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, CUSTOM_MESSAGE
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 = "Sep 9th 2023"

column_names = {
    "MODEL": "Model",
    "Avg. WER": "Average WER ⬇️",    
    "RTF": "RTF (1e-3) ⬇️",
    "Common Voice WER": "Common Voice WER ⬇️",
    "D_AVG_CV_WER": "Delta AVG-CV WER",
}

# Skipping testings just uing the numbers computed in the original space for my sanity sake
# eval_queue_repo, requested_models, csv_results = load_all_info_from_dataset_hub()

# if not csv_results.exists():
#     raise Exception(f"CSV file {csv_results} does not exist locally")
    
# # Get csv with data and parse columns
# original_df = pd.read_csv(csv_results)

data = [
    ["nvidia/stt_en_fastconformer_transducer_xlarge",
     12.3, 8.06, 7.26],
    
    ["nvidia/stt_en_fastconformer_transducer_xxlarge",
     14.4, 8.07, 6.07],
    
    ["openai/whisper-large-v2",
     12.7, 8.16, 10.12],
    
    ["nvidia/stt_en_fastconformer_ctc_xxlarge",
     5, 8.34, 8.31],
    
    ["nvidia/stt_en_conformer_ctc_large",
     7.5, 8.39, 9.1],
    
    ["openai/whisper-medium.en",
     10.7, 8.5, 11.96],
    
    ["nvidia/stt_en_fastconformer_ctc_xlarge",
     2.9, 8.52, 7.51],
    
    ["nvidia/stt_en_fastconformer_ctc_large",
     1.8, 8.9, 8.56],
    
    ["nvidia/stt_en_fastconformer_transducer_large",
     10.4, 8.94, 8.04],
    
    ["openai/whisper-large",
     12.7, 9.2, 10.92],
    
    ["nvidia/stt_en_conformer_transducer_large",
     21.8, 9.27, 7.36],
    
    ["openai/whisper-small.en",
     8.3, 9.34, 15.13],
    
    ["nvidia/stt_en_conformer_transducer_small",
     17.7, 10.81, 14.35],
    
    ["openai/whisper-base.en",
     7.2, 11.67, 21.77],
    
    ["nvidia/stt_en_conformer_ctc_small",
     3.2, 11.77, 16.59],
    
    ["patrickvonplaten/wav2vec2-large-960h-lv60-self-4-gram",
     20.1, 13.65, 20.05],
    
    ["facebook/wav2vec2-large-960h-lv60-self",
     2.5, 14.47, 22.15],
    
    ["openai/whisper-tiny.en",
     9.1, 14.96, 31.09],
    
    ["patrickvonplaten/hubert-xlarge-ls960-ft-4-gram",
     24.5, 15.11, 19.16],
    
    ["speechbrain/asr-wav2vec2-librispeech",
     2.6, 15.61, 23.71],
    
    ["facebook/hubert-xlarge-ls960-ft",
     6.3, 15.81, 22.05],
    
    ["facebook/mms-1b-all",
     5.9, 15.85, 21.23],
    
    ["facebook/hubert-large-ls960-ft",
     2.6, 15.93, 23.12],
    
    ["facebook/wav2vec2-large-robust-ft-libri-960h",
     2.7, 16.07, 22.57],
    
    ["facebook/wav2vec2-conformer-rel-pos-large-960h-ft",
     5.2, 17, 23.01],
    
    ["facebook/wav2vec2-conformer-rope-large-960h-ft",
     7.8, 17.06, 23.08],
    
    ["facebook/wav2vec2-large-960h",
     1.8, 21.76, 34.01],
    
    ["facebook/wav2vec2-base-960h",
     1.2, 26.41, 41.75]
]

columns = [
    "Model", "RTF (1e-3) ⬇️", "Average WER ⬇️", "Common Voice WER ⬇️"
]

original_df = pd.DataFrame(data, columns=columns)

# Formats the columns
def formatter(x):
    x = round(x, 2)
    return x

for col in original_df.columns:
    if col.lower() == "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
 
original_df.rename(columns=column_names, inplace=True)

# Compute delta between average WER and CV WER
original_df['Abs. Detla WER'] = abs(original_df['Average WER ⬇️'] - original_df['Common Voice WER ⬇️'])
original_df['Abs. Detla WER'] = pd.to_numeric(original_df['Abs. Detla WER'], errors='coerce') # Convert to numerical data type
original_df['Abs. Detla WER'] = original_df['Abs. Detla WER'].apply(lambda x: round(x, 2) if not pd.isna(x) else x) # Round and handle NaN values

original_df.sort_values(by='Abs. Detla 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() as demo:
    gr.HTML(BANNER, elem_id="banner")
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") 
    gr.Markdown(CUSTOM_MESSAGE, 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,
                max_rows=None,
                elem_id="leaderboard-table",
                interactive=False,
                visible=True,
                )

        with gr.TabItem("📈 Metrics", elem_id="od-benchmark-tab-table", id=1):
            gr.Markdown(METRICS_TAB_TEXT, elem_classes="markdown-text")

        with gr.TabItem("✉️✨ Request a model here!", elem_id="od-benchmark-tab-table", id=2):
            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",
            ).style(show_copy_button=True)

demo.launch()