import json
import os
import smtplib
from datetime import datetime, timezone

from src.display.formatting import styled_error, styled_message, styled_warning
from src.envs import API, EVAL_REQUESTS_SUBGRAPH, EVAL_REQUESTS_CAUSALGRAPH, TOKEN, QUEUE_REPO_SUBGRAPH, QUEUE_REPO_CAUSALGRAPH
from src.submission.check_validity import (
    already_submitted_models,
    get_model_size,
    is_model_on_hub,
    is_valid_predictions,
    parse_huggingface_url
)
import gradio as gr

REQUESTED_MODELS = None
USERS_TO_SUBMISSION_DATES = None

def upload_to_queue(track, hf_repo_circ, hf_repo_cg, level, method_name, contact_email, _id):
    errors = []
    hf_repo = hf_repo_circ if "Circuit" in track else hf_repo_cg
    repo_id, folder_path, revision = parse_huggingface_url(hf_repo)

    try:
        user_name, repo_name = repo_id.split("/")
    except Exception as e:
        errors.append("Error processing HF URL: could not get username and repo name")
    if revision is None or revision == "main":
        try:
            commit_hash = API.list_repo_commits(repo_id)[0].commit_id
        except Exception as e:
            errors.append("Could not get commit hash of provided Huggingface repo")
    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")

    if not errors:
        if "Circuit" in track:
            eval_entry = {
                "hf_repo": hf_repo,
                "user_name": user_name,
                "revision": commit_hash,
                "circuit_level": level.lower(),
                "method_name": method_name,
                "contact_email": contact_email.lower(),
                "submit_time": current_time,
                "status": "PREVALIDATION",
                "_id": _id
            }
            QUEUE_REPO = QUEUE_REPO_SUBGRAPH
            EVAL_REQUESTS = EVAL_REQUESTS_SUBGRAPH
        else:
            eval_entry = {
                "hf_repo": hf_repo,
                "user_name": user_name,
                "revision": commit_hash,
                "method_name": method_name,
                "contact_email": contact_email.lower(),
                "submit_time": current_time,
                "status": "PREVALIDATION",
                "_id": _id
            }
            QUEUE_REPO = QUEUE_REPO_CAUSALGRAPH
            EVAL_REQUESTS = EVAL_REQUESTS_CAUSALGRAPH

        OUT_DIR = f"{EVAL_REQUESTS}/"
        os.makedirs(OUT_DIR, exist_ok=True)
        out_path = f"{OUT_DIR}/{method_name}_{_id}_{current_time}.json"
        with open(out_path, 'w') as f:
            f.write(json.dumps(eval_entry))

        try:
            API.upload_file(
                path_or_fileobj=out_path,
                path_in_repo=out_path.split("/")[-1],
                repo_id=QUEUE_REPO,
                repo_type="dataset",
                commit_message=f"Add {method_name}_{_id}_{current_time}.json to eval queue"
            )
        except Exception as e:
            errors.append(f"Could not upload entry to eval queue: {e}")

    if errors:
        status = gr.Textbox("\n\n".join(f"❌ {e}" for e in errors), visible=True)
    else:
        status = gr.Textbox(f"✅ Submission received! Your submission ID is \"{_id}\". Save this so that you can manage your submission on the queue.", visible=True)
    return [
        status,
        None, None,
        gr.Column(visible=False)
    ]

def add_new_eval(
    model_name: str,
    model_id: str,
    revision: str,
    track: str,
    predictions: dict,
):
    global REQUESTED_MODELS
    global USERS_TO_SUBMISSION_DATES
    if not REQUESTED_MODELS:
        REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)

    out_message = ""

    user_name = ""
    model_path = model_name
    if "/" in model_name:
        user_name = model_name.split("/")[0]
        model_path = model_name.split("/")[1]

    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
    
    if track is None:
        return styled_error("Please select a track.")

    # Does the model actually exist?
    if revision == "":
        revision = "main"
    
    out_message = ""

    # Is the model info correctly filled?
    print("Made it before 1")
    try:
        model_info = API.model_info(repo_id=model_id, revision=revision)
    except Exception:
        out_message += styled_warning("Could not get your model information. The leaderboard entry will not have a link to its HF repo.") + "<br>"
    print("Made it after 1")

    try:
        predictions_OK, error_msg = is_valid_predictions(predictions)
        if not predictions_OK:
            return styled_error(error_msg) + "<br>"
    except:
        return styled_error(error_msg) + "<br>"

    print("Made it after 3")

    # Seems good, creating the eval
    print("Adding new eval")

    eval_entry = {
        "model_name": model_name,
        "hf_repo": model_id,
        "revision": revision,
        "track": track,
        "predictions": predictions,
        "status": "PENDING",
        "submitted_time": current_time,
    }

    print("Made it after 4")

    # Check for duplicate submission
    if f"{model_name}_{revision}_{track}" in REQUESTED_MODELS:
        return styled_error("A model with this name has been already submitted.")

    print("Creating eval file")
    OUT_DIR = f"{EVAL_REQUESTS}/{user_name}"
    os.makedirs(OUT_DIR, exist_ok=True)
    out_path = f"{OUT_DIR}/{model_path}_{revision}_eval_request_False_{track}.json"

    print("Made it after 5")

    with open(out_path, "w") as f:
        f.write(json.dumps(eval_entry))

    print("Uploading eval file")
    API.upload_file(
        path_or_fileobj=out_path,
        path_in_repo=out_path.split("eval-queue/")[1],
        repo_id=QUEUE_REPO,
        repo_type="dataset",
        commit_message=f"Add {model_name} to eval queue",
    )

    print("Made it after 6")

    # Remove the local file
    os.remove(out_path)

    return styled_message(
        "Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the request to show in the PENDING list."
    )

def remove_submission(track: str, method_name: str, _id: str):
    if track is None:
        return gr.Textbox(f"Please select a track.", visible=True)
    if "Circuit" in track:
        QUEUE_REPO = QUEUE_REPO_SUBGRAPH
        EVAL_REQUESTS = EVAL_REQUESTS_SUBGRAPH
    else:
        QUEUE_REPO = QUEUE_REPO_CAUSALGRAPH
        EVAL_REQUESTS = EVAL_REQUESTS_CAUSALGRAPH
    
    OUT_DIR = f"{EVAL_REQUESTS}/"
    os.makedirs(OUT_DIR, exist_ok=True)
    files = os.listdir(OUT_DIR)
    out_paths = [f for f in files if f.startswith(f"{method_name}_{_id}")]
    if out_paths:
        filename = out_paths[0]
        filepath = os.path.join(OUT_DIR, filename)
        with open(filepath, 'r') as f:
            data = json.load(f)
        hf_repo = data["hf_repo"]
        try:
            API.delete_file(
                path_in_repo=filename,
                repo_id=QUEUE_REPO,
                repo_type="dataset"
            )
        except Exception as e:
            return gr.Textbox(f"Could not delete entry from eval queue: {e}", visible=True)
        os.remove(filepath)
        status = "Submission removed from queue."
    else:
        status = "Submission not found in queue."

    return gr.Textbox(status, visible=True)