feat: add instructions

app.py

@@ -38,10 +38,40 @@ def fold_prot_locally(sequence):
     pdb = convert_outputs_to_pdb(output)
     return pdb
 
+sample_code = """
+## Sample usage
+
+```python
+from gradio_client import Client
+
+client = Client("https://wwydmanski-esmfold.hf.space/")
+
+def fold_huggingface(sequence, fname=None):
+    result = client.predict(
+                sequence,	# str in 'sequence' Textbox component
+                api_name="/predict")
+    result = eval(result)[0]
+    if fname is None:
+        with tempfile.NamedTemporaryFile("w", delete=False, suffix=".pdb", prefix="esmfold_") as fp:
+            fp.write(result)
+            fp.flush()
+            return fp.name
+    else:
+        with open(fname, "w") as fp:
+            fp.write(result)
+            fp.flush()
+        return fname
+
+pdb_fname = fold_huggingface("MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN")
+
+```
+"""
+
 tokenizer = AutoTokenizer.from_pretrained("facebook/esmfold_v1")
 model = EsmForProteinFolding.from_pretrained("facebook/esmfold_v1", low_cpu_mem_usage=True).cuda()
 model.esm = model.esm.half()
 torch.backends.cuda.matmul.allow_tf32 = True
 
-iface = gr.Interface(fn=fold_prot_locally, inputs="text", outputs="text")
-iface.launch()
+iface = gr.Interface(fn=fold_prot_locally, inputs="text", outputs="text", article=sample_code, title="ESMFold")
+
+iface.launch()