chore: lint

inference_app.py

@@ -18,58 +18,55 @@ from gradio_molecule3d import Molecule3D
 
 EVAL_METRICS = ["system", "L_rms", "I_rms", "F_nat", "DOCKQ", "CAPRI_class"]
 
+
 def predict(
-    receptor_pdb: Path, 
-    ligand_pdb: Path, 
-    receptor_fasta: Path | None = None, 
+    receptor_pdb: Path,
+    ligand_pdb: Path,
+    receptor_fasta: Path | None = None,
     ligand_fasta: Path | None = None,
 ) -> tuple[str, float]:
     start_time = time.time()
     # Do inference here
-    # return an output pdb file with the protein and two chains R and L.  
+    # return an output pdb file with the protein and two chains R and L.
     receptor = atoms.atom_array_from_pdb_file(receptor_pdb, extra_fields=["b_factor"])
     ligand = atoms.atom_array_from_pdb_file(ligand_pdb, extra_fields=["b_factor"])
     receptor = atoms.normalize_orientation(receptor)
     ligand = atoms.normalize_orientation(ligand)
-    
+
     # Number of random poses to generate
     M = 50
-    # Inititalize an empty stack with shape (m x n x 3) 
+    # Inititalize an empty stack with shape (m x n x 3)
     stack = AtomArrayStack(M, ligand.shape[0])
-    
+
     # copy annotations from ligand
     for annot in ligand.get_annotation_categories():
         stack.set_annotation(annot, np.copy(ligand.get_annotation(annot)))
-    
+
     # Random translations sampled along 0-50 angstroms per axis
-    translation_magnitudes = np.linspace(
-        0, 26,
-        num=26,
-        endpoint=False
-    )
+    translation_magnitudes = np.linspace(0, 26, num=26, endpoint=False)
     # generate one pose at a time
     for i in range(M):
         q = R.random()
         translation_vec = [
-            random.choice(translation_magnitudes), # x
-            random.choice(translation_magnitudes), # y
-            random.choice(translation_magnitudes), # z
+            random.choice(translation_magnitudes),  # x
+            random.choice(translation_magnitudes),  # y
+            random.choice(translation_magnitudes),  # z
         ]
         # transform the ligand chain
         stack.coord[i, ...] = q.apply(ligand.coord) + translation_vec
-        
+
     # Find clashes (1.2 A contact radius)
     stack_conts = get_stack_contacts(receptor, stack, threshold=1.2)
-    
+
     # Keep the "best" pose based on pose w/fewest clashes
     pose_clashes = []
     for i in range(stack_conts.shape[0]):
         pose_conts = stack_conts[i]
         pose_clashes.append((i, np.argwhere(pose_conts != -1).shape[0]))
-    
+
     best_pose_idx = sorted(pose_clashes, key=lambda x: x[1])[0][0]
     best_pose = receptor + stack[best_pose_idx]
-    
+
     output_dir = Path(receptor_pdb).parent
     # System ID
     pdb_name = Path(receptor_pdb).stem + "--" + Path(ligand_pdb).name
@@ -81,8 +78,8 @@ def predict(
 
 
 def evaluate(
-    system_id: str, 
-    prediction_pdb: Path, 
+    system_id: str,
+    prediction_pdb: Path,
 ) -> tuple[pd.DataFrame, float]:
     start_time = time.time()
     system = PinderSystem(system_id)
@@ -90,7 +87,16 @@ def evaluate(
     bdq = BiotiteDockQ(native, Path(prediction_pdb), parallel_io=False)
     metrics = bdq.calculate()
     metrics = metrics[["system", "LRMS", "iRMS", "Fnat", "DockQ", "CAPRI"]].copy()
-    metrics.rename(columns={"LRMS": "L_rms", "iRMS": "I_rms", "Fnat": "F_nat", "DockQ": "DOCKQ", "CAPRI": "CAPRI_class"}, inplace=True)
+    metrics.rename(
+        columns={
+            "LRMS": "L_rms",
+            "iRMS": "I_rms",
+            "Fnat": "F_nat",
+            "DockQ": "DOCKQ",
+            "CAPRI": "CAPRI_class",
+        },
+        inplace=True,
+    )
     end_time = time.time()
     run_time = end_time - start_time
     pred = Structure(Path(prediction_pdb))
@@ -102,117 +108,131 @@ def evaluate(
 
 with gr.Blocks() as app:
     with gr.Tab("🧬 PINDER inference template"):
-      gr.Markdown("Title, description, and other information about the model")   
-      with gr.Row():
-          with gr.Column():
-              input_protein_1 = gr.File(label="Input Protein 1 monomer (PDB)")
-              input_fasta_1 = gr.File(label="Input Protein 1 monomer sequence (FASTA)")
-          with gr.Column():
-              input_protein_2 = gr.File(label="Input Protein 2 monomer (PDB)")
-              input_fasta_2 = gr.File(label="Input Protein 2 monomer sequence (FASTA)")
-          
-          
-      
-      # 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(
-          [
-              [
-                  "8i5w_R.pdb",
-                  "8i5w_R.fasta",
-                  "8i5w_L.pdb",
-                  "8i5w_L.fasta",
-              ],
-          ],
-          [input_protein_1, input_fasta_1, input_protein_2, input_fasta_2],
-      )
-      reps =    [
-      {
-        "model": 0,
-        "style": "cartoon",
-        "chain": "R",
-        "color": "whiteCarbon",
-      },
-      {
-        "model": 0,
-        "style": "cartoon",
-        "chain": "L",
-        "color": "greenCarbon",
-      },
-      {
-        "model": 0,
-        "chain": "R",
-        "style": "stick",
-        "sidechain": True,
-        "color": "whiteCarbon",
-      },
-      {
-        "model": 0,
-        "chain": "L",
-        "style": "stick",
-        "sidechain": True,
-        "color": "greenCarbon"
-      }      
-    ]
-      
-      out = Molecule3D(reps=reps)
-      run_time = gr.Textbox(label="Runtime")
-
-      btn.click(predict, inputs=[input_protein_1, input_protein_2, input_fasta_1,  input_fasta_2], outputs=[out, run_time])
+        gr.Markdown("Title, description, and other information about the model")
+        with gr.Row():
+            with gr.Column():
+                input_protein_1 = gr.File(label="Input Protein 1 monomer (PDB)")
+                input_fasta_1 = gr.File(
+                    label="Input Protein 1 monomer sequence (FASTA)"
+                )
+            with gr.Column():
+                input_protein_2 = gr.File(label="Input Protein 2 monomer (PDB)")
+                input_fasta_2 = gr.File(
+                    label="Input Protein 2 monomer sequence (FASTA)"
+                )
+
+        # 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(
+            [
+                [
+                    "8i5w_R.pdb",
+                    "8i5w_R.fasta",
+                    "8i5w_L.pdb",
+                    "8i5w_L.fasta",
+                ],
+            ],
+            [input_protein_1, input_fasta_1, input_protein_2, input_fasta_2],
+        )
+        reps = [
+            {
+                "model": 0,
+                "style": "cartoon",
+                "chain": "R",
+                "color": "whiteCarbon",
+            },
+            {
+                "model": 0,
+                "style": "cartoon",
+                "chain": "L",
+                "color": "greenCarbon",
+            },
+            {
+                "model": 0,
+                "chain": "R",
+                "style": "stick",
+                "sidechain": True,
+                "color": "whiteCarbon",
+            },
+            {
+                "model": 0,
+                "chain": "L",
+                "style": "stick",
+                "sidechain": True,
+                "color": "greenCarbon",
+            },
+        ]
+
+        out = Molecule3D(reps=reps)
+        run_time = gr.Textbox(label="Runtime")
+
+        btn.click(
+            predict,
+            inputs=[input_protein_1, input_protein_2, input_fasta_1, input_fasta_2],
+            outputs=[out, run_time],
+        )
     with gr.Tab("⚖️ PINDER evaluation template"):
-      with gr.Row():
-          with gr.Column():
-              input_system_id = gr.Textbox(label="PINDER system ID")
-              input_prediction_pdb = gr.File(label="Top ranked prediction (PDB with chains R and L)")
-
-      eval_btn = gr.Button("Run Evaluation")
-      gr.Examples(
-          [
-              [
-                  "3g9w__A1_Q71LX4--3g9w__D1_P05556",
-                  "3g9w_R--3g9w_L.pdb",
-              ],
-          ],
-          [input_system_id, input_prediction_pdb],
-      )
-      reps = [
-      {
-        "model": 0,
-        "style": "cartoon",
-        "chain": "R",
-        "color": "greenCarbon",
-      },
-      {
-        "model": 0,
-        "style": "cartoon",
-        "chain": "L",
-        "color": "cyanCarbon",
-      },
-      {
-        "model": 1,
-        "style": "cartoon",
-        "chain": "R",
-        "color": "grayCarbon",
-      },
-      {
-        "model": 1,
-        "style": "cartoon",
-        "chain": "L",
-        "color": "blueCarbon",
-      },
-    ]
-      
-      pred_native = Molecule3D(reps=reps, config={"backgroundColor": "black"})
-      eval_run_time = gr.Textbox(label="Evaluation runtime")
-      metric_table = gr.DataFrame(pd.DataFrame([], columns=EVAL_METRICS),label="Evaluation metrics")
-
-      eval_btn.click(evaluate, inputs=[input_system_id, input_prediction_pdb], outputs=[metric_table, pred_native, eval_run_time])
+        with gr.Row():
+            with gr.Column():
+                input_system_id = gr.Textbox(label="PINDER system ID")
+                input_prediction_pdb = gr.File(
+                    label="Top ranked prediction (PDB with chains R and L)"
+                )
+
+        eval_btn = gr.Button("Run Evaluation")
+        gr.Examples(
+            [
+                [
+                    "3g9w__A1_Q71LX4--3g9w__D1_P05556",
+                    "3g9w_R--3g9w_L.pdb",
+                ],
+            ],
+            [input_system_id, input_prediction_pdb],
+        )
+        reps = [
+            {
+                "model": 0,
+                "style": "cartoon",
+                "chain": "R",
+                "color": "greenCarbon",
+            },
+            {
+                "model": 0,
+                "style": "cartoon",
+                "chain": "L",
+                "color": "cyanCarbon",
+            },
+            {
+                "model": 1,
+                "style": "cartoon",
+                "chain": "R",
+                "color": "grayCarbon",
+            },
+            {
+                "model": 1,
+                "style": "cartoon",
+                "chain": "L",
+                "color": "blueCarbon",
+            },
+        ]
+
+        pred_native = Molecule3D(reps=reps, config={"backgroundColor": "black"})
+        eval_run_time = gr.Textbox(label="Evaluation runtime")
+        metric_table = gr.DataFrame(
+            pd.DataFrame([], columns=EVAL_METRICS), label="Evaluation metrics"
+        )
+
+        eval_btn.click(
+            evaluate,
+            inputs=[input_system_id, input_prediction_pdb],
+            outputs=[metric_table, pred_native, eval_run_time],
+        )
 
 app.launch()