Update inference_app.py

inference_app.py

@@ -21,86 +21,10 @@ from plinder.eval.docking.write_scores import evaluate
 
 EVAL_METRICS = ["system", "LDDT-PLI", "LDDT-LP", "BISY-RMSD"]
 
+EVAL_METRICS_PINDER = ["system","L_rms", "I_rms", "F_nat", "DOCKQ", "CAPRI_class"]
+
 
-def vina(
-    ligand, receptor, pocket_center, output_folder: Path, size=10, max_num_poses=5
-):
-    app = VinaApp(
-        ligand,
-        receptor,
-        center=pocket_center,
-        size=[size, size, size],
-    )
-    app.set_max_number_of_models(max_num_poses)
-    app.start()
-    app.join()
-    docked_ligand = from_template(ligand, app.get_ligand_coord())
-    docked_ligand = docked_ligand[..., ~np.isnan(docked_ligand.coord[0]).any(axis=-1)]
-    output_files = []
-    for i in range(max_num_poses):
-        sdf_file = MOLFile()
-        sdf_file.set_structure(docked_ligand[i])
-        output_file = output_folder / f"docked_ligand_{i}.sdf"
-        sdf_file.write(output_file)
-        output_files.append(output_file)
-    return output_files
-
-
-def predict(
-    input_sequence: str,
-    input_ligand: str,
-    input_msa: gr.File | None = None,
-    input_protein: gr.File | None = None,
-    max_num_poses: int = 1,
-):
-    """
-    Main prediction function that calls ligsite and smina
-    Parameters
-    ----------
-    input_sequence: str
-        monomer sequence
-    input_ligand: str
-        ligand as SMILES string
-    input_msa: gradio.File | None
-        Gradio file object to MSA a3m file
-    input_protein: gradio.File | None
-        Gradio file object to monomer protein structure as CIF file
-    max_num_poses: int
-        Number of poses to generate
-    Returns
-    -------
-    output_structures: tuple
-        (output_protein, output_ligand_sdf)
-    run_time: float
-        run time of the program
-    """
-    start_time = time.time()
 
-    if input_protein is None:
-        raise gr.Error("need input_protein")
-    print(input_protein)
-    ligand_file = Path(input_protein).parent / "ligand.sdf"
-    print(ligand_file)
-    conformer = Chem.AddHs(Chem.MolFromSmiles(input_ligand))
-    AllChem.EmbedMolecule(conformer)
-    AllChem.MMFFOptimizeMolecule(conformer)
-    Chem.SDWriter(ligand_file).write(conformer)
-    ligand = SDFile.read(ligand_file).record.get_structure()
-    receptor = load_structure(input_protein, include_bonds=True)
-    docking_poses = vina(
-        ligand,
-        receptor,
-        centroid(receptor),
-        Path(input_protein).parent,
-        max_num_poses=max_num_poses,
-    )
-    end_time = time.time()
-    run_time = end_time - start_time
-    pdb_file = PDBFile()
-    pdb_file.set_structure(receptor)
-    output_pdb = Path(input_protein).parent / "receptor.pdb"
-    pdb_file.write(output_pdb)
-    return [str(output_pdb), str(docking_poses[0])], run_time
 
 
 def get_metrics(
@@ -109,6 +33,8 @@ def get_metrics(
     ligand_file: Path,
     flexible: bool = True,
     posebusters: bool = True,
+    methodname: str,
+    store:bool =True
 ) -> tuple[pd.DataFrame, float]:
     start_time = time.time()
     metrics = pd.DataFrame(
@@ -159,9 +85,20 @@ def get_metrics(
     return metrics, run_time
 
 
+def get_metrics_pinder(
+    system_id: str,
+    receptor_file: Path,
+    ligand_file: Path,
+    flexible: bool = True,
+    posebusters: bool = True,
+    methodname: str,
+    store:bool =True
+) -> tuple[pd.DataFrame, float]:
+    return pd.DataFrame(), 0
+
 with gr.Blocks() as app:
     with gr.Tab("🧬 PINDER evaluation template"):
-         with gr.Row():
+        with gr.Row():
             with gr.Column():
                 input_system_id_pinder = gr.Textbox(label="PINDER system ID")
                 input_receptor_file_pinder = gr.File(label="Receptor file")