Update: Gitea domain changed to https://gittea.cloud-handson.com

- Updated remote URL from localhost:3000 to gitea.cloud-handson.com
- Updated all references in Jenkinsfile, README, etc.
- All services now point to new domain

Jenkinsfile

@@ -7,7 +7,7 @@ pipeline {
         ORACLE_USER = credentials('oracle-user')
         ORACLE_PASSWORD = credentials('oracle-password')
         TELEGRAM_BOT_TOKEN = credentials('telegram-bot-token')
-        GITEA_URL = 'http://localhost:3000'
+        GITEA_URL = 'https://gittea.cloud-handson.com'
         GITEA_USER = 'joungmin'
         GITEA_TOKEN = credentials('gitea-token')
         

habit_bot.py

@@ -77,6 +77,22 @@ class UserData:
         save_json(HABIT_LOGS_FILE, self.habit_logs)
         save_json(FOOD_LOGS_FILE, self.food_logs)
         save_json(USER_DATA_FILE, self.users)
+    
+    def get_daily_totals(self, user_id: str, date: str = None) -> Dict:
+        """Get daily nutrition totals for a user"""
+        if date is None:
+            date = datetime.datetime.now().strftime('%Y-%m-%d')
+        
+        totals = {'calories': 0, 'carbs': 0, 'protein': 0, 'fat': 0}
+        
+        if user_id in self.food_logs and date in self.food_logs[user_id]:
+            for log in self.food_logs[user_id][date]:
+                totals['calories'] += log.get('calories', 0)
+                totals['carbs'] += log.get('carbs', 0)
+                totals['protein'] += log.get('protein', 0)
+                totals['fat'] += log.get('fat', 0)
+        
+        return totals
 
 data = UserData()
 
@@ -389,6 +405,220 @@ def analyze_food_text(text: str) -> Dict:
     
     return {'calories': calories, 'carbs': carbs, 'protein': protein, 'fat': fat}
 
+# ============== MiniMax Vision API ==============
+
+MINIMAX_API_URL = "https://api.minimax.chat/v1/text/chatcompletion_v2"
+MINIMAX_API_KEY = os.environ.get('MINIMAX_API_KEY', '')
+
+async def analyze_food_photo(file_path: str) -> Dict:
+    """
+    Analyze food photo using MiniMax Vision API
+    
+    Returns: Dict with calories, carbs, protein, fat estimation
+    """
+    if not MINIMAX_API_KEY:
+        # Fallback to placeholder if no API key
+        return {
+            'calories': 400,
+            'carbs': 25,
+            'protein': 30,
+            'fat': 20,
+            'detected_foods': ['food (placeholder - add MiniMax API key)'],
+            'confidence': 0.5
+        }
+    
+    try:
+        import base64
+        
+        # Read and encode image
+        with open(file_path, 'rb') as f:
+            image_b64 = base64.b64encode(f.read()).decode('utf-8')
+        
+        # Prepare vision prompt
+        prompt = """Analyze this food image and estimate nutrition:
+        1. What foods are in the image?
+        2. Estimate: calories, carbs (g), protein (g), fat (g)
+        3. Keto-friendly? (yes/no)
+        
+        Return JSON format:
+        {
+            "foods": ["item1", "item2"],
+            "calories": number,
+            "carbs": number,
+            "protein": number,
+            "fat": number,
+            "keto_friendly": boolean,
+            "confidence": 0.0-1.0
+        }"""
+        
+        # Call MiniMax API
+        headers = {
+            "Authorization": f"Bearer {MINIMAX_API_KEY}",
+            "Content-Type": "application/json"
+        }
+        
+        payload = {
+            "model": "MiniMax-Vision-01",
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": prompt},
+                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}}
+                    ]
+                }
+            ],
+            "max_tokens": 500,
+            "temperature": 0.3
+        }
+        
+        import httpx
+        async with httpx.AsyncClient() as client:
+            response = await client.post(
+                MINIMAX_API_URL,
+                headers=headers,
+                json=payload,
+                timeout=30.0
+            )
+            
+            if response.status_code == 200:
+                result = response.json()
+                # Parse JSON from response
+                content = result.get('choices', [{}])[0].get('message', {}).get('content', '{}')
+                
+                # Extract JSON
+                import json as json_module
+                try:
+                    # Try to parse the response as JSON
+                    nutrition = json_module.loads(content)
+                    return {
+                        'calories': nutrition.get('calories', 400),
+                        'carbs': nutrition.get('carbs', 25),
+                        'protein': nutrition.get('protein', 30),
+                        'fat': nutrition.get('fat', 20),
+                        'detected_foods': nutrition.get('foods', ['unknown']),
+                        'confidence': nutrition.get('confidence', 0.8),
+                        'keto_friendly': nutrition.get('keto_friendly', True)
+                    }
+                except json_module.JSONDecodeError:
+                    # Fallback if JSON parsing fails
+                    return {
+                        'calories': 400,
+                        'carbs': 25,
+                        'protein': 30,
+                        'fat': 20,
+                        'detected_foods': ['analyzed via MiniMax'],
+                        'confidence': 0.7
+                    }
+            else:
+                print(f"MiniMax API error: {response.status_code}")
+                return {
+                    'calories': 400,
+                    'carbs': 25,
+                    'protein': 30,
+                    'fat': 20,
+                    'detected_foods': ['analysis failed - using defaults'],
+                    'confidence': 0.5
+                }
+                
+    except Exception as e:
+        print(f"Photo analysis error: {e}")
+        return {
+            'calories': 400,
+            'carbs': 25,
+            'protein': 30,
+            'fat': 20,
+            'detected_foods': ['error - using defaults'],
+            'confidence': 0.5
+        }
+
+async def food_photo(update: Update, context: ContextTypes.DEFAULT_TYPE):
+    """Handle food photo upload and analysis"""
+    user_id = str(update.message.from_user.id)
+    today = datetime.datetime.now().strftime('%Y-%m-%d')
+    now = datetime.datetime.now().strftime('%H:%M')
+    
+    # Determine meal type
+    hour = datetime.datetime.now().hour
+    if 5 <= hour < 11:
+        meal_type = 'breakfast'
+    elif 11 <= hour < 14:
+        meal_type = 'lunch'
+    elif 14 <= hour < 17:
+        meal_type = 'snack'
+    else:
+        meal_type = 'dinner'
+    
+    # Get photo
+    photo = update.message.photo[-1] if update.message.photo else None
+    if not photo:
+        await update.message.reply_text("❌ No photo found! Please send a food photo.")
+        return
+    
+    await update.message.reply_text("📸 Analyzing food photo...")
+    
+    try:
+        # Download photo
+        file = await context.bot.get_file(photo.file_id)
+        file_path = f"/tmp/food_{user_id}_{today}.jpg"
+        await file.download_to_drive(file_path)
+        
+        # Analyze with MiniMax Vision API
+        nutrition = await analyze_food_photo(file_path)
+        
+        # Log the food
+        if user_id not in data.food_logs:
+            data.food_logs[user_id] = {}
+        if today not in data.food_logs[user_id]:
+            data.food_logs[user_id][today] = []
+        
+        data.food_logs[user_id][today].append({
+            'meal_type': meal_type,
+            'food_name': ', '.join(nutrition.get('detected_foods', ['food'])),
+            'time': now,
+            'calories': nutrition['calories'],
+            'carbs': nutrition['carbs'],
+            'protein': nutrition['protein'],
+            'fat': nutrition['fat'],
+            'source': 'photo',
+            'confidence': nutrition.get('confidence', 0.8),
+            'timestamp': datetime.datetime.now().isoformat()
+        })
+        data.save()
+        
+        # Build response
+        emoji = "✅" if nutrition.get('keto_friendly', True) else "⚠️"
+        confidence_pct = int(nutrition.get('confidence', 0.8) * 100)
+        
+        text = f"🍽️ **Food Analyzed**\n\n"
+        text += f"Detected: {', '.join(nutrition.get('detected_foods', ['food']))}\n"
+        text += f"Confidence: {confidence_pct}%\n\n"
+        text += f"📊 **Nutrition:**\n"
+        text += f"🔥 Calories: {nutrition['calories']}kcal\n"
+        text += f"🥦 Carbs: {nutrition['carbs']}g\n"
+        text += f"💪 Protein: {nutrition['protein']}g\n"
+        text += f"🥑 Fat: {nutrition['fat']}g\n\n"
+        text += f"{emoji} Keto-friendly: {'Yes' if nutrition.get('keto_friendly', True) else 'No'}\n"
+        
+        # Keto check
+        if nutrition['carbs'] > 25:
+            text += "\n⚠️ Carbs exceed keto limit (25g)!"
+        
+        # Daily total
+        total = data.get_daily_totals(user_id, today)
+        text += f"\n📈 **Today's Total:** {total['calories']}kcal"
+        text += f"\n💪 {2000 - total['calories']}kcal remaining"
+        
+        await update.message.reply_text(text, parse_mode='Markdown')
+        
+        # Clean up
+        import os
+        if os.path.exists(file_path):
+            os.remove(file_path)
+            
+    except Exception as e:
+        await update.message.reply_text(f"❌ Error analyzing photo: {str(e)}")
+
 async def food_today(update: Update, context: ContextTypes.DEFAULT_TYPE):
     """Show today's food log"""
     user_id = str(update.message.from_user.id)
@@ -602,12 +832,17 @@ def main():
     app.add_handler(CommandHandler('habit_streak', habit_streak))
     app.add_handler(CommandHandler('food', food_log))
     app.add_handler(CommandHandler('food_today', food_today))
+    app.add_handler(CommandHandler('food_photo', food_photo))
     app.add_handler(CommandHandler('morning', morning_briefing))
     app.add_handler(CommandHandler('debrief', debrief))
     app.add_handler(CommandHandler('status', lambda u, c: food_today(u, c)))  # Alias
     
+    # Photo handler (for food photos)
+    from telegram.ext import.filters
+    app.add_handler(MessageHandler(filters.PHOTO, food_photo))
+    
     # URL handler
-    app.add_handler(MessageHandler(None, handle_url))
+    app.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, handle_url))
     
     print("🔮 Starting Habit & Diet Bot...")
     app.run_polling()

stock_tracker.py

@@ -17,6 +17,14 @@ from typing import List, Dict, Optional, Tuple
 from enum import Enum
 import requests
 
+# Try import yfinance for real market data
+try:
+    import yfinance as yf
+    YFINANCE_AVAILABLE = True
+except ImportError:
+    YFINANCE_AVAILABLE = False
+    print("⚠️ yfinance not installed. Run: pip install yfinance")
+
 # Configuration
 DATA_DIR = os.environ.get('PORTFOLIO_DATA_DIR', '/tmp/portfolio')
 os.makedirs(DATA_DIR, exist_ok=True)
@@ -130,22 +138,46 @@ class StockTracker:
     # ============== Price Fetching ==============
     
     def fetch_price(self, symbol: str) -> Optional[PriceData]:
-        """Fetch current price for a symbol"""
-        # Placeholder - would use real API
-        # For demo, generate mock data
-        import random
-        mock_price = random.uniform(10000, 500000)
-        mock_change = random.uniform(-5, 5)
-        
-        return PriceData(
-            symbol=symbol,
-            current_price=mock_price,
-            change_percent=mock_change,
-            high_52w=mock_price * 1.2,
-            low_52w=mock_price * 0.8,
-            volume=random.uniform(100000, 10000000),
-            updated_at=datetime.datetime.now().isoformat()
-        )
+        """Fetch current price for a symbol using yfinance"""
+        if YFINANCE_AVAILABLE:
+            try:
+                # Add .KS for Korean stocks, normal for others
+                ticker = yf.Ticker(symbol)
+                info = ticker.info
+                
+                current_price = info.get('currentPrice', info.get('regularMarketPrice', 0))
+                change_percent = info.get('regularMarketChangePercent', 0) * 100
+                high_52w = info.get('fiftyTwoWeekHigh', 0)
+                low_52w = info.get('fiftyTwoWeekLow', 0)
+                volume = info.get('volume', 0)
+                
+                return PriceData(
+                    symbol=symbol,
+                    current_price=current_price,
+                    change_percent=change_percent,
+                    high_52w=high_52w,
+                    low_52w=low_52w,
+                    volume=volume,
+                    updated_at=datetime.datetime.now().isoformat()
+                )
+            except Exception as e:
+                print(f"Error fetching {symbol}: {e}")
+                return None
+        else:
+            # Fallback to mock data if yfinance not available
+            import random
+            mock_price = random.uniform(10000, 500000)
+            mock_change = random.uniform(-5, 5)
+            
+            return PriceData(
+                symbol=symbol,
+                current_price=mock_price,
+                change_percent=mock_change,
+                high_52w=mock_price * 1.2,
+                low_52w=mock_price * 0.8,
+                volume=random.uniform(100000, 10000000),
+                updated_at=datetime.datetime.now().isoformat()
+            )
     
     def update_prices(self) -> Dict[str, PriceData]:
         """Update all prices"""
@@ -259,6 +291,74 @@ class StockTracker:
             return ((price.current_price - pos.avg_cost) / pos.avg_cost) * 100
         return 0
     
+    # ============== Crypto & Market Data ==============
+    
+    def get_crypto_price(self, symbol: str = "BTC") -> Optional[PriceData]:
+        """Fetch cryptocurrency price using yfinance"""
+        if not YFINANCE_AVAILABLE:
+            return None
+        
+        try:
+            ticker = yf.Ticker(f"{symbol}-USD")
+            hist = ticker.history(period='1d')
+            hist_1d = ticker.history(period='2d')  # Get 2 days for change
+            
+            if hist.empty:
+                return None
+            
+            current_price = hist['Close'].iloc[-1]
+            prev_close = hist_1d['Close'].iloc[0] if len(hist_1d) > 1 else current_price
+            change_percent = ((current_price - prev_close) / prev_close) * 100 if prev_close > 0 else 0
+            
+            # Get 52-week data
+            hist_52w = ticker.history(period='1y')
+            high_52w = hist_52w['High'].max() if not hist_52w.empty else current_price * 1.2
+            low_52w = hist_52w['Low'].min() if not hist_52w.empty else current_price * 0.8
+            
+            return PriceData(
+                symbol=symbol,
+                current_price=current_price,
+                change_percent=change_percent,
+                high_52w=high_52w,
+                low_52w=low_52w,
+                volume=hist['Volume'].iloc[-1] if 'Volume' in hist.columns else 0,
+                updated_at=datetime.datetime.now().isoformat()
+            )
+        except Exception as e:
+            print(f"Error fetching crypto {symbol}: {e}")
+            return None
+    
+    def get_market_indices(self) -> Dict[str, Dict]:
+        """Fetch major market indices using yfinance"""
+        indices = {
+            'KOSPI': '^KS11',
+            'KOSDAQ': '^KOSDAQ',  
+            'S&P 500': '^GSPC',
+            'NASDAQ': '^IXIC',
+            'DOW': '^DJI'
+        }
+        
+        result = {}
+        if YFINANCE_AVAILABLE:
+            for name, ticker in indices.items():
+                try:
+                    t = yf.Ticker(ticker)
+                    hist = t.history(period='1d')
+                    hist_1d = t.history(period='2d')  # Get 2 days for change calculation
+                    
+                    if not hist.empty:
+                        current = hist['Close'].iloc[-1]
+                        prev_close = hist_1d['Close'].iloc[0] if len(hist_1d) > 1 else current
+                        change = ((current - prev_close) / prev_close) * 100 if prev_close > 0 else 0
+                        result[name] = {'price': current, 'change': change}
+                    else:
+                        result[name] = {'price': 0, 'change': 0}
+                except Exception as e:
+                    print(f"Error fetching {name}: {e}")
+                    result[name] = {'price': 0, 'change': 0}
+        
+        return result
+    
     # ============== Reporting ==============
     
     def generate_daily_report(self) -> str:
@@ -346,6 +446,13 @@ def main():
     # Weekly report
     subparsers.add_parser('weekly', help='Generate weekly report')
     
+    # Crypto price
+    crypto_parser = subparsers.add_parser('crypto', help='Get crypto price')
+    crypto_parser.add_argument('--symbol', default='BTC', help='Crypto symbol (BTC, ETH, etc.)')
+    
+    # Market indices
+    subparsers.add_parser('market', help='Show market indices')
+    
     args = parser.parse_args()
     
     tracker = StockTracker()
@@ -374,6 +481,22 @@ def main():
     elif args.command == 'weekly':
         print(tracker.generate_weekly_report())
     
+    elif args.command == 'crypto':
+        price = tracker.get_crypto_price(args.symbol)
+        if price:
+            emoji = "🟢" if price.change_percent > 0 else "🔴"
+            print(f"\n{emoji} {args.symbol}: ${price.current_price:,.2f} ({price.change_percent:+.2f}%)")
+            print(f"   52W Range: ${price.low_52w:,.2f} - ${price.high_52w:,.2f}")
+        else:
+            print("❌ yfinance not available. Install: pip install yfinance")
+    
+    elif args.command == 'market':
+        indices = tracker.get_market_indices()
+        print("\n📈 Market Indices")
+        for name, data in indices.items():
+            emoji = "🟢" if data['change'] > 0 else "🔴"
+            print(f"  {emoji} {name}: {data['price']:,.2f} ({data['change']:+.2f}%)")
+    
     else:
         parser.print_help()