app.py

import gradio as gr
import pandas as pd
from datasets import load_dataset
from jiwer import wer, cer
import os
from datetime import datetime
import re


dataset = load_dataset("sudoping01/bambara-asr-benchmark", name="default")["train"]
references = {row["id"]: row["text"] for row in dataset}


leaderboard_file = "leaderboard.csv"
if not os.path.exists(leaderboard_file):
    pd.DataFrame(columns=["submitter", "WER", "CER", "timestamp"]).to_csv(leaderboard_file, index=False)


def normalize_text(text):
    """
    Normalize text for WER/CER calculation:
    - Convert to lowercase
    - Remove punctuation
    - Replace multiple spaces with single space
    - Strip leading/trailing spaces
    """
    if not isinstance(text, str):
        text = str(text)
    
    text = text.lower()
    text = re.sub(r'[^\w\s]', '', text)
    text = re.sub(r'\s+', ' ', text).strip()
    return text

def calculate_metrics(predictions_df):

    results = []

    for _, row in predictions_df.iterrows():
        id_val = row["id"]
        if id_val not in references:
            continue
            
        reference = normalize_text(references[id_val])
        hypothesis = normalize_text(row["text"])
        

        if not reference or not hypothesis:
            continue
            
        reference_words = reference.split()
        hypothesis_words = hypothesis.split()
        
        
        try:

            sample_wer = wer(reference, hypothesis)
            sample_cer = cer(reference, hypothesis)
                
            results.append({
                "id": id_val,
                "reference": reference,
                "hypothesis": hypothesis,
                "wer": sample_wer,
                "cer": sample_cer
            })
        except Exception as e:
            print(f"Error calculating metrics for ID {id_val}: {str(e)}")
    
    if not results:
        raise ValueError("No valid samples for WER/CER calculation")
        
    avg_wer = sum(item["wer"] for item in results) / len(results)
    avg_cer = sum(item["cer"] for item in results) / len(results)
    return avg_wer, avg_cer, results

def process_submission(submitter_name, csv_file):
    try:
      
        df = pd.read_csv(csv_file)
        
        if len(df) == 0:
            return "Error: Uploaded CSV is empty.", None
            
        if set(df.columns) != {"id", "text"}:
            return f"Error: CSV must contain exactly 'id' and 'text' columns. Found: {', '.join(df.columns)}", None
            
        if df["id"].duplicated().any():
            dup_ids = df[df["id"].duplicated()]["id"].unique()
            return f"Error: Duplicate IDs found: {', '.join(map(str, dup_ids[:5]))}", None
            
        missing_ids = set(references.keys()) - set(df["id"])
        extra_ids = set(df["id"]) - set(references.keys())
        
        if missing_ids:
            return f"Error: Missing {len(missing_ids)} IDs in submission. First few missing: {', '.join(map(str, list(missing_ids)[:5]))}", None
            
        if extra_ids:
            return f"Error: Found {len(extra_ids)} extra IDs not in reference dataset. First few extra: {', '.join(map(str, list(extra_ids)[:5]))}", None
        

        try:
            avg_wer, avg_cer, detailed_results = calculate_metrics(df)
            
            print(f"Calculated metrics - WER: {avg_wer:.4f}, CER: {avg_cer:.4f}")
            print(f"Processed {len(detailed_results)} valid samples")
            
      
            if avg_wer < 0.000001: # I will come back to this 
                return "Error: WER calculation yielded suspicious results (near-zero). Please check your submission CSV.", None
                
        except Exception as e:
            return f"Error calculating metrics: {str(e)}", None
        
        leaderboard = pd.read_csv(leaderboard_file)
        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        new_entry = pd.DataFrame(
            [[submitter_name, avg_wer, avg_cer, timestamp]],
            columns=["submitter", "WER", "CER", "timestamp"]
        )
        leaderboard = pd.concat([leaderboard, new_entry]).sort_values("WER")
        leaderboard.to_csv(leaderboard_file, index=False)
        
        return f"Submission processed successfully! WER: {avg_wer:.4f}, CER: {avg_cer:.4f}", leaderboard
        
    except Exception as e:
        return f"Error processing submission: {str(e)}", None


with gr.Blocks(title="Bambara ASR Leaderboard") as demo:
    gr.Markdown(
        """
        # Bambara ASR Leaderboard
        Upload a CSV file with 'id' and 'text' columns to evaluate your ASR predictions.
        The 'id's must match those in the dataset.
        [View the dataset here](https://huggingface.co/datasets/MALIBA-AI/bambara_general_leaderboard_dataset).
        - **WER**: Word Error Rate (lower is better).
        - **CER**: Character Error Rate (lower is better).
        """
    )
    
    with gr.Row():
        submitter = gr.Textbox(label="Submitter Name or Model Name", placeholder="e.g., MALIBA-AI/asr")
        csv_upload = gr.File(label="Upload CSV File", file_types=[".csv"])
        
    submit_btn = gr.Button("Submit")
    output_msg = gr.Textbox(label="Status", interactive=False)
    leaderboard_display = gr.DataFrame(
        label="Leaderboard",
        value=pd.read_csv(leaderboard_file),
        interactive=False
    )
    
    submit_btn.click(
        fn=process_submission,
        inputs=[submitter, csv_upload],
        outputs=[output_msg, leaderboard_display]
    )


print("Starting Bambara ASR Leaderboard app...")

if __name__ == "__main__":
    demo.launch(share=True)