Update app.py

app.py

@@ -1,36 +1,10 @@
-import os
-import gradio as gr
-import requests
-import pandas as pd
-from huggingface_hub import login
-from dotenv import load_dotenv
-
-# Cargar variables de entorno
-load_dotenv()
-API_BASE_URL = "https://my-custom-api.hf.space"
-MODEL_NAME = os.getenv("MODEL_NAME", "meta-llama/Meta-Llama-3-8B-Instruct")  # o usa 'google/gemma-2b-it'
-
-def call_model(prompt):
-    headers = {
-        "Authorization": f"Bearer {os.environ['HF_TOKEN']}",
-        "Content-Type": "application/json"
-    }
-    payload = {
-        "inputs": prompt,
-        "parameters": {"max_new_tokens": 512}
-    }
-    response = requests.post(
-        f"https://api-inference.huggingface.co/models/{MODEL_NAME}",
-        headers=headers,
-        json=payload,
-        timeout=60
-    )
-    result = response.json()
-    if isinstance(result, dict) and result.get("error"):
-        return f"ERROR: {result['error']}"
-    return result[0]["generated_text"] if isinstance(result, list) else result
+import time
 
 def execute_agent_operations(profile: gr.OAuthProfile | None):
+    """
+    Obtiene preguntas, ejecuta el agente y envía respuestas.
+    """
+    # Obtener el ID de espacio
     space_id = os.getenv("MY_SPACE_ID")
 
     if profile:
@@ -44,76 +18,83 @@ def execute_agent_operations(profile: gr.OAuthProfile | None):
     attachments_url = f"{API_BASE_URL}/files/"
     submit_url = f"{API_BASE_URL}/submit"
 
+    try:
+        print("Iniciando Agente...")
+        # Aquí puedes instanciar tu agente como se define en agent.py
+        # agente = MiAgente() 
+    except Exception as e:
+        print(f"Error al inicializar agente: {e}")
+        return f"Error al inicializar el agente: {e}", None
+
+    # Obtener las preguntas
+    print(f"Obteniendo preguntas de: {questions_url}")
     try:
         response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions = response.json()
         if not questions:
+            print("La lista de preguntas está vacía.")
             return "No se encontraron preguntas.", None
+        print(f"Se obtuvieron {len(questions)} preguntas.")
         
-        for q in questions:
-            task_id = q.get("task_id")
+        # Aquí agregamos el retraso entre cada solicitud para evitar el error 429
+        for i, q in enumerate(questions):
+            if i > 0:  # Para no esperar después de la primera solicitud
+                time.sleep(2)  # Espera de 2 segundos entre cada consulta
+            
             file_name = q.get("file_name", "")
-            if task_id and file_name:
+            task_id = q.get("task_id")
+            if file_name and task_id:
                 try:
                     att_response = requests.get(f"{attachments_url}{task_id}", timeout=15)
                     att_response.raise_for_status()
                     q["attachment_b64"] = att_response.text
                 except Exception as e:
-                    print(f"Error al obtener adjunto: {e}")
+                    print(f"Error al obtener archivo adjunto para la tarea {task_id}: {e}")
                     q["attachment_b64"] = None
-    except Exception as e:
+    except requests.exceptions.RequestException as e:
+        print(f"Error al obtener preguntas: {e}")
         return f"Error al obtener preguntas: {e}", None
 
+    # Ejecutar el agente
+    print(f"Ejecutando agente en {len(questions)} preguntas...")
     results_log = []
     answers_payload = []
-
+    
     for item in questions:
         task_id = item.get("task_id")
         question_text = item.get("question", "")
-        attachment = item.get("attachment_b64", "")
-        full_prompt = f"{question_text}\n\nArchivo adjunto:\n{attachment}" if attachment else question_text
-
-        if not task_id or not full_prompt:
+        attachment_b64 = item.get("attachment_b64", "")
+        if attachment_b64:
+            question_text = f"{question_text}\n\n[ATTACHMENT:]\n{attachment_b64}"
+        if not task_id or not question_text:
+            print(f"Saltando tarea con información incompleta: {item}")
             continue
-
         try:
-            submitted_answer = call_model(full_prompt)
+            submitted_answer = "Placeholder"  # Aquí llamarías a tu agente para obtener la respuesta
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
         except Exception as e:
+            print(f"Error al procesar la tarea {task_id}: {e}")
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"ERROR: {e}"})
 
     if not answers_payload:
         return "El agente no produjo respuestas.", pd.DataFrame(results_log)
 
-    submission_data = {
-        "username": username.strip(),
-        "agent_code": f"https://huggingface.co/spaces/{space_id}",
-        "answers": answers_payload
-    }
+    submission_data = {"username": username.strip(), "agent_code": f"https://huggingface.co/spaces/{space_id}", "answers": answers_payload}
+    status_update = f"Enviando respuestas para el usuario '{username}'..."
+    print(status_update)
 
     try:
-        post_response = requests.post(submit_url, json=submission_data, timeout=60)
-        post_response.raise_for_status()
-        result_data = post_response.json()
-        score = result_data.get("score", "N/A")
-        return f"¡Envío exitoso!\nPuntuación: {score}", pd.DataFrame(results_log)
-    except Exception as e:
-        return f"Error al enviar: {e}", pd.DataFrame(results_log)
-
-# Interfaz Gradio
-with gr.Blocks() as demo:
-    gr.Markdown("# Evaluador de Agente (versión personalizada)")
-    gr.LoginButton()
-    run_button = gr.Button("Ejecutar Evaluación y Enviar Respuestas")
-    status_output = gr.Textbox(label="Estado", lines=3)
-    results_table = gr.DataFrame(label="Respuestas del agente")
-
-    run_button.click(
-        fn=execute_agent_operations,
-        outputs=[status_output, results_table]
-    )
-
-if __name__ == "__main__":
-    demo.launch(debug=True)
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = f"¡Envío exitoso!\nPuntuación: {result_data.get('score', 'N/A')}"
+        print("Envío exitoso.")
+        results_df = pd.DataFrame(results_log)
+        return final_status, results_df
+    except requests.exceptions.RequestException as e:
+        error_message = f"Error al enviar: {e}"
+        print(error_message)
+        results_df = pd.DataFrame(results_log)
+        return error_message, results_df