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| import gradio as gr | |
| from gradio_leaderboard import Leaderboard | |
| from pathlib import Path | |
| import pandas as pd | |
| import os | |
| import json | |
| from envs import API, EVAL_REQUESTS_PATH, TOKEN, QUEUE_REPO | |
| def submit(model_name, model_id, challenge, submission_id, architecture, license): | |
| if model_name == "" or model_id == "" or challenge == "" or submission_id == "" or architecture == "" or license == "": | |
| gr.Error("Please fill all the fields") | |
| return | |
| try: | |
| user_name = "" | |
| if "/" in model_id: | |
| user_name = model_id.split("/")[0] | |
| model_path = model_id.split("/")[1] | |
| eval_entry = { | |
| "model_name": model_name, | |
| "model_id": model_id, | |
| "challenge": challenge, | |
| "submission_id": submission_id, | |
| "architecture": architecture, | |
| "license": license | |
| } | |
| OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}" | |
| os.makedirs(OUT_DIR, exist_ok=True) | |
| out_path = f"{OUT_DIR}/{user_name}_{model_path}.json" | |
| with open(out_path, "w") as f: | |
| f.write(json.dumps(eval_entry)) | |
| print("Uploading eval file") | |
| API.upload_file( | |
| path_or_fileobj=out_path, | |
| path_in_repo=out_path.split("eval-queue/")[1], | |
| repo_id=QUEUE_REPO, | |
| repo_type="dataset", | |
| commit_message=f"Add {model_name} to eval queue", | |
| ) | |
| gr.Info("Successfully submitted", duration=10) | |
| # Remove the local file | |
| os.remove(out_path) | |
| except: | |
| gr.Error("Error submitting the model") | |
| abs_path = Path(__file__).parent | |
| # Any pandas-compatible data | |
| df = pd.read_json(str(abs_path / "leaderboard_data.json")) | |
| with gr.Blocks() as demo: | |
| gr.Markdown(""" | |
| # MLSB 2024 Challenges | |
| """) | |
| with gr.Tab("🎖️ PINDER Leaderboard"): | |
| gr.Markdown("""## PINDER Leaderboard | |
| Evaluating Protein-Protein interaction prediction | |
| """) | |
| Leaderboard( | |
| value=df, | |
| select_columns=["Arch", "Model", "L_rms", "I_rms", | |
| "F_nat", "DOCKQ", "CAPRI"], | |
| search_columns=["model_name_for_query"], | |
| hide_columns=["model_name_for_query",], | |
| filter_columns=["Arch"], | |
| ) | |
| with gr.Tab("🥇 PLINDER Leaderboard"): | |
| gr.Markdown("""## PLINDER Leaderboard | |
| Evaluating Protein-Ligand prediction | |
| """) | |
| Leaderboard( | |
| value=df, | |
| select_columns=["Arch", "Model", "L_rms", "I_rms", | |
| "F_nat", "DOCKQ", "CAPRI"], | |
| search_columns=["model_name_for_query"], | |
| hide_columns=["model_name_for_query",], | |
| filter_columns=["Arch"], | |
| ) | |
| with gr.Tab("✉️ Submit"): | |
| gr.Markdown("""## Submit your model | |
| Submit your model to the leaderboard | |
| """) | |
| model_name = gr.Textbox(label="Model name") | |
| model_id = gr.Textbox(label="username/space e.g mlsb/alphafold3") | |
| challenge = gr.Radio(choices=["PINDER", "PLINDER"],label="Challenge") | |
| submission_id = gr.Textbox(label="Submission ID on CMT") | |
| architecture = gr.Dropdown(choices=["GNN", "CNN", "Physics-based", "Other"],label="Model architecture") | |
| license = gr.Dropdown(choices=["mit", "apache-2.0", "gplv2", "gplv3", "lgpl", "mozilla", "bsd", "other"],label="License") | |
| submit_btn = gr.Button("Submit") | |
| submit_btn.click(submit, inputs=[model_name, model_id, challenge, submission_id, architecture, license], outputs=[]) | |
| if __name__ == "__main__": | |
| demo.launch() |