trocando implementacao para obtencao do FEN

chess_image_to_fen_tool.py

@@ -0,0 +1,50 @@
+from langchain_core.tools import tool
+from image_to_text_tool import image_to_text
+from utils import get_base64
+from typing import Literal
+from utils import get_base64
+
+@tool
+def chess_image_to_fen(image_path_in_base64:str, current_player: Literal["black", "white"]) -> Dict[str,str]:
+    """
+        Convert chess image to FEN (Forsyth-Edwards Notation) notation.
+        Args:
+            image_path_in_base64: Path to the image file in base64 format.
+            current_player: Whose turn it is to play. Must be either 'black' or 'white'.
+        Returns:
+            JSON with FEN (Forsyth-Edwards Notation) string representing the current board position.
+    """
+    print(f"Image to Fen invocada com os seguintes parametros:")
+    print(f"image_path: {image_path}")
+    print(f"current_player: {current_player}")
+
+
+    if current_player not in ["black", "white"]:
+        raise ValueError("current_player must be 'black' or 'white'")
+    
+    base64_image = get_base64(chessboard_image_base64_path)
+
+    if not base64_image:
+        raise ValueError("Failed to encode image to base64.")
+    base64_image_encoded =  f"data:image/jpeg;base64,{base64_image}"
+    url = CHESSVISION_TO_FEN_URL
+    payload = {
+        "board_orientation": "predict",
+        "cropped": False,
+        "current_player": "black",
+        "image": base64_image_encoded,
+        "predict_turn": False
+    }
+
+    response = requests.post(url, json=payload)
+    if response.status_code == 200:
+        dados = response.json()
+        if dados.get("success"):
+            print(f"Retorno Chessvision {dados}")
+            fen = dados.get("result")
+            fen = fen.replace("_", " ") #retorna _ no lugar de espaço em branco
+            return json.dumps({"fen": fen})
+        else:
+            raise Exception("Requisição feita, mas falhou na predição.")
+    else:
+        raise Exception(f"Erro na requisição: {response.status_code}")

langchain_agent.py

@@ -15,6 +15,7 @@ from botanical_classification_tool import get_botanical_classification
 from excel_parser_tool import parse_excel
 from analyse_chess_position_tool import get_chess_best_move
 from convert_chessboard_image_to_fen_tool import convert_chessboard_image_to_fen
+from chess_image_to_fen_tool import chess_image_to_fen
 
 
 class LangChainAgent:
@@ -22,7 +23,7 @@ class LangChainAgent:
         llm = ChatGoogleGenerativeAI(
             model=constants.MODEL,
             api_key=constants.API_KEY, 
-            temperature=0.0,
+            temperature=0.4,
             timeout=20)
 
         tools = [
@@ -32,7 +33,8 @@ class LangChainAgent:
             internet_search,
             get_botanical_classification,
             parse_excel,
-            convert_chessboard_image_to_fen,
+            #convert_chessboard_image_to_fen,
+            chess_image_to_fen,
             get_chess_best_move
         ]
 
@@ -43,7 +45,7 @@ class LangChainAgent:
             MessagesPlaceholder(variable_name="agent_scratchpad"),
         ])
 
-        agent = create_openai_functions_agent(llm, tools, prompt=prompt)
+        agent = create_tool_calling_agent(llm, tools, prompt=prompt)
         self.executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
 
     def __call__(self, question: str) -> str: