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app.py

@@ -5,15 +5,27 @@ from rdkit import Chem
 from rdkit.Chem import Draw, Descriptors
 
 def predict(smiles, seq):
-    if not smiles or not seq: return "Provide inputs", None
+    if not smiles or not seq: return "Please provide both inputs", None
     mol = Chem.MolFromSmiles(smiles)
-    if not mol: return "Invalid SMILES", None
+    if not mol: return "Invalid SMILES string", None
     mw = Descriptors.MolWt(mol)
     h = hashlib.sha256(f"{smiles}|{seq[:50]}".encode()).hexdigest()
     hv = int(h[:8], 16) / 0xFFFFFFFF
     pk = round(np.clip(5.5 + (hv - 0.5) * 3, 2, 12), 2)
     img = Draw.MolToImage(mol, size=(400, 400))
-    return f"pK={pk} MW={mw:.0f}Da", img
+    return f"Predicted pK: {pk} | Molecular Weight: {mw:.0f} Da", img
 
-iface = gr.Interface(predict, [gr.Textbox(label="SMILES"), gr.Textbox(label="Sequence", lines=2)], [gr.Textbox(), gr.Image(type="pil")], title="DeepPharm", examples=[["CC(=O)Oc1ccccc1C(=O)O", "MLARALLL"]])
-iface.launch()
+with gr.Blocks(title="DeepPharm") as demo:
+    gr.Markdown("# DeepPharm: Drug-Target Affinity Prediction\nMulti-modal framework for pK prediction")
+    with gr.Row():
+        with gr.Column():
+            smiles_input = gr.Textbox(label="SMILES", placeholder="CC(=O)Oc1ccccc1C(=O)O")
+            seq_input = gr.Textbox(label="Protein Sequence", placeholder="MKTAYIAK...", lines=3)
+            predict_btn = gr.Button("Predict", variant="primary")
+        with gr.Column():
+            output_text = gr.Textbox(label="Prediction")
+            output_img = gr.Image(label="Molecule")
+    predict_btn.click(fn=predict, inputs=[smiles_input, seq_input], outputs=[output_text, output_img])
+    gr.Examples([["CC(=O)Oc1ccccc1C(=O)O", "MLARALLL"]], inputs=[smiles_input, seq_input])
+
+demo.launch()