import os
import re
import gradio as gr
import requests
import pandas as pd
from huggingface_hub import InferenceClient

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

# --- Smart Agent HF ---
class BasicAgent:
    def __init__(self):
        # Usa token de HF si existe (opcional pero recomendable)
        self.client = InferenceClient(
            token=os.environ.get("HF_TOKEN")
        )
        print("HF Agent initialized.")


    def clean_answer(self, answer: str) -> str:
        answer = answer.strip()
    
        # quitar todo lo que no sea necesario
        answer = answer.split("\n")[0]
        answer = answer.split(".")[0]
        answer = answer.split(",")[0]
    
        # quitar frases típicas
        import re
        answer = re.sub(r"(?i)^.*answer is[:\s]*", "", answer)
    
        return answer.strip()



    def __call__(self, question: str) -> str:
        print(f"Question: {question[:100]}")
    
        q = question.lower()
    
        # ✅ fallback SIEMPRE (evita blanks)
        fallback = "unknown"
    
        # ✅ 1. detectar números simples
        import re
        nums = re.findall(r"\d+", question)
        if "how many" in q and nums:
            return nums[-1]
    
        # ✅ 2. matemáticas simples
        if any(x in q for x in ["sum", "add", "multiply", "divide"]):
            try:
                expr = re.findall(r"[0-9\+\-\*\/\.\(\) ]+", question)[0]
                return str(eval(expr))
            except:
                pass
    
        # ✅ 3. llamada HF con protección
        try:
            response = self.client.text_generation(
                model="google/flan-t5-large",
                prompt=f"Answer with one word or number: {question}",
                max_new_tokens=20
            )
    
            # ✅ controlar respuesta vacía
            if not response or response.strip() == "":
                print("Empty HF response → fallback")
                return fallback
    
            answer = response.strip()
    
            # limpiar
            answer = answer.split("\n")[0]
            answer = answer.split(".")[0]
            answer = answer.split(",")[0].strip()
    
            if answer == "":
                return fallback
    
            return answer
    
        except Exception as e:
            print(f"HF error: {e}")
            return fallback




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:
        return "Please Login to Hugging Face.", None

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

    # Crear agente
    agent = BasicAgent()
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    # Obtener preguntas
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
    except Exception as e:
        return f"Error fetching questions: {e}", None

    results_log = []
    answers_payload = []

    # Ejecutar agente
    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:
            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
            })

        except Exception as e:
            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": f"ERROR: {e}"
            })

    if not answers_payload:
        return "No answers generated.", pd.DataFrame(results_log)

    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload
    }

    # Enviar resultados
    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"Score: {result_data.get('score')}% "
            f"({result_data.get('correct_count')}/{result_data.get('total_attempted')})"
        )

        return final_status, pd.DataFrame(results_log)

    except Exception as e:
        return f"Submission failed: {e}", pd.DataFrame(results_log)


# --- UI ---
with gr.Blocks() as demo:
    gr.Markdown("# HF Free Agent")

    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit")

    status_output = gr.Textbox(label="Result", lines=5)
    results_table = gr.DataFrame()

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    demo.launch(debug=True)