import os
import gradio as gr
import requests
import pandas as pd
import spaces
from langchain_google_genai import ChatGoogleGenerativeAI

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


class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        llm = ChatGoogleGenerativeAI(
            model="gemini-2.0-flash",
            temperature=1
        )
        fixed_answer = llm.invoke(question).content
        print(f"Agent returning fixed answer: {fixed_answer}")
        return fixed_answer


@spaces.GPU(duration=120)
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 from questions endpoint: {e}")
        print(f"Response text: {response.text[:500]}")
        return f"Error decoding server response for questions: {e}", None

    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {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

        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:
            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 "
        f"{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', '?')}/"
            f"{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 "
            f"{e.response.status_code}."
        )

        try:
            error_json = e.response.json()
            error_detail += (
                f" Detail: "
                f"{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

    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


with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")

    gr.Markdown(
        """
        **Instructions:**

        1. Clone this Space and modify the code to define your agent's logic.
        2. Log in to your Hugging Face account using the button below.
        3. Click 'Run Evaluation & Submit All Answers' to run the evaluation.

        **Disclaimers:**

        The evaluation may take some time because the agent processes all questions.
        """
    )

    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}"
        )
    else:
        print(
            "SPACE_HOST environment variable not found."
        )

    if space_id_startup:
        print(f"SPACE_ID found: {space_id_startup}")
        print(
            f"Repo URL: "
            f"https://huggingface.co/spaces/{space_id_startup}"
        )
        print(
            f"Repo Tree URL: "
            f"https://huggingface.co/spaces/"
            f"{space_id_startup}/tree/main"
        )
    else:
        print(
            "SPACE_ID environment variable not found."
        )

    print("-" * 60)
    print("Launching Gradio Interface for Basic Agent Evaluation...")

    demo.launch(
        debug=True,
        share=False
    )