Ajuste Chess + execução tabela comutativa

agent.py

@@ -64,7 +64,7 @@ class Agent:
                 ]
             }
         )
-        print('........ Versão: Execução Frase Invertida e Chess  .....')
+        print('........ Versão: Ajuste Chess + execução tabela comutativa  .....')
         print(f"Resposta LLM: {response}")
         # Extrair o conteúdo das mensagens do tipo AIMessage
         final_content = ""

constantes.py

@@ -35,7 +35,8 @@ HUGGINGFACE_DATASET_URL_TEMPLATE = (
 LISTA_TASKS_PROCESSAR = [
 #    "8e867cd7-cff9-4e6c-867a-ff5ddc2550be",
 #    "a1e91b78-d3d8-4675-bb8d-62741b4b68a6",
-     "2d83110e-a098-4ebb-9987-066c06fa42d0",
-     "cca530fc-4052-43b2-b130-b30968d8aa44"   
+#    "2d83110e-a098-4ebb-9987-066c06fa42d0",
+     "cca530fc-4052-43b2-b130-b30968d8aa44",
+     "6f37996b-2ac7-44b0-8e68-6d28256631b4"   
     
 ]

perguntas.json

@@ -28,6 +28,7 @@
     "detalhes": "Se quiser resposta direta precisa do gpt4o, caso contrário utilizar chessboard-image-parser para gerar um fen da imagem e repassar ao gpt-4o-mini, "
   },
   {
+    "nao_testado": "true",
     "task_id": "4fc2f1ae-8625-45b5-ab34-ad4433bc21f8",
     "question": "Who nominated the only Featured Article on English Wikipedia about a dinosaur that was promoted in November 2016?",
     "Level": "1",

tools.py

@@ -107,7 +107,9 @@ def chess_image_to_fen_tool(image_path:str, current_player: Literal["black", "wh
     if current_player not in ["black", "white"]:
         raise ValueError("current_player must be 'black' or 'white'")
 
-    base64_image = Image_Util.encode_image_to_base64(image_path)
+    final_image_path= os.path.join(AGENTS_FILES_PATH, image_path)
+    
+    base64_image = Image_Util.encode_image_to_base64(final_image_path)
     if not base64_image:
         raise ValueError("Failed to encode image to base64.")
     base64_image_encoded =  f"data:image/jpeg;base64,{base64_image}"