app.py

import gradio as gr
import pandas as pd
import json
from constants import BANNER, INTRODUCTION_TEXT, CITATION_TEXT, METRICS_TAB_TEXT, DIR_OUTPUT_REQUESTS, LEADERBOARD_CSS
from init import is_model_on_hub, upload_file, load_all_info_from_dataset_hub
from utils_display import AutoEvalColumn, fields, make_clickable_model, styled_error, styled_message
from datetime import datetime, timezone

LAST_UPDATED = "Nov 22th 2024"

column_names = {
    "MODEL": "Model",
    "Avg. WER": "Average WER ⬇️",    
    "RTFx": "RTFx ⬆️️",
    "AMI WER": "AMI",
    "Earnings22 WER": "Earnings22",
    "Gigaspeech WER": "Gigaspeech",
    "LS Clean WER": "LS Clean",
    "LS Other WER": "LS Other",
    "SPGISpeech WER": "SPGISpeech",
    "Tedlium WER": "Tedlium",
    "Voxpopuli WER": "Voxpopuli",
}

whisper_column_names = {
    "MODEL": "Model",
    "Avg. WER": "Average WER ⬇️",    
    "RTFx": "RTFx ⬆️️",
    "Backend": "Backend",
    "Hardware": "Device",
    "AMI WER": "AMI",
    "Earnings22 WER": "Earnings22",
    "Gigaspeech WER": "Gigaspeech",
    "LS Clean WER": "LS Clean",
    "LS Other WER": "LS Other",
    "SPGISpeech WER": "SPGISpeech",
    "Tedlium WER": "Tedlium",
    "Voxpopuli WER": "Voxpopuli",
}

eval_queue_repo, requested_models, csv_results, whisper_eval_queue_repo, whisper_csv_results = load_all_info_from_dataset_hub()

if not csv_results.exists():
    raise Exception(f"CSV file {csv_results} does not exist locally")
if not whisper_csv_results.exists():
    raise Exception(f"CSV file {whisper_csv_results} does not exist locally")

# Get csv with data and parse columns
original_df = pd.read_csv(csv_results)
whisper_df = pd.read_csv(whisper_csv_results)
# Formats the columns
def formatter(x):
    if type(x) is str:
        x = x
    else: 
        x = round(x, 2)
    return x

for col in original_df.columns:
    if col == "model":
        original_df[col] = original_df[col].apply(lambda x: x.replace(x, make_clickable_model(x)))
    else:
        original_df[col] = original_df[col].apply(formatter) # For numerical values
        whisper_df[col] = whisper_df[col].apply(formatter) # For numerical values
original_df.rename(columns=column_names, inplace=True)
original_df.sort_values(by='Average WER ⬇️', inplace=True)
whisper_df.rename(columns=whisper_column_names, inplace=True)
whisper_df.sort_values(by='Average WER ⬇️', inplace=True)

COLS = [c.name for c in fields(AutoEvalColumn)]
TYPES = [c.type for c in fields(AutoEvalColumn)]


def request_model(model_text, chbcoco2017):
    
    # Determine the selected checkboxes
    dataset_selection = []
    if chbcoco2017:
        dataset_selection.append("ESB Datasets tests only")

    if len(dataset_selection) == 0:
        return styled_error("You need to select at least one dataset")
        
    base_model_on_hub, error_msg = is_model_on_hub(model_text)

    if not base_model_on_hub:
        return styled_error(f"Base model '{model_text}' {error_msg}")
    
    # Construct the output dictionary
    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
    required_datasets = ', '.join(dataset_selection)
    eval_entry = {
        "date": current_time,
        "model": model_text,
        "datasets_selected": required_datasets
    }
    
    # Prepare file path 
    DIR_OUTPUT_REQUESTS.mkdir(parents=True, exist_ok=True)
    
    fn_datasets = '@ '.join(dataset_selection)
    filename = model_text.replace("/","@") + "@@" + fn_datasets 
    if filename in requested_models:
        return styled_error(f"A request for this model '{model_text}' and dataset(s) was already made.")
    try:
        filename_ext = filename + ".txt"
        out_filepath = DIR_OUTPUT_REQUESTS / filename_ext

        # Write the results to a text file
        with open(out_filepath, "w") as f:
            f.write(json.dumps(eval_entry))
            
        upload_file(filename, out_filepath)
        
        # Include file in the list of uploaded files
        requested_models.append(filename)
        
        # Remove the local file
        out_filepath.unlink()

        return styled_message("🤗 Your request has been submitted and will be evaluated soon!</p>")
    except Exception as e:
        return styled_error(f"Error submitting request!")

with gr.Blocks(css=LEADERBOARD_CSS) as demo:
    gr.HTML(BANNER, elem_id="banner")
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 Leaderboard", elem_id="od-benchmark-tab-table", id=0):
            leaderboard_table = gr.components.Dataframe(
                value=original_df,
                datatype=TYPES,
                elem_id="leaderboard-table",
                interactive=False,
                visible=True,
                )
        with gr.TabItem("🔄 Whisper Model Leaderboard", elem_id="whisper-backends-tab", id=1):
            gr.Markdown("## Whisper Model Performance Across Different Backends", elem_classes="markdown-text")
            gr.Markdown("This table shows how different Whisper model implementations compare in terms of performance and speed.", elem_classes="markdown-text")
            
            with gr.Row():
                backend_filter = gr.Dropdown(
                    choices= sorted(whisper_df["Backend"].unique().tolist()),
                    value="All",
                    label="Filter by Backend",
                    elem_id="backend-filter",
                    multiselect=True  # Enable multiple selection
                )   
                device_choices = sorted(whisper_df["Device"].unique().tolist())
                device_filter = gr.Dropdown(
                    choices=device_choices,
                    value="All",
                    label="Filter by Device",
                    elem_id="device-filter",
                    multiselect=True  # Enable multiple selection
                )
            
            whisper_table = gr.components.Dataframe(
                value=whisper_df,
                datatype=TYPES,
                elem_id="whisper-table",
                interactive=False,
                visible=True,
            )

            def filter_whisper_table(backends, devices):
                filtered_df = whisper_df.copy()
                
                # Handle backend filtering
                if backends and "All" not in backends:
                    filtered_df = filtered_df[filtered_df["Backend"].isin(backends)]
                
                # Handle device filtering
                if devices and "All" not in devices and "Device" in filtered_df.columns:
                    filtered_df = filtered_df[filtered_df["Device"].isin(devices)]
                    
                return filtered_df

            backend_filter.change(
                filter_whisper_table,
                inputs=[backend_filter, device_filter],
                outputs=whisper_table
            )
            device_filter.change(
                filter_whisper_table,
                inputs=[backend_filter, device_filter],
                outputs=whisper_table
            )
        with gr.TabItem("📈 Metrics", elem_id="od-benchmark-tab-table", id=2):
            gr.Markdown(METRICS_TAB_TEXT, elem_classes="markdown-text")

        with gr.TabItem("✉️✨ Request a model here!", elem_id="od-benchmark-tab-table", id=3):
            with gr.Column():
                gr.Markdown("# ✉️✨ Request results for a new model here!", elem_classes="markdown-text")
            with gr.Column():
                gr.Markdown("Select a dataset:", elem_classes="markdown-text")
                with gr.Column():
                    model_name_textbox = gr.Textbox(label="Model name (user_name/model_name)")
                    chb_coco2017 = gr.Checkbox(label="COCO validation 2017 dataset", visible=False, value=True, interactive=False)
                with gr.Column():
                    mdw_submission_result = gr.Markdown()
                    btn_submitt = gr.Button(value="🚀 Request")
                    btn_submitt.click(request_model, 
                                      [model_name_textbox, chb_coco2017], 
                                      mdw_submission_result)

    gr.Markdown(f"Last updated on **{LAST_UPDATED}**", elem_classes="markdown-text")
    
    with gr.Row():
        with gr.Accordion("📙 Citation", open=False):
            gr.Textbox(
                value=CITATION_TEXT, lines=7,
                label="Copy the BibTeX snippet to cite this source",
                elem_id="citation-button",
                show_copy_button=True,
            )

demo.launch(ssr_mode=False)