Create app.py

app.py

@@ -0,0 +1,52 @@
+import gradio as gr
+from gradio_molecule3d import Molecule3D
+import deepchem as dc
+import os
+
+example = Molecule3D().example_inputs()
+
+
+reps =    [
+    {
+      "model": 0,
+      "chain": "",
+      "resname": "",
+      "style": "stick",
+      "color": "whiteCarbon",
+      "residue_range": "",
+      "around": 0,
+      "height": 150,
+      "byres": False,
+      "visible": False
+    }
+  ]
+
+def get_file_name_without_extension(file_path):
+    base_name = os.path.basename(file_path)
+    file_name = os.path.splitext(base_name)[0]
+    return file_name
+
+
+def predict(input_receptor,input_ligand):
+    print(input_ligand)
+    vpg = dc.dock.pose_generation.VinaPoseGenerator()
+    saved_filename = get_file_name_without_extension(input_ligand)
+    complexes, scores = vpg.generate_poses(molecular_complex=(input_receptor,input_ligand),  # protein-ligand files for docking,
+                                            out_dir='./output_directory',
+                                            generate_scores=True
+                                            )
+
+    # print(complexes[0])
+    return scores
+
+
+with gr.Blocks() as docking:
+    inp = [Molecule3D(label="Receptor", reps=reps),Molecule3D(label="Ligand", reps=reps)]
+    out = gr.Textbox(label="Docking Output")#Molecule3D(label="Output", reps=reps)
+    # out = Molecule3D(label="Output", reps=reps)
+    btn = gr.Button("Docking")
+    btn.click(predict, inputs=inp, outputs=out)
+
+
+if __name__ == '__main__':
+  docking.launch()