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
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 = "September 7h 2023"
GPU_MODEL = "NVIDIA Tesla M60"

column_names = {"AP-IoU=0.50:0.95-area=all-maxDets=100": "AP",
                 "AP-IoU=0.50-area=all-maxDets=100": "AP@.50",
                 "AP-IoU=0.75-area=all-maxDets=100": "AP@.75",
                 "AP-IoU=0.50:0.95-area=small-maxDets=100" : "AP-S",
                 "AP-IoU=0.50:0.95-area=medium-maxDets=100": "AP-M",
                 "AP-IoU=0.50:0.95-area=large-maxDets=100": "AP-L",
                 "AR-IoU=0.50:0.95-area=all-maxDets=1": "AR1",
                 "AR-IoU=0.50:0.95-area=all-maxDets=10": "AR10",
                 "AR-IoU=0.50:0.95-area=all-maxDets=100": "AR100",
                 "AR-IoU=0.50:0.95-area=small-maxDets=100": "AR-S",
                 "AR-IoU=0.50:0.95-area=medium-maxDets=100": "AR-M",
                 "AR-IoU=0.50:0.95-area=large-maxDets=100": "AR-L",
                 "estimated_fps": "FPS(*)",
                 "hub_license": "hub license",
                 }

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)

# Formats the columns
def decimal_formatter(x):
    x = "{:.2f}".format(x)
    return x

def perc_formatter(x):
    x = "{:.2%}".format(x)
    while len(x) < 6:
        x = f"0{x}"
    return x

# Drop columns not specified in dictionary
cols_to_drop = [col for col in original_df.columns if col not in column_names]
original_df.drop(cols_to_drop, axis=1, inplace=True)

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)))
    elif col == "estimated_fps":
        original_df[col] = original_df[col].apply(decimal_formatter) # For decimal values
    elif col == "hub_license":
        continue
    else:
        original_df[col] = original_df[col].apply(perc_formatter) # For % values
 
original_df.rename(columns=column_names, 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("COCO validation 2017 dataset")

    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")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 COCO val 2017", 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"(*) FPS was measured using *{GPU_MODEL}* processing 1 image per batch. Refer to the 📈 \"Metrics\" tab for further details.", elem_classes="markdown-text")
    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()