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

from agent import create_agent, _clean_answer, build_question_prompt

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


def run_and_submit_all(username: str):
    space_id = os.getenv("SPACE_ID")

    if not username or not username.strip():
        return "Please enter your Hugging Face username.", None

    username = username.strip()
    print(f"User: {username}")

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

    try:
        agent = create_agent()
    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(f"Agent code URL: {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:
        return f"Error fetching questions: {e}", None
    except requests.exceptions.JSONDecodeError as e:
        return f"Error decoding server response for questions: {e}", None
    except Exception as e:
        return f"An unexpected error occurred fetching questions: {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")
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue

        prompt = build_question_prompt(item)

        try:
            raw_output = agent.run(prompt)
            answer = _clean_answer(raw_output)
            print(f"Task {task_id}: raw={raw_output!r}  cleaned={answer!r}")
        except Exception as e:
            print(f"Error running agent on task {task_id}: {e}")
            answer = f"AGENT ERROR: {e}"

        answers_payload.append({"task_id": task_id, "submitted_answer": answer})
        results_log.append({
            "Task ID": task_id,
            "Question": question_text,
            "Submitted Answer": answer,
        })

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

    submission_data = {
        "username": username,
        "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.')}"
        )
        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]}"
        return f"Submission Failed: {error_detail}", pd.DataFrame(results_log)
    except requests.exceptions.Timeout:
        return "Submission Failed: The request timed out.", 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"An unexpected error occurred during submission: {e}", pd.DataFrame(results_log)


with gr.Blocks() as demo:
    gr.Markdown("# GAIA Benchmark Agent Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Enter your Hugging Face username below.
        2. Click **Run Evaluation & Submit All Answers** to run the agent on all questions and submit.
        3. Results and a per-question breakdown will appear below.

        ---
        **Note:** Running all questions takes several minutes. Each question triggers multi-step reasoning.
        """
    )

    username_input = gr.Textbox(label="Hugging Face Username", placeholder="e.g. john_doe")
    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,
        inputs=[username_input],
        outputs=[status_output, results_table],
    )

if __name__ == "__main__":
    print("\n" + "-" * 30 + " App Starting " + "-" * 30)
    space_host = os.getenv("SPACE_HOST")
    space_id = os.getenv("SPACE_ID")

    if space_host:
        print(f"SPACE_HOST: {space_host}")
        print(f"  Runtime URL: https://{space_host}.hf.space")
    else:
        print("SPACE_HOST not found (running locally?).")

    if space_id:
        print(f"SPACE_ID: {space_id}")
        print(f"  Repo URL: https://huggingface.co/spaces/{space_id}/tree/main")
    else:
        print("SPACE_ID not found (running locally?).")

    print("-" * 60 + "\n")
    demo.launch(debug=True, share=False)