import json
import os

import pandas as pd

from src.display.formatting import has_no_nan_values, make_clickable_model
from src.display.utils import AutoEvalColumn, EvalQueueColumn, ModelType, Precision, WeightType
from src.leaderboard.read_evals import get_raw_eval_results
from src.about import Tasks


def load_csv_results():
    """Load results from main-results.csv file"""
    csv_path = "main-results.csv"
    if not os.path.exists(csv_path):
        return []
    
    df = pd.read_csv(csv_path)
    results = []
    
    for _, row in df.iterrows():
        # Parse parameters - handle different formats
        param_str = str(row['Param'])
        if 'activated' in param_str:
            # Extract the activated parameter count (e.g., "2.8B activated (16B total)")
            param_value = float(param_str.split('B')[0])
        elif 'B' in param_str:
            # Simple format (e.g., "9B")
            param_value = float(param_str.replace('B', ''))
        else:
            param_value = 0
            
        # Convert CSV data to the format expected by the leaderboard
        data_dict = {
            AutoEvalColumn.model.name: make_clickable_model(row['Model']),
            AutoEvalColumn.average.name: row['ACC'],  # Using ACC as the average score
            AutoEvalColumn.params.name: param_value,
            AutoEvalColumn.license.name: "Open Source" if row['Open Source?'] == 'Yes' else "Proprietary",
            AutoEvalColumn.model_type.name: ModelType.FT.value.name,  # Default to fine-tuned
            AutoEvalColumn.precision.name: Precision.float16.value.name,  # Default precision
            AutoEvalColumn.weight_type.name: WeightType.Original.value.name,
            AutoEvalColumn.architecture.name: "Unknown",
            AutoEvalColumn.still_on_hub.name: True,
            AutoEvalColumn.revision.name: "",
            AutoEvalColumn.likes.name: 0,
            AutoEvalColumn.model_type_symbol.name: ModelType.FT.value.symbol,
        }
        
        # Add task-specific scores (required by the leaderboard)
        for task in Tasks:
            data_dict[task.name] = row['ACC']  # Use the same ACC score for all tasks
            
        results.append(data_dict)
    
    return results


def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
    """Creates a dataframe from all the individual experiment results"""
    raw_data = get_raw_eval_results(results_path, requests_path)
    all_data_json = [v.to_dict() for v in raw_data]
    
    # If no JSON data found, try loading from CSV
    if not all_data_json:
        all_data_json = load_csv_results()
    
    if not all_data_json:
        # Return empty dataframe if no data found
        return pd.DataFrame(columns=cols)

    df = pd.DataFrame.from_records(all_data_json)
    
    # Only include columns that exist in the dataframe
    existing_cols = [col for col in cols if col in df.columns]
    
    df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
    df = df[existing_cols].round(decimals=2)

    # filter out if any of the benchmarks have not been produced
    df = df[has_no_nan_values(df, benchmark_cols)]
    return df


def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
    """Creates the different dataframes for the evaluation queues requestes"""
    entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
    all_evals = []

    for entry in entries:
        if ".json" in entry:
            file_path = os.path.join(save_path, entry)
            with open(file_path) as fp:
                data = json.load(fp)

            data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
            data[EvalQueueColumn.revision.name] = data.get("revision", "main")

            all_evals.append(data)
        elif ".md" not in entry:
            # this is a folder
            sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if os.path.isfile(e) and not e.startswith(".")]
            for sub_entry in sub_entries:
                file_path = os.path.join(save_path, entry, sub_entry)
                with open(file_path) as fp:
                    data = json.load(fp)

                data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
                data[EvalQueueColumn.revision.name] = data.get("revision", "main")
                all_evals.append(data)

    pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
    running_list = [e for e in all_evals if e["status"] == "RUNNING"]
    finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"]
    df_pending = pd.DataFrame.from_records(pending_list, columns=cols)
    df_running = pd.DataFrame.from_records(running_list, columns=cols)
    df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
    return df_finished[cols], df_running[cols], df_pending[cols]