import os
import gradio as gr
import requests
import pandas as pd
from agents import LangAgent

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


def resolve_user(profile: gr.OAuthProfile | None):
    if not profile:
        print("User not logged in.")
        return None, "Please Login to Hugging Face with the button."
    username = f"{profile.username}"
    print(f"User logged in: {username}")
    return username, None


def build_agent():
    try:
        return LangAgent(), None
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return None, f"Error initializing agent: {e}"


def build_agent_code(space_id: str | None):
    return f"https://huggingface.co/spaces/{space_id}/tree/main"


def fetch_questions(questions_url: str):
    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 None, "Fetched questions list is empty or invalid format."
        print(f"Fetched {len(questions_data)} questions.")
        return questions_data, None
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return None, f"Error fetching questions: {e}"
    except requests.exceptions.JSONDecodeError as e:
        print(f"Error decoding JSON response from questions endpoint: {e}")
        return None, f"Error decoding server response for questions: {e}"
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return None, f"An unexpected error occurred fetching questions: {e}"


def log_backend_file_status(with_file, total_count: int, api_url: str):
    without_file = total_count - len(with_file)
    print(f"Questions with file_name set: {len(with_file)}; without: {without_file}")
    for q in with_file:
        print(f"Task {q.get('task_id')} expects file: {q.get('file_name')}")
    for q in with_file:
        task_id = q.get("task_id")
        file_url = f"{api_url}/files/{task_id}"
        try:
            probe = requests.get(file_url, timeout=15)
            print(
                f"Attempted access to resource at {file_url} -> "
                f"status {probe.status_code}, "
                f"content-type {probe.headers.get('content-type')}, "
                f"bytes {len(probe.content)}"
            )
        except Exception as e:
            print(f"Attempted access to resource at {file_url} -> error: {e}")


def get_hf_token(profile: gr.OAuthProfile | None):
    token = None
    if profile:
        try:
            print("Profile attributes:", list(profile.__dict__.keys()))
        except Exception as e:
            print(f"Could not inspect profile attributes: {e}")
        for attr in ("access_token", "token"):
            token = getattr(profile, attr, None)
            if token:
                print(f"Using token from profile.{attr}")
                break
        if not token:
            for attr in ("tokens", "auth"):
                container = getattr(profile, attr, None)
                if isinstance(container, dict):
                    token = container.get("access_token") or container.get("token")
                    if token:
                        print(f"Using token from profile.{attr}")
                        break
    if not token:
        token = (
            os.getenv("HF_TOKEN")
            or os.getenv("HUGGINGFACEHUB_API_TOKEN")
            or os.getenv("HUGGINGFACE_HUB_TOKEN")
        )
        if token:
            print("Using token from environment.")
    if not token:
        try:
            try:
                from huggingface_hub import HfFolder  # older versions
            except Exception:
                from huggingface_hub.hf_api import HfFolder  # fallback for space runtime
            token = HfFolder.get_token()
            if token:
                print("Using token from local HF cache (huggingface-cli login).")
        except Exception as e:
            print(f"Could not load token from local HF cache: {e}")
    if token:
        # Avoid printing full token; show a short preview for debugging.
        print(f"HF token obtained (length {len(token)}).")
    else:
        print("No HF token available from profile or environment.")
    return token


def download_gaia_file(file_name: str, token: str | None):
    """Download a GAIA validation file by name from the pinned revision."""
    try:
        from huggingface_hub import hf_hub_download
    except Exception as e:
        print(f"Cannot download {file_name}: huggingface_hub unavailable ({e}).")
        return None

    repo_id = "gaia-benchmark/GAIA"
    revision = "86620fe7a265fdd074ea8d8c8b7a556a1058b0af"
    path_in_repo = f"2023/validation/{file_name}"
    try:
        local_path = hf_hub_download(
            repo_id=repo_id,
            filename=path_in_repo,
            repo_type="dataset",
            token=token,  # can be None if huggingface-cli cache is available
            revision=revision,
        )
        print(f"Downloaded GAIA file {file_name} to {local_path}")
        return local_path
    except Exception as e:
        print(f"Failed to download GAIA file {file_name}: {e}")
        return None


def resolve_file(file_name: str | None, token: str | None):
    if not file_name:
        return None
    # Prefer local cache if present.
    candidate = os.path.join("validation", file_name)
    if os.path.exists(candidate):
        print(f"Local file found: {candidate}")
        return candidate
    print(f"No local file found (expected {candidate}), trying GAIA download.")
    return download_gaia_file(file_name, token)


def run_agent_on_questions(agent, questions_data, token: str | 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")
        file_name = item.get("file_name")
        file_path = resolve_file(file_name, token)
        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, file_path=file_path)
            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}"})
    return answers_payload, results_log


def submit_answers(submit_url: str, username: str, agent_code: str, answers_payload, results_log):
    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)

    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


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetch questions, run the agent, and submit answers.
    """
    space_id = os.getenv("SPACE_ID")
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    username, user_error = resolve_user(profile)
    if user_error:
        return user_error, None

    agent, agent_error = build_agent()
    if agent_error:
        return agent_error, None

    agent_code = build_agent_code(space_id)
    print(agent_code)

    questions_data, fetch_error = fetch_questions(questions_url)
    if fetch_error:
        return fetch_error, None

    with_file = [q for q in questions_data if q.get("file_name")]
    log_backend_file_status(with_file, len(questions_data), api_url)
    token = get_hf_token(profile)
    answers_payload, results_log = run_agent_on_questions(agent, questions_data, token)
    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)

    return submit_answers(submit_url, username, agent_code, answers_payload, results_log)


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    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]
    )

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("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)