## REQUIRED OUTPUT ###
# A list of version names -> v1.6, v.6_multimodal, v1.6_quantized, v1.5, v0.9, etc......
# A corresponding DataFrame?

import requests
from datetime import datetime
import pandas as pd
import json
from io import StringIO

from src.leaderboard_utils import process_df
from src.assets.text_content import REPO

def get_versions_data():
    """
    Read and process data from CSV files of all available versions hosted on GitHub. - https://github.com/clembench/clembench-runs

    Returns:
        versions_data:
            -
    """
    base_repo = REPO
    json_url = base_repo + "benchmark_runs.json"
    response = requests.get(json_url)

    # Check if the JSON file request was successful
    if response.status_code != 200:
        print(f"Failed to read JSON file: Status Code: {response.status_code}")
        return None, None, None, None

    json_data = response.json()
    versions = json_data['versions']

    # Sort version names - latest first
    version_names = sorted(
        [ver['version'] for ver in versions],
        key=lambda v: float(v[1:]),
        reverse=True
    )
    print(f"Found {len(version_names)} versions from get_versions_data(): {version_names}.")

    # Get Last updated date of the latest version
    latest_version = version_names[0]
    latest_date = next(
        ver['date'] for ver in versions if ver['version'] == latest_version
    )
    formatted_date = datetime.strptime(latest_date, "%Y/%m/%d").strftime("%d %b %Y")

    # Get Versions data
    versions_data = {"latest": latest_version, "date": formatted_date}

    # Collect Dataframes
    dfs = []

    for version in version_names:
        text_url = f"{base_repo}{version}/results.csv"
        mm_url = f"{base_repo}{version}_multimodal/results.csv"
        quant_url = f"{base_repo}{version}_quantized/results.csv"

        # Text Data
        response = requests.get(text_url)
        if response.status_code == 200:
            df = pd.read_csv(StringIO(response.text))
            df = process_df(df)
            df = df.sort_values(by=df.columns[1], ascending=False)  # Sort by clemscore column
            versions_data[version] = df
        else:
            print(f"Failed to read Text-only leaderboard CSV file for version: {version}. Status Code: {response.status_code}")

        # Multimodal Data
        mm_response = requests.get(mm_url)
        if mm_response.status_code == 200:
            mm_df = pd.read_csv(StringIO(mm_response.text))
            mm_df = process_df(mm_df)
            mm_df = mm_df.sort_values(by=mm_df.columns[1], ascending=False)  # Sort by clemscore column
            versions_data[version+"_multimodal"] = mm_df
        else:
            print(f"Failed to read multimodal leaderboard CSV file for version: {version}: Status Code: {mm_response.status_code}. Please ignore this message if multimodal results are not available for this version")

        # Multimodal Data
        q_response = requests.get(quant_url)
        if q_response.status_code == 200:
            q_df = pd.read_csv(StringIO(q_response.text))
            q_df = process_df(q_df)
            q_df = q_df.sort_values(by=q_df.columns[1], ascending=False)  # Sort by clemscore column
            versions_data[version + "_quantized"] = q_df
        else:
            print(f"Failed to read quantized leaderboard CSV file for version: {version}: Status Code: {mm_response.status_code}. Please ignore this message if quantized results are not available for this version")

    return versions_data


if __name__ == "__main__":
    versions_data = get_versions_data()
    print(versions_data.keys())