Update app.py

app.py

@@ -1,7 +1,76 @@
+"""
+ZeroGPU Structure Prediction API
+"""
+
+import spaces
 import gradio as gr
+import torch
+from transformers import EsmForProteinFolding, AutoTokenizer
+
+print("Loading ESMFold model...")
+MODEL_NAME = "facebook/esmfold_v1"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = EsmForProteinFolding.from_pretrained(MODEL_NAME)
+
+if torch.cuda.is_available():
+    model = model.cuda()
+    model.esm = model.esm.half()
+    print(f"Model loaded on GPU: {torch.cuda.get_device_name(0)}")
+else:
+    print("Model loaded on CPU")
+
+
+@spaces.GPU(duration=120)
+def predict_structure(sequence: str) -> str:
+    sequence = sequence.strip().upper()
+    valid_aa = set("ACDEFGHIKLMNPQRSTVWY")
+    
+    if not sequence:
+        return "Error: Empty sequence provided"
+    
+    invalid_chars = set(sequence) - valid_aa
+    if invalid_chars:
+        return f"Error: Invalid amino acids found: {invalid_chars}"
+    
+    if len(sequence) > 500:
+        return "Error: Sequence too long (max 500 residues)"
+    
+    try:
+        inputs = tokenizer(sequence, return_tensors="pt", add_special_tokens=False)
+        
+        if torch.cuda.is_available():
+            inputs = {k: v.cuda() for k, v in inputs.items()}
+        
+        with torch.no_grad():
+            outputs = model(**inputs)
+        
+        pdb_string = model.output_to_pdb(outputs)[0]
+        return pdb_string
+        
+    except Exception as e:
+        return f"Error: {str(e)}"
+
 
-def greet(name):
-    return "Hello " + name + "!!"
+with gr.Blocks(title="🧬 Antibody Structure API") as demo:
+    gr.Markdown("""
+    # 🧬 Antibody Structure Prediction API (ZeroGPU)
+    
+    GPU-accelerated ESMFold structure prediction.
+    
+    **API Usage:**
+    ```python
+    from gradio_client import Client
+    client = Client("kmlyyll/antibody-structure-api")
+    pdb = client.predict(sequence, api_name="/predict")
+    ```
+    """)
+    
+    seq_input = gr.Textbox(label="Amino Acid Sequence", placeholder="Enter sequence...", lines=3)
+    predict_btn = gr.Button("Predict Structure", variant="primary")
+    pdb_output = gr.Textbox(label="PDB Output", lines=20)
+    
+    predict_btn.click(fn=predict_structure, inputs=seq_input, outputs=pdb_output, api_name="predict")
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()