Update app.py

app.py

@@ -1,16 +1,12 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
 from smolagents import CodeAgent
 from smolagents.models import InferenceClientModel
 from smolagents.tools import DuckDuckGoSearchTool
 
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Basic Agent Definition ---
+# --- Agente Inteligente usando flan-t5-large + DuckDuckGo ---
 class BasicAgent:
     def __init__(self):
         model = InferenceClientModel(model_id="google/flan-t5-large")
@@ -19,7 +15,7 @@ class BasicAgent:
         print("🤖 Agente inteligente inicializado.")
 
     def __call__(self, question: str) -> str:
-        print(f"❓ Recibida pregunta: {question[:50]}...")
+        print(f"❓ Pregunta recibida: {question[:50]}...")
         try:
             answer = self.agent.run(question).strip()
             print(f"✅ Respuesta generada: {answer}")
@@ -28,140 +24,98 @@ class BasicAgent:
             print(f"❌ Error del agente: {e}")
             return f"AGENT ERROR: {e}"
 
-# --- Evaluation and Submission ---
+# --- Evaluación y envío GAIA ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
 
     if profile:
-        username = f"{profile.username}"
-        print(f"User logged in: {username}")
+        username = profile.username
+        print(f"👤 Usuario: {username}")
     else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
+        return "⚠️ Por favor, inicia sesión en Hugging Face antes de enviar.", None
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
 
     try:
         agent = BasicAgent()
     except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
+        return f"❌ Error al crear el agente: {e}", None
 
-    print(f"Fetching questions from: {questions_url}")
+    # Descargar preguntas
     try:
         response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
-        if not questions_data:
-            print("Fetched questions list is empty.")
-            return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-        print(f"Error decoding JSON response from questions endpoint: {e}")
-        print(f"Response text: {response.text[:500]}")
-        return f"Error decoding server response for questions: {e}", None
     except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
+        return f"❌ Error al descargar preguntas: {e}", None
 
+    # Responder preguntas
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
+
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
         if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
             submitted_answer = agent(question_text)
             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})
+            results_log.append({
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": submitted_answer
+            })
         except Exception as e:
-            print(f"Error running agent on task {task_id}: {e}")
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
-
-    if not answers_payload:
-        print("Agent did not produce any answers to submit.")
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+            results_log.append({
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": f"AGENT ERROR: {e}"
+            })
+
+    # Enviar respuestas
+    submission_data = {
+        "username": username,
+        "agent_code": agent_code,
+        "answers": answers_payload
+    }
 
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
-
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
         response.raise_for_status()
         result_data = response.json()
         final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
+            f"✅ ¡Envío realizado con éxito!\n"
+            f"👤 Usuario: {result_data.get('username')}\n"
+            f"📊 Puntuación: {result_data.get('score', 'N/A')}% "
+            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correctas)\n"
+            f"📬 Mensaje: {result_data.get('message', 'Sin mensaje.')}"
         )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+        return final_status, pd.DataFrame(results_log)
     except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+        return f"❌ Error durante el envío: {e}", pd.DataFrame(results_log)
 
-# --- Gradio Interface ---
+# --- Interfaz Gradio ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# 🧠 Evaluador Agente GAIA - Curso Hugging Face")
     gr.Markdown("""
-        **Instructions:**
-
-        1. Log in to your Hugging Face account using the button below.
-        2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-        ---
-        This app uses a basic agent. Improve its logic and tools to boost performance!
+    1. Inicia sesión en Hugging Face.
+    2. Pulsa el botón para ejecutar tu agente y enviar las respuestas.
+    3. Espera unos minutos y revisa la puntuación.
     """)
 
     gr.LoginButton()
+    run_button = gr.Button("▶️ Ejecutar y Enviar Respuestas")
+    status_output = gr.Textbox(label="Resultado", lines=6, interactive=False)
+    results_table = gr.DataFrame(label="Respuestas Generadas")
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
 
 if __name__ == "__main__":
-    demo.launch(debug=True, share=False)
+    print("🚀 Lanzando interfaz...")
+    demo.launch(debug=True)