import json
import os
import pandas as pd
from datetime import datetime, timezone

from src.about import Tasks, SpeechTasks
from src.display.formatting import styled_error, styled_message, styled_warning
from src.display.utils import REGION_MAP
from src.envs import API, EVAL_REQUESTS_PATH, TOKEN, QUEUE_REPO, RESULTS_REPO, EVAL_RESULTS_PATH

REQUESTED_MODELS = None
USERS_TO_SUBMISSION_DATES = None


def handle_csv_submission(
    model_name: str,
    csv_file,  # uploaded file path
    result_type: str,
):
    if model_name == "" or model_name is None:
        return styled_error("Please provide a model name.")
    if csv_file is None:
        return styled_error("Please provide a CSV file with results.")

    df = pd.read_csv(csv_file)

    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")

    # Save uploaded CSV
    subdir = os.path.join(EVAL_REQUESTS_PATH, result_type)
    os.makedirs(subdir, exist_ok=True)

    filename = f"{current_time}_{model_name}_{result_type}_results.csv"
    remote_path = f"msteb_{result_type}_requests/{filename}"

    csv_save_path = os.path.join(subdir,filename)
    df.to_csv(csv_save_path, index=False)

    print(f"Uploading to {QUEUE_REPO}/{remote_path}")
    API.upload_file(
        path_or_fileobj=csv_save_path,
        path_in_repo=remote_path,
        repo_id=QUEUE_REPO,
        repo_type="dataset",  # or "model" if you made the repo that way
        commit_message=f"Add {result_type} request for {model_name} at {current_time}",
    )

    # Remove the local file
    os.remove(csv_save_path)
    # this converts dataframe to json and uploads it to results


    try:
        convert_csv_to_json_and_upload(df, model_name, result_type)
    except ValueError as e:
        return styled_error(f"{str(e)}")
    return styled_message(f"Results CSV successfully submitted for `{model_name}`!")

def find_task_by_col_name(col_name, enum_cls):
    for task in enum_cls:
        if task.value.col_name == col_name:
            return task
    return None
def convert_csv_to_json_and_upload(df: pd.DataFrame, model_name: str, result_type: str):
    task_enum = Tasks if result_type == "text" else SpeechTasks

    task_display_names = {t.value.col_name for t in task_enum}
    region_names = df["Region"].tolist()
    average_row = "Average (Micro)"

    # --- Validation ---
    df_columns = set(df.columns[1:])  # exclude Region column
    if not df_columns.issubset(task_display_names):
        extra = df_columns - task_display_names
        raise ValueError(f"Extra columns in CSV: {extra}")
    if average_row not in df["Region"].values:
        raise ValueError("Missing row for 'Average (Micro)'")

    data_region_names = [r for r in region_names if r != average_row]

    for region in data_region_names:
        if region not in REGION_MAP:
            raise ValueError(f"Region '{region}' not found in REGION_MAP keys.")

    # --- Build JSON ---
    # I go over the regions in the CSV and create a JSON object.
    model_json = {
        "config": {"model_name": model_name},
        "results": {},
        "regions": {},
    }
    at_least_one_number = False

    for _, row in df.iterrows():
        region_display = row["Region"]

        if region_display == average_row:
            for col, val in row.items():
                if col == "Region":
                    continue
                task = find_task_by_col_name(col, task_enum)
                if val is not None and not pd.isna(val) and isinstance(val, (int, float)):
                    print(f" value {val}")
                    at_least_one_number = True
                    model_json["results"][task.value.benchmark] = {task.value.metric: val/100}
        else:
            model_json["regions"][REGION_MAP[region_display]] = {}
            for col, val in row.items():
                if col == "Region":
                    continue
                task = find_task_by_col_name(col, task_enum)
                if val is not None and not pd.isna(val) and isinstance(val, (int, float)):
                    model_json["regions"][REGION_MAP[region_display]][task.value.benchmark] = {task.value.metric: val/100}

    # Check if at least one number is present in the results
    print(at_least_one_number)
    if at_least_one_number is False:
        raise ValueError("No valid numeric results found in the CSV. Please check your input.")

    # --- Save locally ---
    subdir = os.path.join(EVAL_RESULTS_PATH, result_type)
    os.makedirs(subdir, exist_ok=True)
    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
    filename = f"{current_time}_{model_name}_{result_type}.json"
    json_save_path = os.path.join(subdir,filename)

    with open(json_save_path, "w") as f:
        json.dump(model_json, f, indent=2)

    # --- Upload to HF Hub ---
    remote_path = f"msteb_leaderboard/msteb_{result_type}_results/{filename}"
    API.upload_file(
        path_or_fileobj=json_save_path,
        path_in_repo=remote_path,
        repo_id=RESULTS_REPO,
        repo_type="dataset",
        commit_message=f"Upload results for {model_name} ({result_type}) at {current_time}",
    )
    os.remove(json_save_path)

    print(f"Uploaded to {RESULTS_REPO}/{current_time}")

    return f"Uploaded to {RESULTS_REPO}/{current_time}"