app.py

import os
import gradio as gr
import requests
import pandas as pd
from transformers import pipeline

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"









# --- Basic Agent Definition ---
class GeneralAgent:
    def __init__(self):
        print("Initializing BERT-based QA agent...")
        # Cargar el modelo BERT preentrenado en SQuAD para tareas de Pregunta y Respuesta
        self.qa_pipeline = pipeline("question-answering", model="bert-large-uncased-whole-word-masking-finetuned-squad")
    
    def __call__(self, question: str, context: str = None) -> str:
        """
        Procesa la pregunta y devuelve una respuesta basada en el contexto proporcionado.
        Si no se proporciona contexto, devuelve un mensaje de error.
        """
        if context is None:
            return "FINAL ANSWER: No context provided."
        
        # Crear un prompt dentro del contexto que estructure la tarea más explícitamente
        prompt = f"""
You are a general AI assistant. I will ask you a question based on the provided context. 
Please provide the answer in a clear and concise manner.
Question: {question}
Context: {context}
Answer:
"""
        
        try:
            # Usar el pipeline para obtener la respuesta de la pregunta con el contexto
            result = self.qa_pipeline(question=question, context=prompt)
            answer = result["answer"]
        except Exception as e:
            print(f"Error durante QA: {e}")
            answer = "Error processing question."
        
        # Devuelve la respuesta final con el formato requerido
        return f"FINAL ANSWER: {answer}"








        

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}")
    else:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    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)

    print(f"Fetching questions from: {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
    except requests.exceptions.JSONDecodeError as e:
        print(f"Error decoding JSON response: {e}")
        return f"Error decoding server response: {e}", None
    except Exception as e:
        print(f"Unexpected error: {e}")
        return f"Unexpected error: {e}", None

    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")
        context = item.get("context", question_text)  # Usa 'context' si viene, si no, la propia pregunta

        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, context)
            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}"})

    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)

    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 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.')}"
        )
        print("Submission successful.")
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except requests.exceptions.HTTPError as e:
        error_detail = f"HTTP error {e.response.status_code}: {e.response.text[:300]}"
        return f"Submission Failed: {error_detail}", pd.DataFrame(results_log)
    except requests.exceptions.Timeout:
        return "Submission Failed: Timeout.", pd.DataFrame(results_log)
    except requests.exceptions.RequestException as e:
        return f"Submission Failed: Network error - {e}", pd.DataFrame(results_log)
    except Exception as e:
        return f"Submission Failed: Unexpected error - {e}", pd.DataFrame(results_log)

# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Clone this space and modify your agent.
        2. Log in to Hugging Face with the button below.
        3. Click 'Run Evaluation & Submit All Answers' to evaluate.

        ---
        **Note**: Submitting can take a while. This space is intentionally basic—improve it!
        """
    )

    gr.LoginButton()
    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]
    )

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: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST 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}")
    else:
        print("ℹ️  SPACE_ID not found. Repo URL cannot be determined.")

    print("-" * 70 + "\n")
    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)