import os
import shutil
import tempfile
import time
import uuid
from pathlib import Path

import gradio as gr
import pandas as pd
import requests

from config.settings import config
from core.agent import GaiaAgent, Attachment
from utils.cache_answers import AnswersCache
from utils.dependencies_checker import check_dependencies

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"


def get_question_attached_file(task_id, file_name) -> Attachment:
    api_url = DEFAULT_API_URL
    attachment_url = f"{api_url}/files/{task_id}"

    print(f"Fetching attachment from: {attachment_url}")

    try:
        response = requests.get(attachment_url, timeout=15)
        response.raise_for_status()

        print(f"Retrieved {file_name} attachment from: {attachment_url}")

        # Save to disk
        file_path = Path("attachments") / f"{task_id}" / f"{file_name}"
        content = response.content

        # Create parent directories if they don't exist
        file_path.parent.mkdir(parents=True, exist_ok=True)

        # Write the file
        file_path.write_bytes(content)

        return Attachment(content, file_path.as_posix())

    except Exception as e:
        print(f"An unexpected error occurred fetching attachment for taskid{task_id}: {e}")


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID")  # Get the SPACE_ID for sending link to the code

    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"

    # 1. Instantiate Agent ( modify this part to create your agent)
    try:
        agent = GaiaAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)

    # 2. Fetch Questions
    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

    # 3. Run your Agent
    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")
    cache = AnswersCache()
    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        attached_file_name = item.get("file_name")
        attachment = None
        if attached_file_name:
            attachment = get_question_attached_file(task_id, attached_file_name)
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue

        if task_id in ["a1e91b78-d3d8-4675-bb8d-62741b4b68a6"]:
            print(f"Skipping question. Not handled for the moment: {item}")
            cache.set(task_id, "NAN")
            continue
        try:
            # check if the answer is cached, if not invoke the agent
            submitted_answer = cache.get(task_id)
            if submitted_answer is None:
                print(f"Agent received question (first 50 chars): {question_text[:50]}...")
                if attachment:
                    print(f"Agent received an attachment : {attachment.file_path}...")
                submitted_answer = agent(question_text, attachment)
                print(f"Agent returning fixed answer: {submitted_answer}")
                # sleep in
                time.sleep(30)
                cache.set(task_id, submitted_answer)
            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)

    # 4. Prepare Submission 
    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)

    # 5. Submit
    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]}"
        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


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo_submit:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**

        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.

        ---
        **Disclaimers:**
        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
        """
    )

    gr.LoginButton()

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

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    # Removed max_rows=10 from DataFrame constructor
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

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


def process_input(question: str, file: gr.File):
    """
    Process the user's question and attached file
    """
    if not question:
        return "Please enter a question."

    # Extract file information
    attachment = None
    if file is not None:
        print(f"Received file {file.name} ")

        # Save to disk
        task_id = uuid.uuid4()
        file_name = Path(file.name).name
        file_path = Path("uploads") / f"{task_id}" / f"{file_name}"

        # # Create parent directories if they don't exist
        file_path.parent.mkdir(parents=True, exist_ok=True)
        shutil.copy(file, file_path)

        content = file_path.read_bytes()
        attachment = Attachment(content, file_path.as_posix())

    response = agent(question, attachment)

    return response


with gr.Blocks(title="🐉 GAIA Agent Demo", theme=gr.themes.Ocean()) as demo:
    gr.Markdown("# 🐉 GAIA Agent")
    gr.Markdown("Ask me a complex question")

    with gr.Row():
        with gr.Column(scale=1):
            question_input = gr.Textbox(
                label="Your Question",
                placeholder="Type your question here",
                lines=5
            )

            file_input = gr.File(
                label="Attach File",
                file_types=[
                    ".txt", ".pdf", ".png", ".jpg", ".jpeg",
                    ".csv", ".py", ".mp3", ".xslx"
                ]
            )

            submit_btn = gr.Button("Submit", variant="primary")

        with gr.Column(scale=2):
            output = gr.Textbox(label="Response", lines=10)

    # Set up the submission action
    submit_btn.click(
        fn=process_input,
        inputs=[question_input, file_input],
        outputs=output
    )

    # Also process when file is uploaded (optional)
    file_input.upload(
        fn=process_input,
        inputs=[question_input, file_input],
        outputs=output
    )

if __name__ == "__main__":
    print("\n" + "-" * 30 + " App Starting " + "-" * 30)
    # Check for SPACE_HOST and SPACE_ID at startup for information
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")  # Get SPACE_ID at startup

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    if space_id_startup:  # Print repo URLs if SPACE_ID is found
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")

    print("-" * (60 + len(" App Starting ")) + "\n")
    print("-" * (60 + len(" Check dependencies ")) + "\n")
    check_dependencies()
    agent = GaiaAgent()

    print(f"SUBMISSION MODE FLAG ={config.submission_mode_on}")
    if config.submission_mode_on:
        print("Launching Gradio Interface for Basic Agent Evaluation...")
        demo_submit.launch(debug=True, share=False)
    else:
        print("Launching The GAIA Agent...")
        demo.launch(debug=True, share=False)