
        # Convert data to JSON
        json_data = json.dumps(data, ensure_ascii=False, indent=2)
        file_metadata = {
            'name': filename,
            'parents': []  # Root folder
        }
        
        media = MediaIoBaseUpload(
            io.BytesIO(json_data.encode('utf-8')),
            mimetype='application/json'
        )
        
        file = service.files().create(
            body=file_metadata,
            media_body=media,
            fields='id'
        ).execute()
        
        print(f"✅ Uploaded {filename} to Google Drive (ID: {file.get('id')})")
        return file.get('id')
        
    except Exception as e:
        print(f"Error uploading to Google Drive: {e}")
        return None

def download_from_google_drive(file_id):
    """Download knowledge base data from Google Drive"""
    try:
        service = get_google_drive_service()
        if not service:
            return []
        
        request = service.files().get_media(fileId=file_id)
        file_content = io.BytesIO()
        downloader = MediaIoBaseDownload(file_content, request)
        
        done = False
        while done is False:
            status, done = downloader.next_chunk()
        
        file_content.seek(0)
        data = json.loads(file_content.read().decode('utf-8'))
        
        print(f"✅ Downloaded knowledge base from Google Drive")
        return data
        
    except Exception as e:
        print(f"Error downloading from Google Drive: {e}")
        return []

def save_knowledge_base_cloud(data):
    """Save knowledge base to cloud storage"""
    if CLOUD_STORAGE_TYPE == "google_drive":
        file_id = upload_to_google_drive(data)
        if file_id:
            global GOOGLE_DRIVE_FILE_ID
            GOOGLE_DRIVE_FILE_ID = file_id
        return file_id is not None
    elif CLOUD_STORAGE_TYPE == "google_cloud":
        # TODO: Implement Google Cloud Storage
        print("Google Cloud Storage not implemented yet")
        return False
    return False

def load_knowledge_base_cloud():
    """Load knowledge base from cloud storage"""
    if CLOUD_STORAGE_TYPE == "google_drive" and GOOGLE_DRIVE_FILE_ID:
        return download_from_google_drive(GOOGLE_DRIVE_FILE_ID)
    elif CLOUD_STORAGE_TYPE == "google_cloud":
        # TODO: Implement Google Cloud Storage
        print("Google Cloud Storage not implemented yet")
        return []
    return []

# --- KNOWLEDGE BASE MANAGEMENT ---
def init_knowledge_base():
    """Initialize the SQLite knowledge base"""
    conn = sqlite3.connect(KNOWLEDGE_BASE_DB)
    cursor = conn.cursor()
    
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS knowledge_entries (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            content_hash TEXT UNIQUE,
            news_text TEXT,
            prediction TEXT,
            confidence REAL,
            search_results TEXT,
            gemini_analysis TEXT,
            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
            last_accessed TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
            access_count INTEGER DEFAULT 1
        )
    ''')
    
    conn.commit()
    conn.close()
    print("Knowledge base initialized successfully!")

def add_to_knowledge_base(news_text, prediction, confidence, search_results, gemini_analysis):
    """Add high-confidence result to knowledge base"""
    try:
        # Create content hash for deduplication
        content_hash = hashlib.md5(news_text.encode('utf-8')).hexdigest()
        
        if USE_CLOUD_STORAGE:
            # Add to cloud storage
            data = load_knowledge_base_cloud()
            
            # Check if entry already exists
            for entry in data:
                if entry.get('content_hash') == content_hash:
                    print(f"Entry already exists in cloud knowledge base (hash: {content_hash[:8]}...)")
                    return False
            
            # Create new entry
            new_entry = {
                'content_hash': content_hash,
                'news_text': news_text,
                'prediction': prediction,
                'confidence': confidence,
                'search_results': search_results,
                'gemini_analysis': gemini_analysis,
                'created_at': datetime.now().isoformat(),
                'last_accessed': datetime.now().isoformat(),
                'access_count': 1
            }
            
            # Add to data and save to cloud
            data.append(new_entry)
            success = save_knowledge_base_cloud(data)
            
            if success:
                print(f"✅ Added high-confidence result to cloud knowledge base (confidence: {confidence:.1%})")
                print(f"   Hash: {content_hash[:8]}...")
                print(f"   Prediction: {prediction}")
                return True
            else:
                return False
        else:
            # Add to local SQLite database
            conn = sqlite3.connect(KNOWLEDGE_BASE_DB)
            cursor = conn.cursor()
            
            # Check if entry already exists
            cursor.execute('SELECT id FROM knowledge_entries WHERE content_hash = ?', (content_hash,))
            if cursor.fetchone():
                print(f"Entry already exists in knowledge base (hash: {content_hash[:8]}...)")
                conn.close()
                return False
            
            # Insert new entry
            cursor.execute('''
                INSERT INTO knowledge_entries 
                (content_hash, news_text, prediction, confidence, search_results, gemini_analysis)
                VALUES (?, ?, ?, ?, ?, ?)
            ''', (
                content_hash,
                news_text,
                prediction,
                confidence,
                json.dumps(search_results, ensure_ascii=False),
                gemini_analysis
            ))
            
            conn.commit()
            conn.close()
            
            print(f"✅ Added high-confidence result to knowledge base (confidence: {confidence:.1%})")
            print(f"   Hash: {content_hash[:8]}...")
            print(f"   Prediction: {prediction}")
            return True
        
    except Exception as e:
        print(f"Error adding to knowledge base: {e}")
        return False

def search_knowledge_base(query_text, limit=5):
    """Search the knowledge base for similar entries"""
    try:
        if USE_CLOUD_STORAGE:
            # Search in cloud storage
            data = load_knowledge_base_cloud()
            if not data:
                return []
            
            # Simple text similarity search in JSON data
            results = []
            query_lower = query_text[:50].lower()
            
            for entry in data:
                if (query_lower in entry.get('news_text', '').lower() or 
                    query_lower in entry.get('gemini_analysis', '').lower()):
                    results.append((
                        entry['news_text'],
                        entry['prediction'],
                        entry['confidence'],
                        entry.get('search_results', []),
                        entry.get('gemini_analysis', ''),
                        entry.get('created_at', ''),
                        entry.get('access_count', 1)
                    ))
            
            # Sort by confidence and access count
            results.sort(key=lambda x: (x[2], x[6]), reverse=True)
            results = results[:limit]
            
            if results:
                print(f"📚 Found {len(results)} similar entries in cloud knowledge base")
                return results
            else:
                return []
        else:
            # Search in local SQLite database
            conn = sqlite3.connect(KNOWLEDGE_BASE_DB)
            cursor = conn.cursor()
            
            # Simple text similarity search (you can enhance this with embeddings later)
            cursor.execute('''
                SELECT news_text, prediction, confidence, search_results, gemini_analysis, 
                       created_at, access_count
                FROM knowledge_entries 
                WHERE news_text LIKE ? OR gemini_analysis LIKE ?
                ORDER BY confidence DESC, access_count DESC
                LIMIT ?
            ''', (f'%{query_text[:50]}%', f'%{query_text[:50]}%', limit))
            
            results = cursor.fetchall()
            
            # Update access count and last_accessed
            for result in results:
                cursor.execute('''
                    UPDATE knowledge_entries 
                    SET access_count = access_count + 1, last_accessed = CURRENT_TIMESTAMP
                    WHERE news_text = ?
                ''', (result[0],))
            
            conn.commit()
            conn.close()
            
            if results:
                print(f"📚 Found {len(results)} similar entries in knowledge base")
                return results
            else:
                return []
            
    except Exception as e:
        print(f"Error searching knowledge base: {e}")
        return []

def format_knowledge_for_rag(knowledge_results):
    """Format knowledge base results for RAG augmentation"""
    if not knowledge_results:
        return ""
    
    knowledge_summary = "\n=== KIẾN THỨC TƯƠNG TỰ TỪ CƠ SỞ DỮ LIỆU ===\n"
    
    for i, (news_text, prediction, confidence, search_results, gemini_analysis, created_at, access_count) in enumerate(knowledge_results, 1):
        knowledge_summary += f"\n{i}. Tin tức tương tự (Độ tin cậy: {confidence:.1%}, Lần truy cập: {access_count}):\n"
        knowledge_summary += f"   Nội dung: {news_text[:200]}...\n"
        knowledge_summary += f"   Kết luận: {prediction}\n"
        knowledge_summary += f"   Thời gian: {created_at}\n"
    
    knowledge_summary += "\n==========================================\n"
    return knowledge_summary

def get_knowledge_base_stats():
    """Get statistics about the knowledge base"""
    try:
        conn = sqlite3.connect(KNOWLEDGE_BASE_DB)
        cursor = conn.cursor()
        
        # Get total entries
        cursor.execute('SELECT COUNT(*) FROM knowledge_entries')
        total_entries = cursor.fetchone()[0]
        
        # Get entries by prediction
        cursor.execute('SELECT prediction, COUNT(*) FROM knowledge_entries GROUP BY prediction')
        prediction_counts = dict(cursor.fetchall())
        
        # Get average confidence
        cursor.execute('SELECT AVG(confidence) FROM knowledge_entries')
        avg_confidence = cursor.fetchone()[0] or 0
        
        # Get most accessed entries
        cursor.execute('SELECT news_text, access_count FROM knowledge_entries ORDER BY access_count DESC LIMIT 3')
        top_accessed = cursor.fetchall()
        
        conn.close()
        
        return {
            'total_entries': total_entries,
            'prediction_counts': prediction_counts,
            'avg_confidence': avg_confidence,
            'top_accessed': top_accessed
        }
        
    except Exception as e:
        print(f"Error getting knowledge base stats: {e}")
        return None

# Initialize knowledge base on startup
init_knowledge_base()

# Initialize Enhanced RAG System
if ENABLE_ENHANCED_RAG:
    try:
        from rag_news_manager import initialize_rag_system
        print("🚀 Initializing Enhanced RAG System...")
        if initialize_rag_system():
            print("✅ Enhanced RAG System initialized successfully!")
        else:
            print("⚠️ Enhanced RAG System initialization failed")
            print("   📋 This usually means Google Drive authentication needs to be refreshed")
            print("   🔧 To fix this, run: python setup_google_drive_rag.py")
            print("   📝 Or check if credentials.json and token.json are valid")
            print("   ⚠️  System will continue, but high-confidence news won't be saved to Google Drive")
            # Don't disable - allow retry during runtime
            # ENABLE_ENHANCED_RAG = False
    except ImportError as e:
        print(f"⚠️ Enhanced RAG System not available: {e}")
        print("   📋 Make sure rag_news_manager.py is in the same directory")
        # Don't disable - maybe module will be available later
        # ENABLE_ENHANCED_RAG = False
    except Exception as e:
        print(f"⚠️ Enhanced RAG System initialization error: {e}")
        error_msg = str(e).lower()
        if "invalid_grant" in error_msg or "bad request" in error_msg:
            print("   🔑 Google Drive token expired or invalid")
            print("   🔧 To fix: Run 'python setup_google_drive_rag.py' to re-authenticate")
        elif "credentials" in error_msg:
            print("   🔑 Google Drive credentials issue")
            print("   🔧 To fix: Make sure credentials.json exists and is valid")
        print("   ⚠️  System will continue, but RAG saving may not work until fixed")
        # Don't disable - allow retry during runtime
        # ENABLE_ENHANCED_RAG = False

def populate_knowledge_base_from_training_data():
    """Populate knowledge base with existing training data"""
    try:
        import pandas as pd
        
        # Load training data
        df = pd.read_csv('train_final.csv')
        print(f"📚 Loading {len(df)} training samples into knowledge base...")
        
        conn = sqlite3.connect(KNOWLEDGE_BASE_DB)
        cursor = conn.cursor()
        
        added_count = 0
        skipped_count = 0
        
        for index, row in df.iterrows():
            news_text = str(row['content'])
            label = int(row['label'])
            prediction = "REAL" if label == 0 else "FAKE"
            
            # Create content hash for deduplication
            content_hash = hashlib.md5(news_text.encode('utf-8')).hexdigest()
            
            # Check if entry already exists
            cursor.execute('SELECT id FROM knowledge_entries WHERE content_hash = ?', (content_hash,))
            if cursor.fetchone():
                skipped_count += 1
                continue
            
            # Create synthetic analysis for training data
            synthetic_analysis = f"""1. KẾT LUẬN: {prediction}

2. ĐỘ TIN CẬY: THẬT: {95 if prediction == 'REAL' else 5}% / GIẢ: {5 if prediction == 'REAL' else 95}%

3. PHÂN TÍCH CHI TIẾT:
- Nội dung: {'Tin tức được xác minh từ nguồn đào tạo' if prediction == 'REAL' else 'Tin tức giả được xác định từ nguồn đào tạo'}
- Nguồn tin: Dữ liệu huấn luyện đã được xác minh
- Ngữ cảnh: Mẫu từ bộ dữ liệu huấn luyện DistilBERT
- Ngôn ngữ: {'Ngôn ngữ khách quan, tin cậy' if prediction == 'REAL' else 'Ngôn ngữ có dấu hiệu tin giả'}
- Thời gian: Dữ liệu huấn luyện đã được kiểm chứng

4. CÁC DẤU HIỆU CẢNH BÁO: {'Không có dấu hiệu cảnh báo' if prediction == 'REAL' else 'Tin tức được xác định là giả từ nguồn đào tạo'}

5. KHUYẾN NGHỊ CHO NGƯỜI ĐỌC:
- Nguồn: Dữ liệu huấn luyện đã được xác minh
- Độ tin cậy: Cao (từ bộ dữ liệu đào tạo)
- Lưu ý: Mẫu từ tập huấn luyện DistilBERT"""
            
            # Insert training sample
            cursor.execute('''
                INSERT INTO knowledge_entries 
                (content_hash, news_text, prediction, confidence, search_results, gemini_analysis)
                VALUES (?, ?, ?, ?, ?, ?)
            ''', (
                content_hash,
                news_text,
                prediction,
                0.95,  # High confidence for training data
                json.dumps([], ensure_ascii=False),  # Empty search results for training data
                synthetic_analysis
            ))
            
            added_count += 1
            
            # Show progress every 1000 entries
            if added_count % 1000 == 0:
                print(f"   Added {added_count} entries...")
        
        conn.commit()
        conn.close()
        
        print(f"✅ Knowledge base populated successfully!")
        print(f"   📊 Added: {added_count} entries")
        print(f"   ⏭️ Skipped: {skipped_count} duplicates")
        print(f"   🎯 Total entries: {added_count}")
        
        return True
        
    except FileNotFoundError as e:
        print(f"⚠️ Training data file not found: {e}")
        print("   Knowledge base will start empty (this is OK)")
        return False
    except Exception as e:
        print(f"⚠️ Error populating knowledge base: {e}")
        print("   Knowledge base will start empty (this is OK)")
        return False

# Populate knowledge base with training data on startup (optional)
print("🚀 Populating knowledge base with training data...")
populate_knowledge_base_from_training_data()

CREDIBLE_SOURCES = {
    # Báo tổng hợp & phổ biến
    'vnexpress.net': 0.95,
    'tuoitre.vn': 0.95,
    'thanhnien.vn': 0.90,
    'dantri.com.vn': 0.90,
    'vietnamnet.vn': 0.80,
    'zing.vn': 0.85,
    'zingnews.vn': 0.85,
    'nld.com.vn': 0.90,
    'laodong.vn': 0.90,
    'kenh14.vn': 0.80,
    'soha.vn': 0.80,
    'baotintuc.vn': 0.85, 
    # Báo chính trị - xã hội & nhà nước
    'nhandan.vn': 0.90,
    'qdnd.vn': 0.90,
    'cand.com.vn': 0.95,
    'congan.com.vn': 0.95,
    'baochinhphu.vn': 0.95,
    'vnanet.vn': 0.90,
    'quochoi.vn': 0.95,
    'chinhphu.vn': 0.95,
    # Kinh tế, tài chính, đầu tư, công nghệ
    'cafef.vn': 0.85,
    'vietnamfinance.vn': 0.85,
    'baodautu.vn': 0.85,
    'ictnews.vietnamnet.vn': 0.85,
    # Giáo dục - Văn hóa - Khoa học
    'giaoducthoidai.vn': 0.90,
    'vov.vn': 0.90,
    'toquoc.vn': 0.90,
    # Cổng thông tin các bộ ngành
    'moh.gov.vn': 0.95,   # Bộ Y tế
    'mofa.gov.vn': 0.95,  # Bộ Ngoại giao
    'mard.gov.vn': 0.95,  # Bộ NN&PTNT
    'moc.gov.vn': 0.95,   # Bộ Xây dựng
    'mof.gov.vn': 0.95,   # Bộ Tài chính
    'most.gov.vn': 0.95,  # Bộ KH&CN (đúng domain là most.gov.vn, không phải mst)
    # Quốc tế & tham khảo
    'wikipedia.org': 0.95,
    'fifa.com': 0.95,
}

def clean_text(text):
    """Clean up the text before feeding it to our model"""
    if not isinstance(text, str):
        text = str(text)
    text = re.sub(r'\s+', ' ', text.strip())
    if len(text) < 10:
        text = "Tin tức ngắn: " + text
    return text

def predict_with_distilbert(text):
    """Run the text through our trained DistilBERT model to get a prediction"""
    if model is None or tokenizer is None:
        return None, None, None, None
    
    try:
        clean_text_input = clean_text(text)
        inputs = tokenizer(
            clean_text_input,
            return_tensors="pt",
            truncation=True,
            padding=True,
            max_length=512
        )
        
        
        with torch.no_grad():
            outputs = model(**inputs)
            predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
        
        
        real_score = predictions[0][0].item()
        fake_score = predictions[0][1].item()
        
        
        if real_score > fake_score:
            prediction = "REAL"
            confidence = real_score
        else:
            prediction = "FAKE"
            confidence = fake_score
        
        return prediction, confidence, real_score, fake_score
        
    except Exception as e:
        print(f"DistilBERT prediction error: {e}")
        return None, None, None, None

def process_search_results(items):
    
    search_results = []
    for item in items:
        search_results.append({
            'title': item.get('title', ''),
            'snippet': item.get('snippet', ''),
            'link': item.get('link', '')
        })
    return search_results

def google_search_fallback(news_text):
    print("Google Search is unavailable - using enhanced content analysis")
    # Enhanced content analysis without external search
    fake_indicators = ['giả', 'sai', 'không đúng', 'bịa đặt', 'lừa đảo', 'fake news', 'tin đồn', 'nghi vấn']
    real_indicators = ['chính thức', 'xác nhận', 'chính xác', 'đúng', 'verified', 'chính phủ', 'bộ y tế', 'cơ quan']
    
    text_lower = news_text.lower()
    fake_count = sum(1 for word in fake_indicators if word in text_lower)
    real_count = sum(1 for word in real_indicators if word in text_lower)
    
    # Create more detailed analysis
    analysis_details = []
    if fake_count > real_count:
        analysis_details.append("Nhiều từ khóa nghi ngờ được sử dụng")
    elif real_count > fake_count:
        analysis_details.append("Có từ khóa xác thực từ nguồn chính thức")
    
    # Check for other patterns
    if len(news_text) < 100:
        analysis_details.append("Tin tức quá ngắn, thiếu thông tin chi tiết")
    if '!' in news_text or '!!!' in news_text:
        analysis_details.append("Sử dụng dấu chấm than thái quá")
    
    snippet = f"Phân tích nội dung: {fake_count} từ nghi ngờ, {real_count} từ xác thực. "
    snippet += "; ".join(analysis_details) if analysis_details else "Không phát hiện dấu hiệu đặc biệt"
    
    return [{
        'title': 'Phân tích nội dung chi tiết (không có tìm kiếm Google)',
        'snippet': snippet,
        'link': 'content-analysis-only'
    }]



def google_search(news_text):
    """Search Google for information about the news - general search without prioritizing specific sites"""
    try:
        service = build("customsearch", "v1", developerKey=GOOGLE_API_KEY)
        
        # Extract keywords from news text - improved for Vietnamese
        # Split into words (not individual characters)
        words = news_text.split()
        
        # Filter out very short words and keep important ones
        important_words = [w for w in words if len(w) > 2][:10]  # Top 10 important words
        
        # Create search query from the text
        main_query = ' '.join(important_words)
        
        # Also try with the first 100 characters as exact phrase
        short_query = news_text[:100].strip()
        
        print(f"📝 News text preview: {news_text[:100]}...")
        
        # Prepare search queries
        search_queries = []
        if short_query:
            search_queries.append(f'"{short_query}"')  # Exact phrase search
        if main_query:
            search_queries.append(main_query)  # Keyword search
        
        print(f"🔍 Search queries prepared: {len(search_queries)}")
        for idx, q in enumerate(search_queries, 1):
            print(f"   Query {idx}: {q[:80]}...")
        
        all_results = []
        
        for i, search_query in enumerate(search_queries):
            if not search_query.strip():
                continue
                
            print(f"🔍 Search {i+1}: '{search_query[:80]}...'")
            
            try:
                # Request 10 results per query
                result = service.cse().list(
                    q=search_query,
                    cx=SEARCH_ENGINE_ID,
                    num=10
                ).execute()
                
                if 'items' in result and result['items']:
                    all_results.extend(result['items'])
                    print(f"✅ Found {len(result['items'])} results (total: {len(all_results)})")
                    
                    # If we have enough results, stop searching
                    if len(all_results) >= 15:  # Get a bit more for diversity filtering
                        break
            except Exception as e:
                print(f"Search error: {e}")
                continue
        
        if all_results:
            # Filter for diversity - max 2 results per domain
            diverse_results = []
            domain_count = {}
            
            for item in all_results:
                # Extract domain from URL
                url = item.get('link', '')
                domain = url.split('/')[2] if len(url.split('/')) > 2 else url
                
                # Count results per domain
                if domain not in domain_count:
                    domain_count[domain] = 0
                
                # Only add if we have less than 2 from this domain
                if domain_count[domain] < 2:
                    diverse_results.append(item)
                    domain_count[domain] += 1
                
                # Stop when we have 10 diverse results
                if len(diverse_results) >= 10:
                    break
            
            print(f"✅ Returning {len(diverse_results)} diverse results from {len(domain_count)} domains")
            return process_search_results(diverse_results)
        
        print("No results found, using fallback...")
        return google_search_fallback(news_text)
            
    except Exception as e:
        print(f"Google Search error: {e}")
        print(f"Error type: {type(e).__name__}")
        
        
        error_str = str(e).lower()
        if any(keyword in error_str for keyword in ["403", "blocked", "quota", "limit", "exceeded"]):
            print("Google Search API blocked/quota exceeded, using fallback...")
            # Return error information along with fallback results
            fallback_results = google_search_fallback(news_text)
            return {
                'results': fallback_results,
                'error': 'QUOTA_EXCEEDED',
                'error_message': 'Google Search API quota exceeded. Using content analysis only.',
                'error_details': str(e)
            }
        elif "invalid" in error_str or "unauthorized" in error_str:
            print("API key issue, using fallback...")
            fallback_results = google_search_fallback(news_text)
            return {
                'results': fallback_results,
                'error': 'API_KEY_INVALID',
                'error_message': 'Google Search API key invalid. Using content analysis only.',
                'error_details': str(e)
            }
        else:
            print("Unknown Google Search error, using fallback...")
            fallback_results = google_search_fallback(news_text)
            return {
                'results': fallback_results,
                'error': 'UNKNOWN_ERROR',
                'error_message': 'Google Search failed. Using content analysis only.',
                'error_details': str(e)
            }

def analyze_sources(search_results):
    """Check how trustworthy the news sources are - using AVERAGE credibility"""
    if not search_results:
        return 0.50, 0.20, "No sources found", []
    
    
    credible_count = 0
    total_sources = len(search_results)
    found_sources = []
    credible_sources_found = []
    credibility_scores = []  # Track individual credibility scores
    
    for result in search_results:
        domain = result['link'].split('/')[2] if '//' in result['link'] else ''
        found_sources.append(domain)
        
        # Check if this domain matches any credible source
        found_credible = False
        for source, credibility in CREDIBLE_SOURCES.items():
            if source in domain:
                credible_count += 1
                credible_sources_found.append(f"{source} ({credibility:.0%})")
                credibility_scores.append(credibility)  # Add actual credibility score
                found_credible = True
                break
        
        # If not credible, assign 50% (0.50)
        if not found_credible:
            credibility_scores.append(0.50)
    
    # Calculate AVERAGE credibility of all sources (credible sites keep their %, non-credible = 50%)
    source_credibility = sum(credibility_scores) / len(credibility_scores) if credibility_scores else 0.50
    
    print(f"📊 Source Credibility Calculation:")
    print(f"   Total sources: {total_sources}")
    print(f"   Credible sources: {credible_count}")
    print(f"   Non-credible sources: {total_sources - credible_count} (each counts as 50%)")
    print(f"   Individual scores: {[f'{s:.0%}' for s in credibility_scores]}")
    print(f"   Average credibility: {source_credibility:.1%}")
    
    popularity_score = min(1.0, total_sources / 5.0)  # Normalize to 0-1
    
    # Create a summary of what we found
    if source_credibility > 0.7:
        credibility_text = f"High credibility: {credible_count}/{total_sources} sources from reputable outlets (avg: {source_credibility:.0%})"
    elif source_credibility > 0.4:
        credibility_text = f"Medium credibility: {credible_count}/{total_sources} sources from reputable outlets (avg: {source_credibility:.0%})"
    else:
        credibility_text = f"Low credibility: {credible_count}/{total_sources} sources from reputable outlets (avg: {source_credibility:.0%})"
    
    return source_credibility, popularity_score, credibility_text, found_sources, credible_sources_found

def analyze_source_support(news_text, search_results):
    """Check if the search results agree or disagree with the news"""
    if not search_results:
        return 0.5, "No sources to analyze"
    
    support_count = 0
    contradict_count = 0
    total_sources = len(search_results)
    
    # Look for years mentioned in the news
    import re
    news_years = re.findall(r'\b(20\d{2})\b', news_text)
    news_year = news_years[0] if news_years else None
    
    for result in search_results:
        title_snippet = (result.get('title', '') + ' ' + result.get('snippet', '')).lower()
        
        # See if the years match up
        if news_year:
            source_years = re.findall(r'\b(20\d{2})\b', title_snippet)
            if source_years and news_year not in source_years:
                contradict_count += 1
                continue
        
        # Look for words that suggest agreement or disagreement
        support_keywords = ['confirm', 'verify', 'true', 'accurate', 'correct', 'xác nhận', 'chính xác', 'đúng']
        contradict_keywords = ['false', 'fake', 'incorrect', 'wrong', 'sai', 'giả', 'không đúng']
        
        support_score = sum(1 for keyword in support_keywords if keyword in title_snippet)
        contradict_score = sum(1 for keyword in contradict_keywords if keyword in title_snippet)
        
        if contradict_score > support_score:
            contradict_count += 1
        elif support_score > contradict_score:
            support_count += 1
        else:
            # If unclear, assume slight support
            support_count += 0.5
    
    support_ratio = support_count / total_sources if total_sources > 0 else 0.5
    
    if support_ratio > 0.7:
        support_text = f"Sources strongly support the news: {support_count:.1f}/{total_sources} sources confirm"
    elif support_ratio > 0.4:
        support_text = f"Sources mixed: {support_count:.1f}/{total_sources} sources support, {contradict_count} contradict"
    else:
        support_text = f"Sources contradict the news: {contradict_count}/{total_sources} sources contradict"
    
    return support_ratio, support_text

def analyze_with_gemini(news_text, search_results, distilbert_prediction, distilbert_confidence):
    """Use Gemini AI to analyze the news and compare with our model results"""
    try:
        # Knowledge base search with training data
        if ENABLE_KNOWLEDGE_BASE_SEARCH:
            print("🔍 Searching knowledge base for similar entries...")
            knowledge_results = search_knowledge_base(news_text, limit=2)  # Reduced to 2 for speed
            knowledge_context = format_knowledge_for_rag(knowledge_results)
        else:
            knowledge_context = ""
        # Try to use the latest Gemini model available
        try:
            model = genai.GenerativeModel('gemini-2.0-flash-exp')
        except:
            try:
                model = genai.GenerativeModel('gemini-2.5-flash')
            except:
                try:
                    model = genai.GenerativeModel('gemini-1.5-pro')
                except:
                    model = genai.GenerativeModel('gemini-1.5-flash')
        
        # Format the search results for Gemini (limit to top 3 for speed)
        search_summary = ""
        if search_results:
            search_summary = "Kết quả tìm kiếm Google:\n"
            for i, result in enumerate(search_results[:3], 1):  # Reduced from 5 to 3
                search_summary += f"{i}. {result['title']}\n   {result['snippet']}\n   Nguồn: {result['link']}\n\n"
        else:
            search_summary = "Không tìm thấy kết quả tìm kiếm Google cho tin tức này. Điều này có thể do API bị giới hạn hoặc tin tức quá mới/chưa được đăng tải."
        
        # Note: We're not including DistilBERT results to keep Gemini analysis independent
        
        prompt = f"""
Bạn là một chuyên gia phân tích tin tức chuyên nghiệp. Hãy phân tích chi tiết tin tức sau và đánh giá độ tin cậy của nó:

"{news_text}"

{search_summary}

{knowledge_context}

Hãy thực hiện phân tích toàn diện theo các tiêu chí sau:

1. Phân tích nội dung: Kiểm tra tính logic, mâu thuẫn, ngôn ngữ cảm xúc thái quá
2. Phân tích nguồn tin: Đánh giá uy tín và độ tin cậy của nguồn
3. Phân tích ngữ cảnh: So sánh với thông tin có sẵn và kiến thức thực tế
4. Phân tích ngôn ngữ: Tìm dấu hiệu của tin giả như từ ngữ gây sốc, cảm xúc
5. Phân tích thời gian: Kiểm tra tính hợp lý về mặt thời gian

Trả lời theo định dạng sau (chỉ bằng tiếng Việt, viết chi tiết và chuyên nghiệp):

1. KẾT LUẬN: [THẬT/GIẢ/KHÔNG XÁC ĐỊNH]

2. ĐỘ TIN CẬY: [THẬT: X% / GIẢ: Y%] (Trong đó X% là độ tin cậy tin THẬT, Y% là độ tin cậy tin GIẢ, X+Y=100%)

3. PHÂN TÍCH CHI TIẾT:
- Nội dung: [Phân tích chi tiết về nội dung tin tức]
- Nguồn tin: [Đánh giá về nguồn và độ tin cậy]
- Ngữ cảnh: [So sánh với thông tin có sẵn]
- Ngôn ngữ: [Phân tích cách sử dụng từ ngữ]
- Thời gian: [Kiểm tra tính hợp lý về mặt thời gian]

4. CÁC DẤU HIỆU CẢNH BÁO: [Liệt kê các dấu hiệu đáng ngờ nếu có]

5. KHUYẾN NGHỊ CHO NGƯỜI ĐỌC:
- [Hướng dẫn cụ thể để kiểm chứng thông tin]
- [Các nguồn tin đáng tin cậy để tham khảo]
- [Cách phân biệt tin thật và tin giả]

QUAN TRỌNG: Trong phần "ĐỘ TIN CẬY", hãy cung cấp tỷ lệ phần trăm chính xác dựa trên phân tích của bạn. Ví dụ: "THẬT: 95% / GIẢ: 5%" nghĩa là 95% tin tức này là THẬT, 5% là GIẢ.

Viết chi tiết, chuyên nghiệp và hữu ích cho người đọc.
"""
        
        print("Calling Gemini API...")
        print(f"DEBUG - News text being analyzed: {news_text}")
        print(f"DEBUG - Search results count: {len(search_results)}")
        if search_results:
            print(f"DEBUG - First search result title: {search_results[0].get('title', 'No title')}")
        
        # Use settings optimized for faster processing
        generation_config = genai.types.GenerationConfig(
            temperature=0.3,  # Lower for more consistent results
            top_p=0.8,        # Reduced for faster processing
            top_k=20,         # Reduced for faster processing
            max_output_tokens=1000  # Reduced for faster responses
        )
        response = model.generate_content(prompt, generation_config=generation_config)
        print("Gemini API response received successfully")
        return response.text
        
    except Exception as e:
        print(f"Gemini analysis error: {e}")
        print(f"Error type: {type(e).__name__}")
        
        # If we hit the API limit, provide a basic analysis
        if "429" in str(e) or "quota" in str(e).lower():
            print("Gemini API quota exceeded, providing enhanced fallback analysis...")
            
            # Enhanced analysis based on content patterns
            fake_patterns = ['giả', 'sai', 'không đúng', 'bịa đặt', 'lừa đảo', 'fake news', 'tin đồn']
            real_patterns = ['chính thức', 'xác nhận', 'chính xác', 'đúng', 'verified', 'chính phủ', 'bộ y tế']
            
            news_lower = news_text.lower()
            fake_score = sum(1 for pattern in fake_patterns if pattern in news_lower)
            real_score = sum(1 for pattern in real_patterns if pattern in news_lower)
            
            # Adjust prediction based on patterns
            if fake_score > real_score and distilbert_prediction == 'FAKE':
                confidence_boost = "Cao (có từ khóa nghi ngờ)"
            elif real_score > fake_score and distilbert_prediction == 'REAL':
                confidence_boost = "Cao (có từ khóa xác thực)"
            else:
                confidence_boost = "Trung bình"
            
            # Create detailed fallback analysis
            conclusion = 'THẬT' if distilbert_prediction == 'REAL' else 'GIẢ' if distilbert_prediction == 'FAKE' else 'KHÔNG XÁC ĐỊNH'
            
            # Enhanced analysis based on content patterns
            suspicious_patterns = []
            if fake_score > 0:
                suspicious_patterns.append(f"Tìm thấy {fake_score} từ khóa nghi ngờ")
            if real_score > 0:
                suspicious_patterns.append(f"Tìm thấy {real_score} từ khóa xác thực")
            
            warning_signs = []
            if 'cảnh báo' in news_lower or 'nguy hiểm' in news_lower:
                warning_signs.append("Sử dụng từ ngữ gây sợ hãi")
            if 'ngay lập tức' in news_lower or 'khẩn cấp' in news_lower:
                warning_signs.append("Tạo cảm giác cấp bách không cần thiết")
            if len(news_text) < 100:
                warning_signs.append("Tin tức quá ngắn, thiếu thông tin chi tiết")
            
            fallback_analysis = f"""1. KẾT LUẬN: {conclusion}

2. ĐỘ TIN CẬY: {'THẬT: 5% / GIẢ: 95%' if conclusion == 'GIẢ' else 'THẬT: 95% / GIẢ: 5%' if conclusion == 'THẬT' else 'THẬT: 50% / GIẢ: 50%'}

3. PHÂN TÍCH CHI TIẾT:
- Nội dung: {'Tin tức có vẻ hợp lý' if distilbert_prediction == 'REAL' else 'Tin tức có nhiều dấu hiệu đáng ngờ' if distilbert_prediction == 'FAKE' else 'Nội dung không rõ ràng'}
- Nguồn tin: Google Search không khả dụng (hết quota) - không thể kiểm tra nguồn
- Ngữ cảnh: Phân tích từ khóa: {confidence_boost}
- Ngôn ngữ: {'Ngôn ngữ trung tính' if fake_score == real_score else 'Có dấu hiệu cảm xúc thái quá' if fake_score > real_score else 'Ngôn ngữ khách quan'}
- Thời gian: Không thể xác minh do thiếu thông tin bổ sung

4. CÁC DẤU HIỆU CẢNH BÁO: 
{chr(10).join([f"- {sign}" for sign in warning_signs]) if warning_signs else "- Không phát hiện dấu hiệu cảnh báo rõ ràng"}

5. KHUYẾN NGHỊ CHO NGƯỜI ĐỌC:
- Kiểm tra nguồn: Tìm kiếm thông tin tương tự trên các trang báo uy tín như VnExpress, Tuổi Trẻ, Thanh Niên
- Xác minh thời gian: Kiểm tra xem tin tức có được đăng tải đồng thời trên nhiều nguồn không
- Đánh giá ngôn ngữ: Tránh chia sẻ tin tức có ngôn ngữ cảm xúc thái quá hoặc tạo cảm giác cấp bách
- Lưu ý: Do hệ thống API tạm thời không khả dụng, kết quả phân tích có thể không hoàn toàn chính xác"""
            return fallback_analysis
        
        # For other errors, see what models are available
        try:
            models = genai.list_models()
            print("Available models:")
            for model in models:
                if 'gemini' in model.name.lower():
                    print(f"  - {model.name}")
        except Exception as list_error:
            print(f"Could not list models: {list_error}")
        return f"Lỗi phân tích Gemini: {e}"

def extract_gemini_percentage(gemini_analysis):
    """Extract percentage confidence from Gemini analysis - Enhanced with multiple patterns"""
    try:
        gemini_lower = gemini_analysis.lower()
        
        # Look for the confidence percentage pattern
        import re
        
        # Pattern 1: "ĐỘ TIN CẬY: THẬT: X% / GIẢ: Y%" format (flexible spacing)
        patterns = [
            # Standard format with "độ tin cậy"
            r'độ\s*tin\s*cậy[:\s]*thật[:\s]*(\d+)\s*%[:\s/]*giả[:\s]*(\d+)\s*%',
            # Reversed order
            r'độ\s*tin\s*cậy[:\s]*giả[:\s]*(\d+)\s*%[:\s/]*thật[:\s]*(\d+)\s*%',
            # Without "độ tin cậy" prefix
            r'thật[:\s]*(\d+)\s*%[:\s/]*giả[:\s]*(\d+)\s*%',
            r'giả[:\s]*(\d+)\s*%[:\s/]*thật[:\s]*(\d+)\s*%',
            # With ":" separator
            r'thật:\s*(\d+)\s*%\s*/\s*giả:\s*(\d+)\s*%',
            r'giả:\s*(\d+)\s*%\s*/\s*thật:\s*(\d+)\s*%',
        ]
        
        for pattern in patterns:
            match = re.search(pattern, gemini_lower)
            if match:
                percent1 = int(match.group(1))
                percent2 = int(match.group(2))
                
                # Determine which is real and which is fake based on context
                # Check which group comes first in the match
                match_text = match.group(0).lower()
                if 'thật' in match_text[:len(match_text)//2]:
                    # "thật" appears first
                    real_percent = percent1
                    fake_percent = percent2
                elif 'giả' in match_text[:len(match_text)//2]:
                    # "giả" appears first, so swap
                    fake_percent = percent1
                    real_percent = percent2
                else:
                    # Can't determine from context, assume first is real
                    real_percent = percent1
                    fake_percent = percent2
                
                # Normalize to ensure they're between 0-100
                real_percent = min(100, max(0, real_percent))
                fake_percent = min(100, max(0, fake_percent))
                
                # Convert to 0-1 scale
                real_percent = real_percent / 100
                fake_percent = fake_percent / 100
                
                # If they don't add up to 1, normalize
                total = real_percent + fake_percent
                if total > 0:
                    real_percent = real_percent / total
                    fake_percent = fake_percent / total
                else:
                    real_percent = 0.5
                    fake_percent = 0.5
                    
                print(f"✅ Extracted Gemini percentages: {real_percent:.1%} real, {fake_percent:.1%} fake")
                return real_percent, fake_percent
        
        # Fallback: try to find individual percentages in separate lines
        # Look for "THẬT: X%" and "GIẢ: Y%" on separate lines or separated
        real_match = re.search(r'thật[:\s]*(\d+)\s*%', gemini_lower)
        fake_match = re.search(r'giả[:\s]*(\d+)\s*%', gemini_lower)
        
        if real_match and fake_match:
            real_percent = min(100, max(0, int(real_match.group(1)))) / 100
            fake_percent = min(100, max(0, int(fake_match.group(1)))) / 100
            
            # Normalize
            total = real_percent + fake_percent
            if total > 0:
                real_percent = real_percent / total
                fake_percent = fake_percent / total
            else:
                real_percent = 0.5
                fake_percent = 0.5
                
            print(f"✅ Extracted Gemini percentages (fallback): {real_percent:.1%} real, {fake_percent:.1%} fake")
            return real_percent, fake_percent
            
        # Last resort: Look for any percentage numbers near "thật" or "giả"
        # Find all percentage patterns
        all_percentages = re.findall(r'(\d+)\s*%', gemini_lower)
        if len(all_percentages) >= 2:
            # Try to find context around percentages
            for i, match_obj in enumerate(re.finditer(r'(\d+)\s*%', gemini_lower)):
                start_pos = max(0, match_obj.start() - 20)
                end_pos = min(len(gemini_lower), match_obj.end() + 20)
                context = gemini_lower[start_pos:end_pos]
                
                percent_val = int(match_obj.group(1))
                if 'thật' in context and i == 0:
                    real_percent = min(100, max(0, percent_val)) / 100
                elif 'giả' in context and i == 1:
                    fake_percent = min(100, max(0, percent_val)) / 100
                elif 'thật' in context:
                    real_percent = min(100, max(0, percent_val)) / 100
                elif 'giả' in context:
                    fake_percent = min(100, max(0, percent_val)) / 100
            
            if 'real_percent' in locals() and 'fake_percent' in locals():
                # Normalize
                total = real_percent + fake_percent
                if total > 0:
                    real_percent = real_percent / total
                    fake_percent = fake_percent / total
                else:
                    real_percent = 0.5
                    fake_percent = 0.5
                print(f"✅ Extracted Gemini percentages (last resort): {real_percent:.1%} real, {fake_percent:.1%} fake")
                return real_percent, fake_percent
            
        print(f"⚠️ Could not extract Gemini percentages from analysis")
        print(f"   Analysis preview: {gemini_analysis[:200]}...")
        return None, None
        
    except Exception as e:
        print(f"❌ Error extracting Gemini percentages: {e}")
        import traceback
        print(f"   Traceback: {traceback.format_exc()}")
        return None, None

def calculate_combined_confidence(distilbert_prediction, distilbert_confidence, source_credibility, popularity_score, gemini_analysis, source_support=0.5, skip_google_search=False, num_search_results=0):
    """Calculate combined confidence using multi-tier system:

    SPECIAL CASES (Priority Order):
    1. Gemini ≥95% (real OR fake) → Final Score = Gemini Max Confidence
    2. Google Search ≥95% (real) → Final Score = Google Search Score
    3. Google Search ≤5% (fake) → Final Score = Google Search Score
    4. Cả hai ≥85% → Final Score = (Gemini + Google Search) / 2
    5. Default: Weighted average (DistilBERT 35%, Gemini 35%, Google Search 30%)

    WEIGHTS (when Google Search available):
    - DistilBERT: 35% (or 52.5% if Google Search skipped)
    - Gemini AI: 35% (or 47.5% if Google Search skipped)
    - Google Search: 30% (or 0% if skipped)

    Note: Gemini score uses MAX confidence (≥95% in either direction triggers override)
    """
    
    # Adjust weights if Google Search is skipped
    if skip_google_search:
        distilbert_weight = 0.525  # 52.5%
        gemini_weight = 0.475  # 47.5%
        google_weight = 0.0  # 0%
        print("⚠️ Google Search skipped - Using adjusted weights: DistilBERT 52.5%, Gemini 47.5%")
    else:
        distilbert_weight = 0.35  # 35%
        gemini_weight = 0.35  # 35%
        google_weight = 0.30  # 30%
    
    # 1. DISTILBERT SCORE
    if distilbert_prediction == "REAL":
        distilbert_score = distilbert_confidence
    else:
        distilbert_score = 1 - distilbert_confidence
    
    print(f"DistilBERT Score: {distilbert_score:.3f} ({distilbert_weight*100:.1f}% weight)")
    
    # 2. GEMINI AI SCORE
    gemini_lower = gemini_analysis.lower()
    gemini_direction = "UNKNOWN"  # Initialize direction
    
    # Try to extract percentage from Gemini analysis first
    gemini_real_percent, gemini_fake_percent = extract_gemini_percentage(gemini_analysis)
    
    if gemini_real_percent is not None and gemini_fake_percent is not None:
        # Determine which direction Gemini is more confident about
        if gemini_real_percent > gemini_fake_percent:
            # Gemini thinks it's REAL - use real percentage
            gemini_score = gemini_real_percent
            gemini_direction = "REAL"
        else:
            # Gemini thinks it's FAKE - use fake percentage  
            gemini_score = gemini_fake_percent
            gemini_direction = "FAKE"
        print(f"Gemini Score (from percentage): {gemini_score:.3f} ({gemini_weight*100:.1f}% weight) - {gemini_real_percent:.1%} real, {gemini_fake_percent:.1%} fake")
        print(f"  → Gemini direction: {gemini_direction} with {gemini_score:.1%} confidence (triggers override if ≥95%)")
    else:
        # Fallback to conclusion analysis
        conclusion_score = 0.5  # Default neutral
        if "kết luận: giả" in gemini_lower or "kết luận: fake" in gemini_lower:
            conclusion_score = 0.1  # Very low for FAKE
            print("Gemini Conclusion: FAKE")
        elif "kết luận: thật" in gemini_lower or "kết luận: real" in gemini_lower:
            conclusion_score = 0.9  # Very high for REAL
            print("Gemini Conclusion: REAL")
        elif "giả" in gemini_lower and "kết luận" in gemini_lower:
            # Check if "giả" appears near "kết luận"
            conclusion_start = gemini_lower.find("kết luận")
            if conclusion_start != -1:
                conclusion_section = gemini_lower[conclusion_start:conclusion_start + 50]
                if "giả" in conclusion_section:
                    conclusion_score = 0.1
                    print("Gemini Conclusion: FAKE (detected in conclusion section)")
                elif "thật" in conclusion_section:
                    conclusion_score = 0.9
                    print("Gemini Conclusion: REAL (detected in conclusion section)")
        
        # Additional analysis indicators
        fake_indicators = ["giả", "fake", "vô lý", "phi thực tế", "absurd", "preposterous", "impossible", 
                          "không thể xảy ra", "không có căn cứ", "tin giả"]
        real_indicators = ["thật", "real", "chính xác", "đúng", "xác nhận", "verified", "đáng tin cậy"]
        
        fake_count = sum(1 for indicator in fake_indicators if indicator in gemini_lower)
        real_count = sum(1 for indicator in real_indicators if indicator in gemini_lower)
        
        # Adjust based on analysis indicators (but conclusion takes priority)
        if fake_count > real_count:
            analysis_adjustment = -0.2
            print(f"Gemini Analysis: {fake_count} fake indicators vs {real_count} real indicators")
        elif real_count > fake_count:
            analysis_adjustment = 0.2
            print(f"Gemini Analysis: {real_count} real indicators vs {fake_count} fake indicators")
        else:
            analysis_adjustment = 0.0
        
        gemini_score = max(0.1, min(0.9, conclusion_score + analysis_adjustment))
        print(f"Gemini Score (from conclusion): {gemini_score:.3f} ({gemini_weight*100:.1f}% weight)")
    
    # 3. GOOGLE SEARCH SCORE
    if skip_google_search:
        google_search_score = 0
        google_search_real = False  # Cannot determine if Google Search indicates real news
        google_search_fake = False  # Cannot determine if Google Search indicates fake news
        print(f"Google Search Score: 0.000 (0% weight - SKIPPED due to short input)")
    else:
        # Calculate Google Search score based on credibility and support
        # Source credibility component (max 0.75)
        credibility_component = source_credibility * 0.75  # Convert to 0-0.75 scale

        # Source support component (max 0.75)
        support_component = source_support * 0.75  # Convert to 0-0.75 scale

        # Base score (0.25) + components, but CAP at 1.0 (100%)
        google_search_score = min(credibility_component + support_component + 0.25, 1.0)

        # If Gemini strongly says FAKE, reduce Google Search score
        if gemini_score < 0.3:  # Gemini says FAKE (low score)
            google_search_score = min(google_search_score, 0.4)  # Cap at 0.4 when Gemini says fake
            print(f"Google Search Score: {google_search_score:.3f} ({google_weight*100:.1f}% weight - {num_search_results} results) - Credibility: {source_credibility:.2f}, Support: {source_support:.2f} - CAPPED due to Gemini FAKE")
        else:
            print(f"Google Search Score: {google_search_score:.3f} ({google_weight*100:.1f}% weight - {num_search_results} results) - Credibility: {source_credibility:.2f}, Support: {source_support:.2f}")

        # Determine if Google Search indicates high confidence real or fake
        google_search_real = google_search_score >= 0.95  # ≥95% indicates strong evidence for real news
        google_search_fake = google_search_score <= 0.05  # ≤5% indicates strong evidence for fake news

    # 4. CHECK FOR SPECIAL CASES FROM SPREADSHEET (in priority order)

    # Case 1: Gemini ≥95% (real OR fake) → Final Score = Gemini Score
    if gemini_score >= 0.95:
        print(f"🚀 GEMINI OVERRIDE TRIGGERED: {gemini_score:.1%} ≥ 95%")
        
        # Use the actual Gemini score (not max, but the direction Gemini chose)
        if gemini_real_percent is not None and gemini_fake_percent is not None:
            if gemini_real_percent > gemini_fake_percent:
                # Gemini thinks it's REAL with high confidence
                final_confidence = gemini_real_percent
                print(f"🎯 GEMINI ≥95% REAL: Final Score = Gemini Real Confidence ({gemini_real_percent:.1%})")
            else:
                # Gemini thinks it's FAKE with high confidence  
                final_confidence = gemini_fake_percent
                print(f"🎯 GEMINI ≥95% FAKE: Final Score = Gemini Fake Confidence ({gemini_fake_percent:.1%})")
        else:
            # Fallback to the calculated gemini_score
            final_confidence = gemini_score
            print(f"🎯 GEMINI ≥95% (Fallback): Final Score = Gemini Score ({gemini_score:.1%})")
        
        final_confidence = min(1.0, max(0.0, final_confidence))  # Bound between 0-100%
        print(f"  - Gemini Direction: {gemini_direction}")
        print(f"  - Gemini Confidence: {gemini_score:.1%}")
        print(f"  - Final Confidence: {final_confidence:.1%}")
        print(f"  - DistilBERT: IGNORED (Gemini override)")
        print(f"  - Google Search: IGNORED (Gemini override)")
        print(f"✅ GEMINI OVERRIDE COMPLETE: Using {final_confidence:.1%} as final score")

    # Case 2: Google Search ≥95% → Final Score = Google Search Score
    elif google_search_real and not skip_google_search:
        final_confidence = min(1.0, max(0.0, google_search_score))  # Bound between 0-100%
        print(f"🎯 GOOGLE SEARCH ≥95%: Final Score = Google Search Score")
        print(f"  - Google Search Score: {google_search_score:.1%}")
        print(f"  - Final Confidence: {final_confidence:.1%}")
        print(f"  - DistilBERT: IGNORED (Google Search override)")
        print(f"  - Gemini: IGNORED (Google Search override)")

    # Case 3: Google Search ≤5% (fake) → Final Score = Google Search Score
    elif google_search_fake and not skip_google_search:
        final_confidence = min(1.0, max(0.0, google_search_score))  # Bound between 0-100%
        print(f"🎯 GOOGLE SEARCH ≤5% (FAKE): Final Score = Google Search Score")
        print(f"  - Google Search Score: {google_search_score:.1%}")
        print(f"  - Final Confidence: {final_confidence:.1%}")
        print(f"  - DistilBERT: IGNORED (Google Search override)")
        print(f"  - Gemini: IGNORED (Google Search override)")

    # Case 4: Cả hai ≥85% → Final Score = (Gemini + Google Search) / 2
    elif (not skip_google_search and google_search_score >= 0.85 and gemini_score >= 0.85):
        final_confidence = min(1.0, max(0.0, (google_search_score + gemini_score) / 2))  # Bound between 0-100%
        print(f"🎯 CẢ HAI ≥85%: Final Score = (Gemini + Google Search) / 2")
        print(f"  - Gemini Max Confidence: {gemini_score:.1%}")
        print(f"  - Google Search Score: {google_search_score:.1%}")
        print(f"  - Final Confidence: {final_confidence:.1%} (Average)")
        print(f"  - DistilBERT: IGNORED (Both high confidence override)")

    # Case 5: Default weighted average (normal case)
    else:
        final_confidence = (
            distilbert_score * distilbert_weight +
            gemini_score * gemini_weight +
            google_search_score * google_weight
        )

        # Apply reasonable bounds for weighted average (allow full 0-100% range)
        final_confidence = min(1.0, max(0.0, final_confidence))

        print(f"📊 DEFAULT WEIGHTED AVERAGE: Using standard weights")
        print(f"  - DistilBERT ({distilbert_weight*100:.1f}%): {distilbert_score:.1%}")
        print(f"  - Gemini ({gemini_weight*100:.1f}%): {gemini_score:.1%}")
        print(f"  - Google Search ({google_weight*100:.1f}%): {google_search_score:.1%}")
        print(f"  - Final Confidence: {final_confidence:.1%}")

    return final_confidence

def analyze_news(news_text):
    """Main analysis function combining all three tools"""
    try:
        # Check if input is empty
        if not news_text.strip():
            empty_message = """
<div style="font-family: 'Segoe UI', Arial, sans-serif; line-height: 1.6; color: #333;">

## 📝 **HƯỚNG DẪN SỬ DỤNG**

<div style="background: linear-gradient(135deg, #74b9ff 0%, #0984e3 100%); color: white; padding: 20px; border-radius: 10px; margin: 20px 0; text-align: center;">
<h2 style="margin: 0; font-size: 24px;">💡 Vui lòng nhập tin tức</h2>
<p style="margin: 10px 0 0 0; font-size: 16px; opacity: 0.9;">Để bắt đầu phân tích</p>
</div>

<div style="background: #f8f9fa; padding: 15px; border-radius: 8px; border-left: 4px solid #17a2b8; margin: 10px 0;">
<p><strong>Hướng dẫn:</strong></p>
<ul>
<li>Nhập tin tức tiếng Việt cần kiểm tra vào ô trên</li>
<li>Nhấn nút "Phân tích với AI nâng cao"</li>
<li>Chờ hệ thống phân tích (có thể mất 10-30 giây)</li>
<li>Xem kết quả phân tích chi tiết</li>
</ul>
</div>

</div>
"""
            return gr.update(value=empty_message, visible=True), "**Độ chắc chắn là tin thật:** 0%", "**Độ chắc chắn là tin giả:** 0%", gr.update(visible=False), gr.update(visible=False)
        
        print(f"Analyzing: {news_text[:50]}...")
        
        # Check if input is too short for Google Search (< 20 characters)
        skip_google_search = len(news_text.strip()) < 20
        if skip_google_search:
            print("⚠️ Input too short (< 20 chars) - Skipping Google Search, will use only DistilBERT + Gemini")
        
        # Step 1: Search Google for related information (skip if input too short)
        search_results = []
        search_error = None
        
        if skip_google_search:
            print("1. Skipping Google Search (input < 20 chars)...")
            search_results = []
        else:
            print("1. Running Google Search...")
            try:
                search_response = google_search(news_text)
                # Handle both old format (list) and new format (dict with error info)
                if isinstance(search_response, dict) and 'error' in search_response:
                    search_results = search_response['results']
                    search_error = {
                        'type': search_response['error'],
                        'message': search_response['error_message'],
                        'details': search_response['error_details']
                    }
                    print(f"Google Search error: {search_error['message']}")
                else:
                    search_results = search_response
            except Exception as e:
                print(f"Google Search error: {e}")
                search_results = []
                search_error = {
                    'type': 'EXCEPTION',
                    'message': 'Google Search failed with exception',
                    'details': str(e)
                }
        
        # Step 2: Run our trained model
        print("2. Running DistilBERT analysis...")
        try:
            distilbert_prediction, distilbert_confidence, real_score, fake_score = predict_with_distilbert(news_text)
        except Exception as e:
            print(f"DistilBERT analysis error: {e}")
            distilbert_prediction, distilbert_confidence, real_score, fake_score = None, None, None, None
        
        # Step 3: Check the sources we found
        print("3. Analyzing sources and popularity...")
        try:
            source_credibility, popularity_score, credibility_text, found_sources, credible_sources_found = analyze_sources(search_results)
            source_support, support_text = analyze_source_support(news_text, search_results)
        except Exception as e:
            print(f"Source analysis error: {e}")
            source_credibility, popularity_score, credibility_text = 0.5, 0.2, "Lỗi phân tích nguồn"
            found_sources, credible_sources_found = [], []
            source_support, support_text = 0.5, "Lỗi phân tích hỗ trợ nguồn"
        
        # Step 4: Get Gemini AI analysis
        print("4. Running Gemini analysis...")
        try:
            gemini_analysis = analyze_with_gemini(news_text, search_results, distilbert_prediction, distilbert_confidence)
        except Exception as e:
            print(f"Gemini analysis error: {e}")
            gemini_analysis = f"Lỗi phân tích Gemini: {str(e)}"
        
        # Step 5: Combine everything into final result
        print("5. Calculating combined confidence...")
        print(f"   DistilBERT: {distilbert_prediction} ({distilbert_confidence:.3f})")
        print(f"   Source credibility: {source_credibility:.3f}")
        print(f"   Source support: {source_support:.3f}")
        print(f"   Popularity: {popularity_score:.3f}")
        try:
            combined_confidence = calculate_combined_confidence(
                distilbert_prediction, distilbert_confidence,
                source_credibility, popularity_score, gemini_analysis, source_support, skip_google_search, len(search_results)
            )
            print(f"   Final combined confidence: {combined_confidence:.3f}")
        except Exception as e:
            print(f"Confidence calculation error: {e}")
            combined_confidence = 0.5  # Default to neutral
        
        # Step 6: Format the final results
        # Always prioritize Gemini's percentages when available
        gemini_real_percent, gemini_fake_percent = extract_gemini_percentage(gemini_analysis)
        
        if gemini_real_percent is not None and gemini_fake_percent is not None:
            # Use Gemini's actual percentages directly
            real_confidence = gemini_real_percent
            fake_confidence = gemini_fake_percent
            print(f"🎯 Using Gemini results directly: {real_confidence:.1%} REAL, {fake_confidence:.1%} FAKE")
        else:
            # Fallback to weighted average if Gemini percentages not available
            real_confidence = combined_confidence
            fake_confidence = 1 - combined_confidence
            print(f"📊 Using weighted average (Gemini fallback): {real_confidence:.1%} REAL, {fake_confidence:.1%} FAKE")
        # Set to 0 to avoid errors
        # Step 7: Check if result should be added to RAG (Gemini ≥95% in either direction)
        # Use the same gemini_real_percent and gemini_fake_percent from above
        if gemini_real_percent is not None and gemini_fake_percent is not None:
            gemini_max_confidence = max(gemini_real_percent, gemini_fake_percent)
            print(f"📊 Gemini confidence extracted: REAL={gemini_real_percent:.1%}, FAKE={gemini_fake_percent:.1%}, MAX={gemini_max_confidence:.1%}")
        else:
            gemini_max_confidence = 0
            print("⚠️ Could not extract Gemini confidence percentages for RAG")
            if gemini_analysis:
                print(f"   Analysis preview: {gemini_analysis[:300]}...")

        # Step 7: Enhanced RAG System - Save to Google Drive if confidence is high enough
        # (Old knowledge base system disabled to avoid duplicates - Enhanced RAG is better)
        print(f"\n🔍 RAG SAVE DEBUG:")
        print(f"   ENABLE_ENHANCED_RAG: {ENABLE_ENHANCED_RAG}")
        print(f"   gemini_max_confidence: {gemini_max_confidence} ({gemini_max_confidence:.1%} if > 0)")
        print(f"   RAG_CONFIDENCE_THRESHOLD: {RAG_CONFIDENCE_THRESHOLD}")
        print(f"   Condition check: gemini_max_confidence >= RAG_CONFIDENCE_THRESHOLD: {gemini_max_confidence >= RAG_CONFIDENCE_THRESHOLD if gemini_max_confidence else False}")
        print(f"   Should save? {ENABLE_ENHANCED_RAG and gemini_max_confidence and gemini_max_confidence >= RAG_CONFIDENCE_THRESHOLD}\n")
        
        if ENABLE_ENHANCED_RAG and gemini_max_confidence and gemini_max_confidence >= RAG_CONFIDENCE_THRESHOLD:
            try:
                from rag_news_manager import add_news_to_rag, initialize_rag_system, rag_manager
                
                # Try to initialize if not already initialized
                if not rag_manager.service or not rag_manager.rag_file_id:
                    print("🔄 RAG system not initialized - attempting to initialize now...")
                    if initialize_rag_system():
                        print("✅ RAG system initialized successfully!")
                    else:
                        print("❌ Failed to initialize RAG system - cannot save")
                        print("   🔧 Please run: python setup_google_drive_rag.py")
                        print("   📝 Or check Google Drive authentication")
                
                print(f"🚀 High confidence detected ({gemini_max_confidence:.1%}) - saving to Enhanced RAG system...")
                final_prediction = "REAL" if gemini_real_percent > gemini_fake_percent else "FAKE"
                
                rag_success = add_news_to_rag(
                    news_text=news_text,
                    gemini_analysis=gemini_analysis,
                    gemini_confidence=gemini_max_confidence,
                    prediction=final_prediction,
                    search_results=search_results,
                    distilbert_confidence=distilbert_confidence
                )
                
                if rag_success:
                    print("✅ Successfully saved to Enhanced RAG system (Google Drive)!")
                else:
                    print("⚠️ Failed to save to Enhanced RAG system")
                    print("   Possible reasons:")
                    print("   - Entry already exists (duplicate)")
                    print("   - Google Drive authentication expired (run: python setup_google_drive_rag.py)")
                    print("   - File permission issue")
                    
            except ImportError as e:
                print(f"❌ Cannot import RAG system: {e}")
                print("   Make sure rag_news_manager.py is in the same directory")
            except Exception as e:
                print(f"❌ Enhanced RAG system error: {e}")
                error_msg = str(e).lower()
                if "invalid_grant" in error_msg or "bad request" in error_msg:
                    print("   🔑 Google Drive token expired!")
                    print("   🔧 Run: python setup_google_drive_rag.py to re-authenticate")
                import traceback
                print(f"   Traceback: {traceback.format_exc()}")
        else:
            if not ENABLE_ENHANCED_RAG:
                print("⚠️ Enhanced RAG system is disabled (ENABLE_ENHANCED_RAG = False)")
            elif not gemini_max_confidence:
                print(f"⚠️ Cannot save: gemini_max_confidence is {gemini_max_confidence} (needs to be extracted)")
            elif gemini_max_confidence < RAG_CONFIDENCE_THRESHOLD:
                print(f"⚠️ Confidence {gemini_max_confidence:.1%} below threshold {RAG_CONFIDENCE_THRESHOLD:.1%}")
    
        # Build the detailed report with better formatting
        # Use the actual confidence scores to determine the final classification
        final_prediction = "REAL" if real_confidence > fake_confidence else "FAKE" if fake_confidence > real_confidence else "UNCERTAIN"
        prediction_emoji = "✅" if final_prediction == "REAL" else "❌" if final_prediction == "FAKE" else "❓"
        
        # Use the higher confidence for display
        max_confidence = max(real_confidence, fake_confidence)
        confidence_level = "Cao" if max_confidence > 0.7 else "Trung bình" if max_confidence > 0.4 else "Thấp"
        confidence_emoji = "🟢" if max_confidence > 0.7 else "🟡" if max_confidence > 0.4 else "🔴"
        
        # Convert technical metrics to user-friendly Vietnamese
        source_quality = "Tốt" if source_credibility > 0.7 else "Trung bình" if source_credibility > 0.4 else "Kém"
        source_count_text = f"{len(search_results)} nguồn tin" if len(search_results) > 0 else "Không tìm thấy nguồn"
        
        # Create source list display - SHOW ALL SOURCES
        sources_display = ""
        if found_sources:
            sources_display = "<br>".join([f"• {source}" for source in found_sources])  # Show ALL sources
        elif len(search_results) == 0:
            sources_display = "⚠️ Google Search không khả dụng do hết quota"
        
        # Show credible sources found
        credible_display = ""
        if credible_sources_found:
            credible_display = f"<br><strong>Nguồn uy tín:</strong><br>" + "<br>".join([f"✅ {source}" for source in credible_sources_found])
        
        # Simplify credibility text
        if search_error:
            if search_error['type'] == 'QUOTA_EXCEEDED':
                credibility_summary = f"⚠️ Google Search hết quota - chỉ dùng phân tích nội dung"
                source_count_text = "Không có (API hết quota)"
            elif search_error['type'] == 'API_KEY_INVALID':
                credibility_summary = f"❌ Google Search API key không hợp lệ"
                source_count_text = "Không có (API key lỗi)"
            else:
                credibility_summary = f"⚠️ Google Search lỗi - chỉ dùng phân tích nội dung"
                source_count_text = "Không có (lỗi API)"
        elif "High credibility" in credibility_text:
            credibility_summary = f"✅ Nguồn tin đáng tin cậy"
        elif "Medium credibility" in credibility_text:
            credibility_summary = f"⚠️ Nguồn tin trung bình"
        else:
            credibility_summary = f"❌ Nguồn tin kém tin cậy"
        
        # Simplify support text
        if "strongly support" in support_text.lower():
            support_summary = "✅ Các nguồn ủng hộ tin tức này"
        elif "contradict" in support_text.lower():
            support_summary = "❌ Các nguồn phản bác tin tức này"
        else:
            support_summary = "⚠️ Các nguồn có ý kiến trái chiều"
        
        detailed_analysis = f"""
<div style="font-family: 'Segoe UI', Arial, sans-serif; line-height: 1.6; color: #333;">

## 🔍 **KẾT QUẢ PHÂN TÍCH TIN TỨC**

<div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 20px; border-radius: 10px; margin: 20px 0; text-align: center;">
<h2 style="margin: 0; font-size: 24px;">{prediction_emoji} {'TIN THẬT' if final_prediction == 'REAL' else 'TIN GIẢ' if final_prediction == 'FAKE' else 'KHÔNG XÁC ĐỊNH'}</h2>
<p style="margin: 10px 0 0 0; font-size: 18px; opacity: 0.9;">{confidence_emoji} Độ tin cậy: {confidence_level} ({max_confidence:.0%})</p>
</div>

### 🤖 **Phân tích bằng AI**
<div style="background: #f8f9fa; padding: 15px; border-radius: 8px; border-left: 4px solid #007bff; margin: 10px 0;">
<p><strong>Kết quả:</strong> {prediction_emoji} {'Tin tức này có vẻ THẬT' if final_prediction == 'REAL' else 'Tin tức này có vẻ GIẢ' if final_prediction == 'FAKE' else 'Không thể xác định'}</p>
<p><strong>Độ chắc chắn:</strong> {f"{distilbert_confidence:.0%}" if distilbert_confidence else 'Không có'} - {'Rất cao' if distilbert_confidence and distilbert_confidence > 0.8 else 'Cao' if distilbert_confidence and distilbert_confidence > 0.6 else 'Trung bình' if distilbert_confidence and distilbert_confidence > 0.4 else 'Thấp'}</p>
</div>

### 🌐 **Kiểm tra nguồn tin**
<div style="background: #f8f9fa; padding: 15px; border-radius: 8px; border-left: 4px solid #28a745; margin: 10px 0;">
<p><strong>Tìm thấy:</strong> {source_count_text}</p>
<p><strong>Chất lượng nguồn:</strong> {source_quality} ({source_credibility:.0%})</p>
<p><strong>Đánh giá:</strong> {credibility_summary}</p>
<p><strong>Hỗ trợ:</strong> {support_summary}</p>
{sources_display and f'<p><strong>Nguồn tìm thấy:</strong><br>{sources_display}</p>' or ''}
{credible_display}
</div>

{search_error and f'''
### ⚠️ **Cảnh báo Google Search**
<div style="background: #fff3cd; padding: 15px; border-radius: 8px; border-left: 4px solid #ffc107; margin: 10px 0;">
<p><strong>Lỗi:</strong> {search_error["message"]}</p>
<p><strong>Chi tiết:</strong> {search_error["details"]}</p>
<p><strong>Ảnh hưởng:</strong> Hệ thống đang sử dụng phân tích nội dung thay vì tìm kiếm Google. Kết quả có thể kém chính xác hơn.</p>
</div>
''' or ''}

### 🧠 **Phân tích thông minh**
<div style="background: #f8f9fa; padding: 20px; border-radius: 10px; border-left: 4px solid #ffc107; margin: 15px 0; font-family: 'Segoe UI', Arial, sans-serif; line-height: 1.6;">
<div style="white-space: pre-line; color: #333;">
{gemini_analysis}
</div>
</div>

### 📊 **KẾT LUẬN CUỐI CÙNG**
<div style="background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); color: white; padding: 15px; border-radius: 8px; margin: 20px 0;">
<p style="margin: 0; font-size: 16px;"><strong>Tin tức này có khả năng {'THẬT' if final_prediction == 'REAL' else 'GIẢ' if final_prediction == 'FAKE' else 'KHÔNG XÁC ĐỊNH'} với độ tin cậy {max(real_confidence, fake_confidence):.0%}</strong></p>
<p style="margin: 5px 0 0 0; font-size: 14px; opacity: 0.9;">Dựa trên phân tích AI, kiểm tra nguồn tin và đánh giá thông minh</p>
</div>

</div>
"""
    
        return gr.update(value=detailed_analysis, visible=True), f"**Độ chắc chắn là tin thật:** {real_confidence:.1%}", f"**Độ chắc chắn là tin giả:** {fake_confidence:.1%}", gr.update(visible=True), gr.update(visible=False)
    
    except Exception as e:
        error_message = f"""
<div style="font-family: 'Segoe UI', Arial, sans-serif; line-height: 1.6; color: #333;">

## ❌ **LỖI PHÂN TÍCH**

<div style="background: linear-gradient(135deg, #ff6b6b 0%, #ee5a24 100%); color: white; padding: 20px; border-radius: 10px; margin: 20px 0; text-align: center;">
<h2 style="margin: 0; font-size: 24px;">⚠️ Có lỗi xảy ra</h2>
<p style="margin: 10px 0 0 0; font-size: 16px; opacity: 0.9;">Vui lòng thử lại sau</p>
</div>

<div style="background: #f8f9fa; padding: 15px; border-radius: 8px; border-left: 4px solid #dc3545; margin: 10px 0;">
<p><strong>Chi tiết lỗi:</strong> {str(e)}</p>
<p><strong>Gợi ý:</strong> Kiểm tra kết nối internet và thử lại</p>
</div>

</div>
"""
        print(f"Analysis error: {e}")
        return gr.update(value=error_message, visible=True), "**Độ chắc chắn là tin thật:** 0%", "**Độ chắc chắn là tin giả:** 0%", gr.update(visible=True), gr.update(visible=False)

# --- GRADIO INTERFACE ---
def create_interface():
    with gr.Blocks(title="Vietnamese Fake News Detection System", theme=gr.themes.Soft()) as interface:
        gr.Markdown("""
        <div style="text-align: center; padding: 20px;">
        <h1 style="color: #2c3e50; margin-bottom: 10px;">🔍 Vietnamese Fake News Detection System</h1>
        <p style="color: #7f8c8d; font-size: 16px; margin-bottom: 30px;">Powered by Google Search + Gemini AI + DistilBERT</p>
        
        <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 20px; border-radius: 15px; margin: 20px 0;">
        <h3 style="margin: 0 0 15px 0;">🛡️ Hệ thống phát hiện tin giả tiếng Việt</h3>
        <div style="display: flex; justify-content: space-around; flex-wrap: wrap; gap: 15px;">
        <div style="text-align: center;">
        <div style="font-size: 24px; margin-bottom: 5px;">🌐</div>
        <strong>Google Search</strong><br>
        <small>Tìm kiếm thông tin thực tế</small>
        </div>
        <div style="text-align: center;">
        <div style="font-size: 24px; margin-bottom: 5px;">🧠</div>
        <strong>Gemini AI</strong><br>
        <small>Phân tích thông minh</small>
        </div>
        <div style="text-align: center;">
        <div style="font-size: 24px; margin-bottom: 5px;">🤖</div>
        <strong>DistilBERT</strong><br>
        <small>AI chuyên tiếng Việt</small>
        </div>
        </div>
        </div>
        
        <div style="background: #f8f9fa; padding: 15px; border-radius: 10px; border-left: 4px solid #17a2b8; margin: 20px 0;">
        <p style="margin: 0; color: #495057;"><strong>💡 Lưu ý:</strong> Kết quả có thể thay đổi nhẹ giữa các lần phân tích do tính chất AI của Gemini, nhưng độ chính xác tổng thể vẫn được đảm bảo.</p>
        </div>
        
        <div style="background: linear-gradient(135deg, #a8edea 0%, #fed6e3 100%); padding: 15px; border-radius: 10px; margin: 20px 0;">
        <h4 style="margin: 0 0 10px 0; color: #333;">🧠 Hệ thống RAG với Cơ sở Tri thức Tự động</h4>
        <p style="margin: 0; color: #555; font-size: 14px;">Khi độ tin cậy > 95%, hệ thống sẽ tự động lưu kết quả vào cơ sở tri thức để sử dụng cho các phân tích tương lai.</p>
        </div>
        </div>
        """)
        
        with gr.Row():
            with gr.Column(scale=2):
                gr.Markdown("### 📝 Nhập tin tức cần kiểm tra")
                news_input = gr.Textbox(
                    placeholder="Nhập tin tức tiếng Việt cần kiểm tra...",
                    lines=4,
                    show_label=False
                )
                
                analyze_btn = gr.Button("🔍 Phân tích với AI nâng cao", variant="primary", size="lg")
            
            with gr.Column(scale=1, visible=False) as results_column:
                gr.Markdown("### 📊 Kết quả phân tích")
                real_confidence = gr.Markdown("**Độ chắc chắn là tin thật:** 0%")
                fake_confidence = gr.Markdown("**Độ chắc chắn là tin giả:** 0%")
        
        detailed_analysis = gr.Markdown("### 📋 Phân tích chi tiết sẽ hiển thị ở đây...", visible=False)
        
        # Loading indicator
        loading_status = gr.Markdown("", visible=False)
        
        # Event handlers
        analyze_btn.click(
            fn=lambda: gr.update(visible=True, value="🔄 **Đang phân tích...** Vui lòng chờ trong giây lát..."),
            inputs=[],
            outputs=[loading_status]
        ).then(
            fn=analyze_news,
            inputs=[news_input],
            outputs=[detailed_analysis, real_confidence, fake_confidence, results_column, loading_status]
        )
        

    return interface

def test_google_search():
    """Test Google Search API functionality"""
    print("Testing Google Search API...")
    print("=" * 50)
    
    # Test queries
    test_queries = [
        "Argentina World Cup 2022",
        "Vietnam COVID-19 news",
        "Tin tức Việt Nam"
    ]
    
    results_found = 0
    
    for i, query in enumerate(test_queries, 1):
        print(f"\nTest {i}: '{query}'")
        print("-" * 30)
        
        try:
            results = google_search(query)
            print(f"Results: {len(results)} found")
            
            if results:
                results_found += 1
                print(f"First result: {results[0]['title'][:50]}...")
                print(f"   Link: {results[0]['link']}")
            else:
                print("No results found")
                
        except Exception as e:
            print(f"Error: {e}")
    
    print(f"\nTest Summary: {results_found}/{len(test_queries)} tests passed")
    
    if results_found == 0:
        print("\nGoogle Search is not working!")
        print("Possible solutions:")
        print("   1. Check API quota in Google Cloud Console")
        print("   2. Verify API keys are correct")
        print("   3. Ensure Custom Search API is enabled")
        print("   4. Check Search Engine ID is valid")
    elif results_found < len(test_queries):
        print("\nGoogle Search partially working")
        print("Some queries work, others don't - check query formatting")
    else:
        print("\nGoogle Search is working perfectly!")
    
    return results_found > 0

def test_gemini_override():
    """Test the Gemini override functionality"""
    print("Testing Gemini Override Logic")
    print("=" * 50)
    
    # Test cases with different Gemini confidence levels
    test_cases = [
        {
            "name": "High Real Confidence",
            "gemini_analysis": "1. KẾT LUẬN: THẬT\n2. ĐỘ TIN CẬY: THẬT: 98% / GIẢ: 2%",
            "expected_override": True,
            "expected_direction": "REAL"
        },
        {
            "name": "High Fake Confidence", 
            "gemini_analysis": "1. KẾT LUẬN: GIẢ\n2. ĐỘ TIN CẬY: THẬT: 3% / GIẢ: 97%",
            "expected_override": True,
            "expected_direction": "FAKE"
        },
        {
            "name": "Low Confidence",
            "gemini_analysis": "1. KẾT LUẬN: KHÔNG XÁC ĐỊNH\n2. ĐỘ TIN CẬY: THẬT: 60% / GIẢ: 40%",
            "expected_override": False,
            "expected_direction": "UNKNOWN"
        }
    ]
    
    for i, test_case in enumerate(test_cases, 1):
        print(f"\nTest Case {i}: {test_case['name']}")
        print("-" * 40)
        print(f"Gemini Analysis: {test_case['gemini_analysis']}")
        
        try:
            # Test the confidence calculation directly
            gemini_real_percent, gemini_fake_percent = extract_gemini_percentage(test_case['gemini_analysis'])
            
            if gemini_real_percent is not None and gemini_fake_percent is not None:
                if gemini_real_percent > gemini_fake_percent:
                    gemini_score = gemini_real_percent
                    gemini_direction = "REAL"
                else:
                    gemini_score = gemini_fake_percent
                    gemini_direction = "FAKE"
                
                print(f"Extracted: Real={gemini_real_percent:.1%}, Fake={gemini_fake_percent:.1%}")
                print(f"Direction: {gemini_direction}")
                print(f"Score: {gemini_score:.1%}")
                print(f"Override triggered: {gemini_score >= 0.95}")
                
                if gemini_score >= 0.95:
                    print(f"✅ OVERRIDE: Using {gemini_score:.1%} as final score")
                else:
                    print(f"❌ NO OVERRIDE: Score {gemini_score:.1%} < 95%")
                    
            else:
                print("❌ Could not extract percentages")
                
        except Exception as e:
            print(f"❌ Test failed: {e}")

def test_complete_system():
    """Test the complete fake news detection system"""
    print("Testing Complete Vietnamese Fake News Detection System")
    print("=" * 60)
    
    # Test cases
    test_cases = [
        "Argentina vô địch World Cup 2022",
        "Hôm nay trời mưa ở Hà Nội",
        "COVID-19 đã được chữa khỏi hoàn toàn"
    ]
    
    for i, test_text in enumerate(test_cases, 1):
        print(f"\nTest Case {i}: '{test_text}'")
        print("-" * 40)
        
        try:
            result = analyze_news(test_text)
            print("Analysis completed successfully")
            print(f"Result type: {type(result)}")
        except Exception as e:
            print(f"Analysis failed: {e}")

# --- LAUNCH APP ---
if __name__ == "__main__":
    print("Starting Vietnamese Fake News Detection System...")
    print("Tools integrated: Google Search + Gemini AI + DistilBERT")
    
    # Uncomment the line below to run tests first
    # test_google_search()
    # test_gemini_override()  # Uncomment to test Gemini override logic
    
    interface = create_interface()
    interface.launch(
        server_name="0.0.0.0",
        server_port=7860,  # Standard port for Hugging Face Spaces
        share=True,  # Not needed for Hugging Face Spaces
        show_error=True
    )