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| import time | |
| import traceback | |
| import gradio as gr | |
| from gradio_molecule3d import Molecule3D | |
| from run_on_seq import run_on_sample_seqs | |
| from env_consts import RUN_CONFIG_PATH, OUTPUT_PROT_PATH, OUTPUT_LIG_PATH, MODEL_NAME_TO_CKPT | |
| def predict(input_sequence, input_ligand, input_protein, model_variation): | |
| print("Strating inference!!!!!!!!!!!!!!!!!", input_sequence, input_ligand, input_protein) | |
| start_time = time.time() | |
| try: | |
| ckpt_path = MODEL_NAME_TO_CKPT[model_variation] | |
| metrics = run_on_sample_seqs(input_sequence, input_protein, input_ligand, OUTPUT_PROT_PATH, OUTPUT_LIG_PATH, | |
| RUN_CONFIG_PATH, ckpt_path) | |
| end_time = time.time() | |
| run_time = end_time - start_time | |
| return [OUTPUT_PROT_PATH, OUTPUT_LIG_PATH], metrics, run_time | |
| except Exception as e: | |
| print(f"Error during inference: {e}") | |
| traceback.print_exc() # Print the full traceback | |
| return None, {"error": str(e)}, "Error occurred" # return error message to the output. | |
| with gr.Blocks() as app: | |
| print("Starting app!!!!") | |
| gr.Markdown("DockFormer") | |
| model_variation = gr.Dropdown( | |
| choices=["DockFormer-Screen", "DockFormer-PDBBind", "DockFormer-PLINDER"], | |
| label="Select model variation", | |
| value="DockFormer-Screen" # Default value | |
| ) | |
| # gr.Markdown("Title, description, and other information about the model") | |
| with gr.Row(): | |
| input_sequence = gr.Textbox(lines=3, label="Input Protein sequence (FASTA)") | |
| input_ligand = gr.Textbox(lines=3, label="Input ligand SMILES") | |
| with gr.Row(): | |
| # input_msa = gr.File(label="Input Protein MSA (A3M)") | |
| input_protein = gr.File(label="Input protein monomer") | |
| # define any options here | |
| # for automated inference the default options are used | |
| # slider_option = gr.Slider(0,10, label="Slider Option") | |
| # checkbox_option = gr.Checkbox(label="Checkbox Option") | |
| # dropdown_option = gr.Dropdown(["Option 1", "Option 2", "Option 3"], label="Radio Option") | |
| btn = gr.Button("Run Inference") | |
| gr.Examples( | |
| [ | |
| [ | |
| "MLLLPLPLLLFLLCSRAEAGEIIGGTESKPHSRPYMAYLEIVTSNGPSKFCGGFLIRRNFVLTAAHCAGRSITVTLGAHNITEEEDTWQKLEVIKQFRHPKYNTSTLHHDIMLLKLKEKASLTLAVGTLPFPSQKNFVPPGRMCRVAGWGRTGVLKPGSDTLQEVKLRLMDPQACSHFRDFDHNLQLCVGNPRKTKSAFKGDSGGPLLCAGVAQGIVSYGRSDAKPPAVFTRISHYRPWINQILQAN", | |
| "[nH]1c5c(c(c1C(c2ccccc2)C3=C(C(CC3=O)c4ccccc4)O)CCNC(=O)C)ccc(c5)CC", | |
| "resources/example/L1001.pdb" | |
| ], | |
| ], | |
| [input_sequence, input_ligand, input_protein], | |
| ) | |
| reps = [ | |
| { | |
| "model": 0, | |
| "style": "cartoon", | |
| "color": "whiteCarbon", | |
| }, | |
| { | |
| "model": 1, | |
| "style": "stick", | |
| "color": "greenCarbon", | |
| } | |
| ] | |
| out = Molecule3D(reps=reps) | |
| metrics = gr.JSON(label="Metrics") | |
| run_time = gr.Textbox(label="Runtime") | |
| btn.click(predict, inputs=[input_sequence, input_ligand, input_protein, model_variation], | |
| outputs=[out, metrics, run_time]) | |
| app.launch() | |