import os
import gradio as gr
import requests
import pandas as pd
import re

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

# --- Basic Agent Definition ---
class BasicAgent:
    def __init__(self):
        print("✅ BasicAgent initialized.")

    def __call__(self, question: str) -> str:
        """
        Process a question and return an answer.
        Handles basic arithmetic, string extraction, and fallback for other tasks.
        """
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        try:
            numbers = [float(n) for n in re.findall(r"\d+\.?\d*", question)]
            q_lower = question.lower()

            # Basic addition
            if ("sum" in q_lower or "add" in q_lower) and numbers:
                answer = str(sum(numbers))

            # Basic multiplication
            elif "multiply" in q_lower and numbers:
                product = 1
                for n in numbers: 
                    product *= n
                answer = str(product)

            # Extract first letter (example task type)
            elif "first letter" in q_lower:
                words = question.strip().split()
                answer = words[0][0] if words else "N/A"

            # Default: return first 5 words
            else:
                answer = " ".join(question.strip().split()[:5])

        except Exception as e:
            answer = f"ERROR: {e}"

        print(f"Agent returning answer: {answer}")
        return answer

# --- Run & Submit Function ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetch all questions, run the BasicAgent on them, submit all answers,
    and display the results.
    """
    space_id = os.getenv("SPACE_ID")
    if profile:
        username = profile.username
        print(f"User logged in: {username}")
    else:
        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"

    # Instantiate agent
    try:
        agent = BasicAgent()
    except Exception as e:
        return f"Error initializing agent: {e}", None

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    # Fetch questions
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # Run agent on each question
    results_log = []
    answers_payload = []

    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"AGENT ERROR: {e}"
            })

    if not answers_payload:
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # Submit
    submission_data = {"username": username.strip(), "agent_code": agent_code, "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.')}"
        )
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except Exception as e:
        results_df = pd.DataFrame(results_log)
        return f"Submission Failed: {e}", results_df

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    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 your score.
        """
    )
    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])

# --- Launch App ---
if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    demo.launch(debug=True, share=False)