utils.py

import base64
from huggingface_hub import hf_hub_download
import fasttext
import os
import json
import pandas as pd
from sklearn.metrics import (
    precision_score, 
    recall_score, 
    f1_score, 
    confusion_matrix, 
    balanced_accuracy_score, 
    matthews_corrcoef
)
import numpy as np
from datasets import load_dataset

# Constants
MODEL_REPO = "atlasia/Sfaya-Moroccan-Darija-vs-All"
BIN_FILENAME = "model_multi_v3_2fpr.bin"
BINARY_LEADERBOARD_FILE = "darija_leaderboard_binary.json"
MULTILINGUAL_LEADERBOARD_FILE = "darija_leaderboard_multilingual.json"
DATA_PATH = "atlasia/Arabic-LID-Leaderboard"

target_label = "Morocco"
is_binary = False

# Load test dataset
test_dataset = load_dataset(DATA_PATH, split='test')  
 
# Supported dialects
all_target_languages = list(test_dataset.unique("dialect"))
supported_dialects = all_target_languages + ['All']
languages_to_display_one_vs_all = all_target_languages # everything except All

print(f'all_target_languages: {all_target_languages}')

metrics = [
    'f1_score',
    'precision',
    'recall',
    'specificity',
    'false_positive_rate',
    'false_negative_rate',
    'negative_predictive_value',
    'n_test_samples',
]

default_metrics = [
    'f1_score', 
    'precision', 
    'recall', 
    'false_positive_rate', 
    'false_negative_rate'
]

# default language to display in one-vs-all leaderboard
default_languages = [
    #'Morocco',
    'MSA',
    #'Egypt',
    #'Algeria',
    #'Tunisia',
    #'Levantine',
]

language_mapping_dict = {   
    'ace_Arab': 'Acehnese',
    'acm_Arab': 'Mesopotamia',  # 'Gilit Mesopotamian'
    'aeb_Arab': 'Tunisia',
    'ajp_Arab': 'Levantine',  # 'South Levantine'
    'apc_Arab': 'Levantine',
    'arb_Arab': 'MSA',
    'arq_Arab': 'Algeria',
    'ars_Arab': 'Saudi',  # Najdi is primarily Saudi Arabian
    'ary_Arab': 'Morocco',
    'arz_Arab': 'Egypt',
    'ayp_Arab': 'Mesopotamia',  # 'North Mesopotamian'
    'azb_Arab': 'Azerbaijan',  # South Azerbaijani pertains to this region
    'bcc_Arab': 'Balochistan',  # Southern Balochi is from Balochistan
    'bjn_Arab': 'Indonesia',  # Banjar is spoken in Indonesia
    'brh_Arab': 'Pakistan',  # Brahui is spoken in Pakistan
    'ckb_Arab': 'Kurdistan',  # Central Kurdish is mainly in Iraq
    'fuv_Arab': 'Nigeria', # Hausa States Fulfulde
    'glk_Arab': 'Iran',  # Gilaki is spoken in Iran
    'hac_Arab': 'Iran',  # Gurani is also primarily spoken in Iran
    'kas_Arab': 'Kashmir',
    'knc_Arab': 'Nigeria',  # Central Kanuri is in Nigeria
    'lki_Arab': 'Iran',  # Laki is from Iran
    'lrc_Arab': 'Iran',  # Northern Luri is from Iran
    'min_Arab': 'Indonesia',  # Minangkabau is spoken in Indonesia
    'mzn_Arab': 'Iran',  # Mazanderani is spoken in Iran
    'ota_Arab': 'Turkey',  # Ottoman Turkish
    'pbt_Arab': 'Afghanistan',  # Southern Pashto
    'pnb_Arab': 'Pakistan',  # Western Panjabi
    'sdh_Arab': 'Iraq',  # Southern Kurdish
    'shu_Arab': 'Chad',  # Chadian Arabic
    'skr_Arab': 'Pakistan',  # Saraiki
    'snd_Arab': 'Pakistan',  # Sindhi
    'sus_Arab': 'Guinea',  # Susu
    'tuk_Arab': 'Turkmenistan',  # Turkmen
    'uig_Arab': 'Uighur (China)',  # Uighur
    'urd_Arab': 'Pakistan',  # Urdu
    'uzs_Arab': 'Uzbekistan',  # Southern Uzbek
    'zsm_Arab': 'Malaysia'  # Standard Malay
}

def predict_label(text, model, language_mapping_dict, use_mapping=False):
    # Remove any newline characters and strip whitespace
    text = str(text).strip().replace('\n', ' ')
    
    if text == '':
        return 'Other'
    
    try:
        # Get top prediction
        prediction = model.predict(text, 1)
        
        # Extract label and remove __label__ prefix
        label = prediction[0][0].replace('__label__', '')
        
        # Extract confidence score
        confidence = prediction[1][0]
        
        # map label to language using language_mapping_dict
        if use_mapping:
            label = language_mapping_dict.get(label, 'Other')
        return label
    
    except Exception as e:
        print(f"Error processing text: {text}")
        print(f"Exception: {e}")
        return {'prediction_label': 'Error', 'prediction_confidence': 0.0}

def compute_classification_metrics(test_dataset):
    """
    Compute comprehensive classification metrics for each class.
    
    Args:
        data (pd.DataFrame): DataFrame containing 'dialect' as true labels and 'preds' as predicted labels.
        
    Returns:
        pd.DataFrame: DataFrame with detailed metrics for each class.
    """
    # transform the dataset into a DataFrame
    data = pd.DataFrame(test_dataset)
    # Extract true labels and predictions
    true_labels = list(data['dialect'])
    predicted_labels = list(data['preds'])

    # Handle all unique labels
    labels = sorted(list(set(true_labels + predicted_labels)))
    label_to_index = {label: index for index, label in enumerate(labels)}
    
    # Convert labels to indices
    true_indices = [label_to_index[label] for label in true_labels]
    pred_indices = [label_to_index[label] for label in predicted_labels]

    # Compute basic metrics
    f1_scores = f1_score(true_indices, pred_indices, average=None, labels=range(len(labels)))
    precision_scores = precision_score(true_indices, pred_indices, average=None, labels=range(len(labels)))
    recall_scores = recall_score(true_indices, pred_indices, average=None, labels=range(len(labels)))

    # Compute confusion matrix
    conf_mat = confusion_matrix(true_indices, pred_indices, labels=range(len(labels)))

    # Calculate various metrics per class
    FP = conf_mat.sum(axis=0) - np.diag(conf_mat)  # False Positives
    FN = conf_mat.sum(axis=1) - np.diag(conf_mat)  # False Negatives
    TP = np.diag(conf_mat)                         # True Positives
    TN = conf_mat.sum() - (FP + FN + TP)          # True Negatives

    # Calculate sample counts per class
    samples_per_class = np.bincount(true_indices, minlength=len(labels))

    # Calculate additional metrics
    with np.errstate(divide='ignore', invalid='ignore'):
        fp_rate = FP / (FP + TN)  # False Positive Rate
        fn_rate = FN / (FN + TP)  # False Negative Rate
        specificity = TN / (TN + FP)  # True Negative Rate
        npv = TN / (TN + FN)  # Negative Predictive Value
        
        # Replace NaN/inf with 0
        metrics = [fp_rate, fn_rate, specificity, npv]
        metrics = [np.nan_to_num(m, nan=0.0, posinf=0.0, neginf=0.0) for m in metrics]
        fp_rate, fn_rate, specificity, npv = metrics

    # Calculate overall metrics
    balanced_acc = balanced_accuracy_score(true_indices, pred_indices)
    mcc = matthews_corrcoef(true_indices, pred_indices)

    # Compile results into a DataFrame
    result_df = pd.DataFrame({
        'country': labels,
        'samples': samples_per_class,
        'f1_score': f1_scores,
        'precision': precision_scores,
        'recall': recall_scores,
        'specificity': specificity,
        'false_positive_rate': fp_rate,
        'false_negative_rate': fn_rate,
        'true_positives': TP,
        'false_positives': FP,
        'true_negatives': TN,
        'false_negatives': FN,
        'negative_predictive_value': npv
    })

    # Sort by number of samples (descending)
    result_df = result_df.sort_values('samples', ascending=False)

    # Calculate and add summary metrics
    summary_metrics = {
        'macro_f1': f1_score(true_indices, pred_indices, average='macro'),
        'weighted_f1': f1_score(true_indices, pred_indices, average='weighted'),
        'micro_f1': f1_score(true_indices, pred_indices, average='micro'),
        'balanced_accuracy': balanced_acc,
        'matthews_correlation': mcc
    }

    # Format all numeric columns to 4 decimal places
    numeric_cols = result_df.select_dtypes(include=[np.number]).columns
    result_df[numeric_cols] = result_df[numeric_cols].round(4)
    
    print(f'result_df: {result_df}')

    return result_df, summary_metrics

def make_binary(dialect, target):
    if dialect != target:
        return 'Other'
    return target

def run_eval_one_vs_all(data_test, TARGET_LANG='Morocco'):
    
    # map to binary
    df_test_preds = data_test.copy()
    df_test_preds.loc[df_test_preds['dialect'] == TARGET_LANG, 'dialect'] = TARGET_LANG
    df_test_preds.loc[df_test_preds['dialect'] != TARGET_LANG, 'dialect'] = 'Other'
    
    # compute the fpr per dialect
    dialect_counts = data_test.groupby('dialect')['dialect'].count().reset_index(name='size')
    result_df = pd.merge(dialect_counts, data_test, on='dialect')
    result_df = result_df.groupby(['dialect', 'size', 'preds'])['preds'].count()/result_df.groupby(['dialect', 'size'])['preds'].count()
    result_df.sort_index(ascending=False, level='size', inplace=True)
    
    # group by dialect and get the false positive rate
    out = result_df.copy()
    out.name = 'false_positive_rate'
    out = out.reset_index()
    out = out[out['preds']==TARGET_LANG].drop(columns=['preds', 'size'])
    
    print(f'out for TARGET_LANG={TARGET_LANG} \n: {out}')
    
    return out

def update_darija_one_vs_all_leaderboard(result_df, model_name, target_lang, BINARY_LEADERBOARD_FILE="darija_leaderboard_binary.json"):
    try:
        with open(BINARY_LEADERBOARD_FILE, "r") as f:
            data = json.load(f)
    except FileNotFoundError:
        data = []

    # Process the results for each dialect/country
    for _, row in result_df.iterrows():
        dialect = row['dialect']
        # Skip 'Other' class, it is considered as the null space
        if dialect == 'Other':
            continue
        
        # Find existing target_lang entry or create a new one
        target_entry = next((item for item in data if target_lang in item), None)
        if target_entry is None:
            target_entry = {target_lang: {}}
            data.append(target_entry)

        # Get the country-specific data for this target language
        country_data = target_entry[target_lang]
        
        # Initialize the dialect/country entry if it doesn't exist
        if dialect not in country_data:
            country_data[dialect] = {}

        # Update the model metrics under the model name for the given dialect
        country_data[dialect][model_name] = float(row['false_positive_rate'])
        
        # # Add the number of test samples, if not already present
        # if "n_test_samples" not in country_data[dialect]:
        #     country_data[dialect]["n_test_samples"] = int(row['size'])

    # Save updated leaderboard data
    with open(BINARY_LEADERBOARD_FILE, "w") as f:
        json.dump(data, f, indent=4)

def handle_evaluation(model_path, model_path_bin, use_mapping=False):
    
    # download model and get the model path
    model_path_hub = hf_hub_download(repo_id=model_path, filename=model_path_bin, cache_dir=None)
   
    # Load the trained model
    print(f"[INFO] Loading model from Path: {model_path_hub}, using version {model_path_bin}...")
    model = fasttext.load_model(model_path_hub)
    
    # Load the evaluation dataset
    print(f"[INFO] Loading evaluation dataset from Path: {DATA_PATH}...")
    eval_dataset = load_dataset(DATA_PATH, split='test')
    
    # Transform to pandas DataFrame
    print(f"[INFO] Converting evaluation dataset to Pandas DataFrame...")
    df_eval = pd.DataFrame(eval_dataset)
    
    # Predict labels using the model
    print(f"[INFO] Running predictions...")
    df_eval['preds'] = df_eval['text'].apply(lambda text: predict_label(text, model, language_mapping_dict, use_mapping=use_mapping))
    
    # run the evaluation
    result_df, _ = run_eval(df_eval)
    # set the model name
    model_name = model_path + '/' + model_path_bin
    
    # update the multilingual leaderboard
    update_darija_multilingual_leaderboard(result_df, model_name, MULTILINGUAL_LEADERBOARD_FILE)
    
    for target_lang in all_target_languages:
        result_df_one_vs_all =run_eval_one_vs_all(df_eval, TARGET_LANG=target_lang)
        update_darija_one_vs_all_leaderboard(result_df_one_vs_all, model_name, target_lang, BINARY_LEADERBOARD_FILE)
    
    # load the updated leaderboard tables
    df_multilingual = load_leaderboard_multilingual()
    df_one_vs_all = load_leaderboard_one_vs_all()
    
    status_message = "**Evaluation now ended! 🤗**"
    
    return create_leaderboard_display_multilingual(df_multilingual, target_label, default_metrics), status_message

def run_eval(df_eval):
    """Run evaluation on a dataset and compute metrics.

    Args:
        model: The model to evaluate.
        DATA_PATH (str): Path to the dataset.
        is_binary (bool): If True, evaluate as binary classification.
                          If False, evaluate as multi-class classification.
        target_label (str): The target class label in binary mode.

    Returns:
        pd.DataFrame: A DataFrame containing evaluation metrics.
    """

    # map to binary
    df_eval_multilingual = df_eval.copy()
    
    # now drop the columns that are not needed, i.e. 'text'
    df_eval_multilingual = df_eval_multilingual.drop(columns=['text', 'metadata', 'dataset_source'])

    # Compute evaluation metrics
    print(f"[INFO] Computing metrics...")
    result_df, _ = compute_classification_metrics(df_eval_multilingual)
    
    # update_darija_multilingual_leaderboard(result_df, model_path, MULTILINGUAL_LEADERBOARD_FILE)
    
    return result_df, df_eval_multilingual  

def process_results_file(file, uploaded_model_name, base_path_save="./atlasia/submissions/", default_language='Morocco'):
    try:
        if file is None:
            return "Please upload a file."
            
        # Clean the model name to be safe for file paths
        uploaded_model_name = uploaded_model_name.strip().replace(" ", "_")
        print(f"[INFO] uploaded_model_name: {uploaded_model_name}")
        
        # Create the directory for saving submissions
        path_saving = os.path.join(base_path_save, uploaded_model_name)
        os.makedirs(path_saving, exist_ok=True)
        
        # Define the full path to save the file
        saved_file_path = os.path.join(path_saving, 'submission.csv')
        
        # Read the uploaded file as DataFrame
        print(f"[INFO] Loading results...")
        df_eval = pd.read_csv(file.name)
        
        # Save the DataFrame
        print(f"[INFO] Saving the file locally in: {saved_file_path}")
        df_eval.to_csv(saved_file_path, index=False)
        
    except Exception as e:
        return f"Error processing file: {str(e)}"
    
    # Compute evaluation metrics
    print(f"[INFO] Computing metrics...")
    result_df, _ = compute_classification_metrics(df_eval)
    
    # Update the leaderboards
    update_darija_multilingual_leaderboard(result_df, uploaded_model_name, MULTILINGUAL_LEADERBOARD_FILE)
    
    # TODO: implement this ove_vs_all differently for people only submitting csv file. They need to submit two files, one for multi-lang and the other for one-vs-all
    # result_df_one_vs_all = run_eval_one_vs_all(...)
    # update_darija_one_vs_all_leaderboard(...)
    
    # update the leaderboard table
    df = load_leaderboard_multilingual()
    
    return create_leaderboard_display_multilingual(df, default_language, default_metrics)
    
def update_darija_multilingual_leaderboard(result_df, model_name, MULTILINGUAL_LEADERBOARD_FILE="darija_leaderboard_multilingual.json"):
    
    # Load leaderboard data
    current_dir = os.path.dirname(os.path.abspath(__file__))
    MULTILINGUAL_LEADERBOARD_FILE = os.path.join(current_dir, MULTILINGUAL_LEADERBOARD_FILE)
    
    try:
        with open(MULTILINGUAL_LEADERBOARD_FILE, "r") as f:
            data = json.load(f)
    except FileNotFoundError:
        data = []
        
    # Process the results for each dialect/country
    for _, row in result_df.iterrows():
        country = row['country']
        # skip 'Other' class, it is considered as the null space
        if country == 'Other':
            continue
            
        # Create metrics dictionary directly
        metrics = {
            'f1_score': float(row['f1_score']),
            'precision': float(row['precision']),
            'recall': float(row['recall']),
            'specificity': float(row['specificity']),
            'false_positive_rate': float(row['false_positive_rate']),
            'false_negative_rate': float(row['false_negative_rate']),
            'negative_predictive_value': float(row['negative_predictive_value']),
            'n_test_samples': int(row['samples'])
        }
        
        # Find existing country entry or create new one
        country_entry = next((item for item in data if country in item), None)
        if country_entry is None:
            country_entry = {country: {}}
            data.append(country_entry)
        
        # Update the model metrics directly under the model name
        if country not in country_entry:
            country_entry[country] = {}
        country_entry[country][model_name] = metrics
    
    # Save updated leaderboard data
    with open(MULTILINGUAL_LEADERBOARD_FILE, "w") as f:
        json.dump(data, f, indent=4)
        

def load_leaderboard_one_vs_all(BINARY_LEADERBOARD_FILE="darija_leaderboard_binary.json"):
    current_dir = os.path.dirname(os.path.abspath(__file__))
    BINARY_LEADERBOARD_FILE = os.path.join(current_dir, BINARY_LEADERBOARD_FILE)
    
    with open(BINARY_LEADERBOARD_FILE, "r") as f:
        data = json.load(f)
    
    # Initialize lists to store the flattened data
    rows = []
    
    # Process each target language's data
    for leaderboard_data in data:
        for target_language, results in leaderboard_data.items():
            for language, models in results.items():
                
                for model_name, false_positive_rate in models.items():
                    
                    row = {
                        'target_language': target_language,
                        'language': language,
                        'model': model_name,
                        'false_positive_rate': false_positive_rate,
                    }
                    # Add all metrics to the row
                    rows.append(row)
    
    # Convert to DataFrame
    df = pd.DataFrame(rows)
    
    # Pivot the DataFrame to create the desired structure: all languages in columns and models in rows, and each (model, target_language, language) = false_positive_rate
    df_pivot = df.pivot(index=['model', 'target_language'], columns='language', values='false_positive_rate').reset_index()
    
    # print(f'df_pivot \n: {df_pivot}')
    
    return df_pivot

def load_leaderboard_multilingual(MULTILINGUAL_LEADERBOARD_FILE="darija_leaderboard_multilingual.json"):
    current_dir = os.path.dirname(os.path.abspath(__file__))
    MULTILINGUAL_LEADERBOARD_FILE = os.path.join(current_dir, MULTILINGUAL_LEADERBOARD_FILE)
    
    with open(MULTILINGUAL_LEADERBOARD_FILE, "r") as f:
        data = json.load(f)
    
    # Initialize lists to store the flattened data
    rows = []
    
    # Process each country's data
    for country_data in data:
        for country, models in country_data.items():
            for model_name, metrics in models.items():
                row = {
                    'country': country,
                    'model': model_name,
                }
                # Add all metrics to the row
                row.update(metrics)
                rows.append(row)
    
    # Convert to DataFrame
    df = pd.DataFrame(rows)
    return df

def create_leaderboard_display_one_vs_all(df, target_language, selected_languages):
    
    # Filter by target_language if specified
    if target_language:
        df = df[df['target_language'] == target_language]
        
        # Remove the target_language from selected_languages
        if target_language in selected_languages:
            selected_languages = [lang for lang in selected_languages if lang != target_language]
    
        # Select only the chosen languages (plus 'model' column)
        columns_to_show = ['model'] + [language for language in selected_languages if language in df.columns]
        
    # Sort by first selected metric by default
    if selected_languages:
        df = df.sort_values(by=selected_languages[0], ascending=False)
    
    df = df[columns_to_show]
    
    # Format numeric columns to 4 decimal places
    numeric_cols = df.select_dtypes(include=['float64']).columns
    df[numeric_cols] = df[numeric_cols].round(4)
    
    return df, selected_languages


def create_leaderboard_display_multilingual(df, selected_country, selected_metrics):
    # Filter by country if specified
    if selected_country and selected_country.upper() != 'ALL':
        # print(f"Filtering leaderboard by country: {selected_country}")
        df = df[df['country'] == selected_country]
        df = df.drop(columns=['country'])
    
        # Select only the chosen metrics (plus 'model' column)
        columns_to_show = ['model'] + [metric for metric in selected_metrics if metric in df.columns]
    
    else:
        # Select all metrics (plus 'country' and 'model' columns), if no country is selected or 'All' is selected for ease of comparison
        columns_to_show = ['model', 'country'] + selected_metrics
        
    # Sort by first selected metric by default
    if selected_metrics:
        df = df.sort_values(by=selected_metrics[0], ascending=False)
    
    df = df[columns_to_show]
    
    # Format numeric columns to 4 decimal places
    numeric_cols = df.select_dtypes(include=['float64']).columns
    df[numeric_cols] = df[numeric_cols].round(4)
    
    return df

def update_leaderboard_multilingual(country, selected_metrics):
    if not selected_metrics:  # If no metrics selected, show all
        selected_metrics = metrics
    df = load_leaderboard_multilingual()
    display_df = create_leaderboard_display_multilingual(df, country, selected_metrics)
    return display_df

def update_leaderboard_one_vs_all(target_language, selected_languages):
    if not selected_languages:  # If no language selected, show all defaults
        selected_languages = default_languages
    df = load_leaderboard_one_vs_all()
    display_df, selected_languages = create_leaderboard_display_one_vs_all(df, target_language, selected_languages)
    # to improve visibility in case the user chooses multiple language leading to many columns, the `model` column must remain fixed 
    # display_df = render_fixed_columns(display_df)
    return display_df, selected_languages

def encode_image_to_base64(image_path):
    with open(image_path, "rb") as image_file:
        encoded_string = base64.b64encode(image_file.read()).decode()
    return encoded_string

def create_html_image(image_path):
    # Get base64 string of image
    img_base64 = encode_image_to_base64(image_path)
    
    # Create HTML string with embedded image and centering styles
    html_string = f"""
    <div style="display: flex; justify-content: center; align-items: center; width: 100%; text-align: center;">
        <div style="max-width: 800px; margin: auto;">
            <img src="data:image/jpeg;base64,{img_base64}"
                 style="max-width: 75%; height: auto; display: block; margin: 0 auto; margin-top: 50px;"
                 alt="Displayed Image">
        </div>
    </div>
    """
    return html_string

# Function to render HTML table with fixed 'model' column
def render_fixed_columns(df):
    style = """
    <style>
    .table-container {
        overflow-x: auto;
        position: relative;
        white-space: nowrap;
    }
    table {
        border-collapse: collapse;
        width: 100%;
    }
    th, td {
        border: 1px solid black;
        padding: 8px;
        text-align: left;
    }
    th.fixed, td.fixed {
        position: sticky;
        left: 0;
        background-color: white;
        z-index: 2;
    }
    </style>
    """
    table_html = df.to_html(index=False).replace(
        "<th>model</th>", '<th class="fixed">model</th>'
    ).replace(
        '<td>', '<td class="fixed">', 1
    )
    return f"{style}<div class='table-container'>{table_html}</div>"