Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+from transformers import AutoTokenizer, EsmForProteinFolding
+from transformers.models.esm.openfold_utils.protein import to_pdb, Protein as OFProtein
+from transformers.models.esm.openfold_utils.feats import atom14_to_atom37
+import torch
+
+def convert_outputs_to_pdb(outputs):
+    final_atom_positions = atom14_to_atom37(outputs["positions"][-1], outputs)
+    outputs = {k: v.to("cpu").numpy() for k, v in outputs.items()}
+    final_atom_positions = final_atom_positions.cpu().numpy()
+    final_atom_mask = outputs["atom37_atom_exists"]
+    pdbs = []
+    for i in range(outputs["aatype"].shape[0]):
+        aa = outputs["aatype"][i]
+        pred_pos = final_atom_positions[i]
+        mask = final_atom_mask[i]
+        resid = outputs["residue_index"][i] + 1
+        pred = OFProtein(
+            aatype=aa,
+            atom_positions=pred_pos,
+            atom_mask=mask,
+            residue_index=resid,
+            b_factors=outputs["plddt"][i],
+            chain_index=outputs["chain_index"][i] if "chain_index" in outputs else None,
+        )
+        pdbs.append(to_pdb(pred))
+    return pdbs
+
+def fold_prot_locally(sequence):
+    tokenized_input = tokenizer([sequence], return_tensors="pt", add_special_tokens=False)['input_ids']
+
+    with torch.no_grad():
+        output = model(tokenized_input)
+    pdb = convert_outputs_to_pdb(output)
+    return pdb
+
+tokenizer = AutoTokenizer.from_pretrained("facebook/esmfold_v1")
+model = EsmForProteinFolding.from_pretrained("facebook/esmfold_v1", low_cpu_mem_usage=True)
+
+iface = gr.Interface(fn=fold_prot_locally, inputs="text", outputs="text")
+iface.launch()