Update app.py

app.py

@@ -1207,7 +1207,116 @@ def audio_transcription_tool(file_path: str) -> str:
         telemetry.record_call("audio_transcription_tool", time.time() - start_time, False)
         raise ToolError("audio_transcription_tool", e)
 
+class ChessAnalysisInput(BaseModel):
+    image_path: str = Field(description="Path to chess board image")
+    description: str = Field(description="Context about position", default="")
 
+@tool(args_schema=ChessAnalysisInput)
+def analyze_chess_position(image_path: str, description: str = "") -> str:
+    """
+    Analyze chess position using Stockfish.
+    Requires stockfish binary installed.
+    """
+    start_time = time.time()
+    
+    try:
+        print(f"♟️ Analyzing chess: {image_path}")
+        
+        # Find image
+        chess_image = find_file(image_path)
+        if not chess_image and os.path.exists(image_path):
+            chess_image = Path(image_path)
+        
+        if not chess_image or not chess_image.exists():
+            raise FileNotFoundError(f"Chess image not found: {image_path}")
+        
+        # Extract FEN using Gemini Vision
+        GOOGLE_API_KEY = os.getenv("GEMINI_API_KEY")
+        if not GOOGLE_API_KEY:
+            raise ValueError("GEMINI_API_KEY not set")
+        
+        img = Image.open(chess_image)
+        if img.mode not in ['RGB', 'RGBA']:
+            img = img.convert('RGB')
+        
+        buffered = io.BytesIO()
+        img.save(buffered, format="JPEG")
+        img_base64 = base64.b64encode(buffered.getvalue()).decode()
+        
+        vision_llm = ChatGoogleGenerativeAI(
+            model="gemini-2.5-flash",
+            google_api_key=GOOGLE_API_KEY,
+            temperature=0
+        )
+        
+        fen_prompt = """Analyze this chess board and provide FEN notation.
+Return ONLY the FEN string, nothing else.
+Format: piece_placement active_color castling en_passant halfmove fullmove"""
+        
+        message = HumanMessage(
+            content=[
+                {"type": "text", "text": fen_prompt},
+                {"type": "image_url", "image_url": f"data:image/jpeg;base64,{img_base64}"}
+            ]
+        )
+        
+        response = vision_llm.invoke([message])
+        fen = response.content.strip()
+        
+        # Clean FEN
+        for line in fen.split('\n'):
+            line = line.strip().replace('```', '').replace('fen', '')
+            if '/' in line and ' ' in line:
+                fen = line
+                break
+        
+        print(f"✓ FEN: {fen}")
+        
+        # Analyze with Stockfish
+        try:
+            import chess
+            from stockfish import Stockfish
+        except ImportError:
+            raise ImportError("Need: pip install python-chess stockfish")
+        
+        # Find Stockfish binary
+        stockfish_paths = [
+            "/usr/games/stockfish",
+            "/usr/local/bin/stockfish",
+            "/usr/bin/stockfish",
+            "stockfish"
+        ]
+        
+        stockfish_path = None
+        for path in stockfish_paths:
+            if os.path.exists(path):
+                stockfish_path = path
+                break
+        
+        if not stockfish_path:
+            raise FileNotFoundError("Stockfish binary not found. Install: apt-get install stockfish")
+        
+        stockfish = Stockfish(path=stockfish_path, depth=20)
+        stockfish.set_fen_position(fen)
+        
+        best_move_uci = stockfish.get_best_move()
+        if not best_move_uci:
+            raise ValueError("No legal move found")
+        
+        # Convert to SAN
+        board = chess.Board(fen)
+        uci_move = chess.Move.from_uci(best_move_uci)
+        san_move = board.san(uci_move)
+        
+        print(f"✓ Best move: {san_move}")
+        
+        telemetry.record_call("analyze_chess_position", time.time() - start_time, True)
+        return san_move
+        
+    except Exception as e:
+        telemetry.record_call("analyze_chess_position", time.time() - start_time, False)
+        raise ToolError("analyze_chess_position", e, "Check if stockfish installed")
+        
 class ImageAnalysisInput(BaseModel):
     file_path: str = Field(description="Image file path")
     query: str = Field(description="What to analyze")