Update app.py

app.py

@@ -2,182 +2,106 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
-from agent import DuckDuckGoAgent
+from dotenv import load_dotenv
 
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+load_dotenv()  # Cargar variables de entorno
 
-# --- Basic Agent Definition ---
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
+# Función para ejecutar y enviar todas las respuestas
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the DuckDuckGoAgent on them, submits all answers,
-    and displays the results.
+    Recoge todas las preguntas, ejecuta el agente sobre ellas, envía las respuestas y muestra los resultados.
     """
-    space_id = os.getenv("SPACE_ID")
+    space_id = os.getenv("SPACE_ID")  # ID del espacio para enlaces al código
 
     if profile:
         username = f"{profile.username}"
-        print(f"User logged in: {username}")
+        print(f"Usuario logueado: {username}")
     else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
+        print("Usuario no logueado.")
+        return "Por favor, inicia sesión en Hugging Face.", None
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
+    attachments_url = f"{api_url}/files/"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent
+    # Crear agente (modificado)
     try:
-        agent = DuckDuckGoAgent()  # Instanciando DuckDuckGoAgent
+        print("Iniciando agente...")
+        agent = agent.BasicAgent()  # Usar el agente principal
     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)
+        print(f"Error al iniciar el agente: {e}")
+        return f"Error al iniciar el agente: {e}", None
 
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
+    # 2. Recoger las preguntas
+    print(f"Recogiendo preguntas desde: {questions_url}")
     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
+            print("La lista de preguntas está vacía.")
+            return "La lista de preguntas está vacía.", None
+        print(f"Se recogieron {len(questions_data)} preguntas.")
+    except Exception as e:
+        print(f"Error recogiendo preguntas: {e}")
+        return f"Error recogiendo preguntas: {e}", None
 
-    # 3. Run Agent
+    # 3. Ejecutar el agente
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
+    print(f"Ejecutando el agente sobre {len(questions_data)} preguntas...")
     for item in questions_data:
         task_id = item.get("task_id")
-        question_text = item.get("question")
+        question_text = item.get("question", "")
+        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 question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
+            print(f"Saltando tarea con ID o pregunta faltante: {item}")
             continue
         try:
-            submitted_answer = agent(question_text)  # Usando el agente DuckDuckGo
+            submitted_answer = agent.forward(question_text)  # Respuesta del agente
             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 running agent on task {task_id}: {e}")
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+            print(f"Error ejecutando agente en tarea {task_id}: {e}")
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"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)
-
-    # 4. Prepare Submission
-    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)
-
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    # 4. Enviar las respuestas
+    submission_data = {"username": username.strip(), "agent_code": "agent_code_placeholder", "answers": answers_payload}
     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.')}"
-        )
-        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
+        final_status = f"¡Envío exitoso!\nUsuario: {result_data.get('username')}\nPuntaje final: {result_data.get('score', 'N/A')}%"
+        print("Envío exitoso.")
+        return final_status, pd.DataFrame(results_log)
     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
-    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
-
+        print(f"Error al enviar respuestas: {e}")
+        return f"Error al enviar respuestas: {e}", pd.DataFrame(results_log)
 
-
-# --- Build Gradio Interface using Blocks ---
+# Interfaz Gradio
 with gr.Blocks() as demo:
-    gr.Markdown("# DuckDuckGo Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-        ---
-        **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time (this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution.
-        """
-    )
-
-    gr.LoginButton()
+    gr.Markdown("# Evaluación Básica del Agente")
+    gr.Markdown("""
+    **Instrucciones:**
+    1. Modifica este espacio con tu lógica de agente y las herramientas necesarias.
+    2. Inicia sesión en Hugging Face con el botón abajo.
+    3. Haz clic en 'Ejecutar Evaluación y Enviar Todas las Respuestas' para obtener resultados.
 
-    run_button = gr.Button("Run Evaluation & Print Results")
+    **Aviso:**
+    Puede tomar tiempo procesar las respuestas, así que ten paciencia.
+    """)
 
-    status_output = gr.Textbox(label="Run Status / Evaluation Result", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+    gr.LoginButton()
+    run_button = gr.Button("Ejecutar Evaluación y Enviar Todas las Respuestas")
+    status_output = gr.Textbox(label="Resultado de Ejecución / Envío", lines=5, interactive=False)
+    results_table = gr.DataFrame(label="Preguntas y Respuestas del Agente", 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__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID")
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    if space_id_startup:
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for DuckDuckGo Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)