app.py

import gradio as gr
import pandas as pd
import numpy as np
from datetime import datetime
from pathlib import Path

from constants import (
    BANNER,
    INTRODUCTION_TEXT,
    CITATION_TEXT,
    METRICS_TAB_TEXT,
    DIR_OUTPUT_REQUESTS,
    CSV_PATH
)
from utils_display import make_clickable_model, styled_error, styled_message

LAST_UPDATED = "Oct 14th 2025"
CSV_PATH = Path(CSV_PATH)

if not CSV_PATH.exists():
    raise FileNotFoundError(f"Benchmark CSV not found at {CSV_PATH.resolve()}")

df = pd.read_csv(CSV_PATH)

column_mapping = {
    'fleurs WER': 'FLEURS WER (avg) ⬇️',
    'fleurs CER': 'FLEURS CER (avg) ⬇️',
    'common voice WER': 'CommonVoice WER (avg) ⬇️',
    'common voice CER': 'CommonVoice CER (avg) ⬇️',
    'mana WER': 'MANA WER (avg) ⬇️',
    'mana CER': 'MANA CER (avg) ⬇️',
    'Persian-ASR-Benchmark WER': 'PERSIAN ASR BENCHMARK WER (avg) ⬇️',
    'Persian-ASR-Benchmark CER': 'PERSIAN ASR BENCHMARK CER (avg) ⬇️',
}
df.rename(columns=column_mapping, inplace=True)

wer_avg_cols = [c for c in df.columns if "WER (avg)" in c]
cer_avg_cols = [c for c in df.columns if "CER (avg)" in c]

df["Avg WER ⬇️"] = df[wer_avg_cols].mean(axis=1).round(3)
df["Avg CER ⬇️"] = df[cer_avg_cols].mean(axis=1).round(3)

for col in df.columns:
    if col != "model" and col != "Model" and col != "link" and col != "Parameters":
        df[col] = df[col].round(4)

df = make_clickable_model(df)

df.sort_values(by="Avg CER ⬇️", inplace=True)

# Add rank column based on Avg WER (lowest WER = rank 1)
df["Rank"] = range(1, len(df) + 1)

df.drop(columns=["model"], inplace=True)

column_order = [
    "Rank",
    "Model",
    "Avg CER ⬇️",
    "Avg WER ⬇️",
    # PERSIAN ASR BENCHMARK
    'PERSIAN ASR BENCHMARK CER (avg) ⬇️',
    'PERSIAN ASR BENCHMARK WER (avg) ⬇️',
    # FLEURS
    "FLEURS CER (avg) ⬇️",
    "FLEURS WER (avg) ⬇️",
    # CommonVoice
    "CommonVoice CER (avg) ⬇️",
    "CommonVoice WER (avg) ⬇️",
    # MANA
    "MANA CER (avg) ⬇️",
    "MANA WER (avg) ⬇️",
]

df = df[column_order]

DATATYPES = ["number" if c == "Rank" else "markdown" if c == "Model" else "number" for c in df.columns]

num_columns = len(df.columns)
model_width_percent = 35  
remaining_width = 220 - model_width_percent
other_column_width = remaining_width / (num_columns - 2)

column_widths = ["50" if c == "Rank" else f"{model_width_percent}%" if c == "Model" else "125" if c == "Avg CER ⬇️" else "125" if c == "Avg WER ⬇️" else f"{other_column_width:.1f}%" 
                 for c in df.columns]

# Function to filter dataframe based on selected datasets
def filter_dataframe(show_persian_asr, show_fleurs, show_common_voice, show_mana):
    filtered_df = df.copy()
    
    # Determine which columns to keep based on checkboxes
    cols_to_keep = ["Rank", "Model"]
    
    # Always keep average columns
    cols_to_keep.extend(["Avg CER ⬇️", "Avg WER ⬇️"])
    
    if show_persian_asr:
        cols_to_keep.extend([
            "PERSIAN ASR BENCHMARK CER (avg) ⬇️",
            "PERSIAN ASR BENCHMARK WER (avg) ⬇️",
        ])
    
    if show_fleurs:
        cols_to_keep.extend([
            "FLEURS CER (avg) ⬇️",
            "FLEURS WER (avg) ⬇️",
        ])
    
    if show_common_voice:
        cols_to_keep.extend([
            "CommonVoice CER (avg) ⬇️",
            "CommonVoice WER (avg) ⬇️",
        ])
    
    if show_mana:
        cols_to_keep.extend([
            "MANA CER (avg) ⬇️",
            "MANA WER (avg) ⬇️",
        ])
    
    # Return only the selected columns
    return filtered_df[cols_to_keep]

def request_model(model_text):
    if not model_text.strip():
        return styled_error("Please enter a model name before submitting.")

    DIR_OUTPUT_REQUESTS.mkdir(parents=True, exist_ok=True)
    filename = model_text.replace("/", "@") + ".txt"
    out_path = DIR_OUTPUT_REQUESTS / filename

    if out_path.exists():
        return styled_error(f"Request for '{model_text}' already exists.")

    request_entry = {
        "date": datetime.now().isoformat(),
        "model": model_text,
    }

    try:
        with open(out_path, "w") as f:
            f.write(str(request_entry))
        return styled_message(f"✅ Your request for '{model_text}' has been recorded!")
    except Exception as e:
        return styled_error(f"Error saving request: {e}")

with gr.Blocks(theme=gr.themes.Base(), css="""
    #leaderboard-table {
        font-size: 14px;
    }
    #leaderboard-table td:second-child,
    #leaderboard-table th:second-child {
        max-width: 350px !important;
        white-space: normal !important;
        overflow: hidden;
        text-overflow: ellipsis;
    }   
    #leaderboard-table td:not(:second-child),
    #leaderboard-table th:not(:second-child) {
        text-align: center !important;
        min-width: 100px;
    }
    #leaderboard-table th {
        font-weight: bold;
        background-color: #f0f0f0;
    }
    #banner-image {
        width: 50%;
        margin: 0 auto;
    }
""") as demo:
    gr.Image(BANNER, show_label=False, show_download_button=False, container=False, elem_id="banner-image")
    gr.Markdown(INTRODUCTION_TEXT)

    with gr.Tabs():
        with gr.TabItem("🏅 Leaderboard"):
            gr.Markdown("### Select Datasets to Display")
            with gr.Row():
                persian_asr_checkbox = gr.Checkbox(label="Persian ASR Benchmark", value=True)
                fleurs_checkbox = gr.Checkbox(label="FLEURS", value=True)
                common_voice_checkbox = gr.Checkbox(label="Common Voice", value=True)
                mana_checkbox = gr.Checkbox(label="MANA", value=True)
            
            leaderboard_table = gr.Dataframe(
                value=df,
                datatype=DATATYPES,
                interactive=False,
                wrap=True,
                column_widths=column_widths,
                elem_id="leaderboard-table"
            )
            
            # Update table when checkboxes change
            persian_asr_checkbox.change(
                fn=filter_dataframe,
                inputs=[persian_asr_checkbox, fleurs_checkbox, common_voice_checkbox, mana_checkbox],
                outputs=leaderboard_table
            )
            fleurs_checkbox.change(
                fn=filter_dataframe,
                inputs=[persian_asr_checkbox, fleurs_checkbox, common_voice_checkbox, mana_checkbox],
                outputs=leaderboard_table
            )
            common_voice_checkbox.change(
                fn=filter_dataframe,
                inputs=[persian_asr_checkbox, fleurs_checkbox, common_voice_checkbox, mana_checkbox],
                outputs=leaderboard_table
            )
            mana_checkbox.change(
                fn=filter_dataframe,
                inputs=[persian_asr_checkbox, fleurs_checkbox, common_voice_checkbox, mana_checkbox],
                outputs=leaderboard_table
            )

        with gr.TabItem("📈 Metrics"):
            gr.Markdown(METRICS_TAB_TEXT)

        with gr.TabItem("✉️ Request a Model"):
            gr.Markdown("### ✉️ Submit your model to be evaluated")
            model_name_textbox = gr.Textbox(label="Model name (e.g. user/model_name)")
            submit_button = gr.Button("🚀 Submit Request")
            result_box = gr.Markdown()

            submit_button.click(request_model, inputs=model_name_textbox, outputs=result_box)

        with gr.TabItem("🤗 About"):
            gr.Markdown("""
                ### About
                This leaderboard showcases benchmark results for speech recognition models.
                Data is sourced from local evaluations in `Benchmark_data.csv`.
            """)

    gr.Markdown(f"Last updated on **{LAST_UPDATED}**")
    with gr.Accordion("📙 Citation", open=False):
        gr.Textbox(
            value=CITATION_TEXT,
            label="BibTeX citation",
            show_copy_button=True,
            lines=6
        )
    
    gr.Markdown("""
        ---
        For further information, keep in touch:  
        **info@c1tech.group**
    """)

demo.launch(server_name="0.0.0.0", server_port=7860)