Create app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModel, AutoConfig
+import pandas as pd
+
+tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")
+config = AutoConfig.from_pretrained("ChatterjeeLab/MetaLATTE")
+model = AutoModel.from_pretrained("ChatterjeeLab/MetaLATTE", config=config)
+
+def predict(sequence):
+    inputs = tokenizer(sequence, return_tensors="pt")
+    raw_probs, predictions = model(**inputs)
+    
+    id2label = config.id2label
+    results = {}
+    for i, pred in enumerate(predictions[0]):
+        metal = id2label[i]
+        probability = raw_probs[0][i].item()
+        results[metal] = '✓' if pred == 1 else ''
+    
+    df = pd.DataFrame([results])
+    return df
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=3, placeholder="Enter protein sequence here..."),
+    outputs=gr.Dataframe(headers=list(config.id2label.values())),
+    title="MetaLATTE: Metal Binding Prediction",
+    description="Enter a protein sequence to predict its metal binding properties."
+)
+
+iface.launch()