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

from huggingface_hub import login

token = os.environ.get("HG_TOKEN")
login(token)

try:
    dataset = load_dataset("sudoping01/bambara-speech-recognition-benchmark", name="default")["eval"]
    references = {row["id"]: row["text"] for row in dataset}
except Exception as e:
    references = {}

leaderboard_file = "leaderboard.csv"
if not os.path.exists(leaderboard_file):
    pd.DataFrame(columns=["Model_Name", "WER", "CER", "Combined_Score", "timestamp"]).to_csv(leaderboard_file, index=False)
else:
    leaderboard_df = pd.read_csv(leaderboard_file)
    
    if "Combined_Score" not in leaderboard_df.columns:
        leaderboard_df["Combined_Score"] = leaderboard_df["WER"] * 0.7 + leaderboard_df["CER"] * 0.3
        leaderboard_df.to_csv(leaderboard_file, index=False)

def normalize_text(text):
    """Normalize text for WER/CER calculation"""
    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):
    """Calculate WER and CER for predictions."""
    results = []
    total_ref_words = 0
    total_ref_chars = 0

    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()
        reference_chars = list(reference)
        
        try:
            sample_wer = wer(reference, hypothesis)
            sample_cer = cer(reference, hypothesis)
            
            sample_wer = min(sample_wer, 2.0)  
            sample_cer = min(sample_cer, 2.0)  
            
            total_ref_words += len(reference_words)
            total_ref_chars += len(reference_chars)
            
            results.append({
                "id": id_val,
                "reference": reference,
                "hypothesis": hypothesis,
                "ref_word_count": len(reference_words),
                "ref_char_count": len(reference_chars),
                "wer": sample_wer,
                "cer": sample_cer
            })
        except Exception:
            pass
    
    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)
    
    # Calculate weighted average metrics based on reference length
    weighted_wer = sum(item["wer"] * item["ref_word_count"] for item in results) / total_ref_words
    weighted_cer = sum(item["cer"] * item["ref_char_count"] for item in results) / total_ref_chars
    
    return avg_wer, avg_cer, weighted_wer, weighted_cer, results

def format_as_percentage(value):
    """Convert decimal to percentage with 2 decimal places"""
    return f"{value * 100:.2f}%"

def prepare_leaderboard_for_display(df, sort_by="Combined_Score"):
    """Format leaderboard for display with ranking and percentages"""
    if len(df) == 0:
        return pd.DataFrame(columns=["Rank", "Model_Name", "WER (%)", "CER (%)", "Combined_Score (%)", "timestamp"])
    

    display_df = df.copy()
    
    display_df = display_df.sort_values(sort_by)
    
    display_df.insert(0, "Rank", range(1, len(display_df) + 1))
    
    for col in ["WER", "CER", "Combined_Score"]:
        if col in display_df.columns:
            display_df[f"{col} (%)"] = display_df[col].apply(lambda x: f"{x * 100:.2f}")
            display_df = display_df.drop(col, axis=1)
    
    # Removed the clickable model name transformation
    
    return display_df

def update_ranking(method):
    """Update leaderboard ranking based on selected method"""
    try:
        current_lb = pd.read_csv(leaderboard_file)
        
        if "Combined_Score" not in current_lb.columns:
            current_lb["Combined_Score"] = current_lb["WER"] * 0.7 + current_lb["CER"] * 0.3
        
        sort_column = "Combined_Score"
        if method == "WER Only":
            sort_column = "WER"
        elif method == "CER Only":
            sort_column = "CER"
        
        return prepare_leaderboard_for_display(current_lb, sort_column)
        
    except Exception:
        return pd.DataFrame(columns=["Rank", "Model_Name", "WER (%)", "CER (%)", "Combined_Score (%)", "timestamp"])

def process_submission(model_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, weighted_wer, weighted_cer, detailed_results = calculate_metrics(df)
            
            # suspiciously low values
            if avg_wer < 0.001:
                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")
        
        # Calculate combined score (70% WER, 30% CER)
        combined_score = avg_wer * 0.7 + avg_cer * 0.3
        
        new_entry = pd.DataFrame(
            [[model_name, avg_wer, avg_cer, combined_score, timestamp]],
            columns=["Model_Name", "WER", "CER", "Combined_Score", "timestamp"]
        )
        

        updated_leaderboard = pd.concat([leaderboard, new_entry]).sort_values("Combined_Score")
        updated_leaderboard.to_csv(leaderboard_file, index=False)
        
        display_leaderboard = prepare_leaderboard_for_display(updated_leaderboard)
        
        return f"Submission processed successfully! WER: {format_as_percentage(avg_wer)}, CER: {format_as_percentage(avg_cer)}, Combined Score: {format_as_percentage(combined_score)}", display_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
        
        This leaderboard ranks and evaluates speech recognition models for the Bambara language.
        Models are ranked based on a combined score of WER and CER metrics.
        """
    )
    
    with gr.Tabs() as tabs:
        with gr.TabItem("🏅 Current Rankings"):
            try:
                current_leaderboard = pd.read_csv(leaderboard_file)
                
                if "Combined_Score" not in current_leaderboard.columns:
                    current_leaderboard["Combined_Score"] = current_leaderboard["WER"] * 0.7 + current_leaderboard["CER"] * 0.3
                
                display_leaderboard = prepare_leaderboard_for_display(current_leaderboard)
            except Exception:
                display_leaderboard = pd.DataFrame(columns=["Rank", "Model_Name", "WER (%)", "CER (%)", "Combined_Score (%)", "timestamp"])
            
            gr.Markdown("### Current ASR Model Rankings")
            
            ranking_method = gr.Radio(
                ["Combined Score (WER 70%, CER 30%)", "WER Only", "CER Only"], 
                label="Ranking Method",
                value="Combined Score (WER 70%, CER 30%)"
            )
            
            leaderboard_view = gr.DataFrame(
                value=display_leaderboard,
                interactive=False,
                label="Models are ranked by selected metric - lower is better"
            )
            
            ranking_method.change(
                fn=update_ranking,
                inputs=[ranking_method],
                outputs=[leaderboard_view]
            )
            
            gr.Markdown(
                """
                ## Metrics Explanation
                - **WER (%)**: Word Error Rate (lower is better) - measures word-level accuracy
                - **CER (%)**: Character Error Rate (lower is better) - measures character-level accuracy
                - **Combined Score (%)**: Weighted average of WER (70%) and CER (30%) - provides a balanced evaluation
                """
            )
        
        with gr.TabItem("📊 Submit New Results"):
            gr.Markdown(
                """
                ### Submit a new model for evaluation
                
                Upload a CSV file with 'id' and 'text' columns to evaluate your ASR predictions.
                The 'id's must match those in the reference dataset.
                """
            )
            
            with gr.Row():
                model_name_input = gr.Textbox(label="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="Updated Leaderboard",
                value=display_leaderboard,
                interactive=False
            )
            
            submit_btn.click(
                fn=process_submission,
                inputs=[model_name_input, csv_upload],
                outputs=[output_msg, leaderboard_display]
            )

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