Update app.py

app.py

@@ -1,10 +1,15 @@
 import gradio as gr
 import joblib
+from huggingface_hub import hf_hub_download
+import numpy as np
 import pandas as pd
 
-# --- Load model package ---
-model_package_path = "Ym420/xgb_multilabel_model_full_pkl"
-model_package = joblib.load(model_package_path)
+# --- Download model from HF Hub ---
+repo_id = "GiMikawa/Peptide-Function"  # replace with your HF repo
+model_filename = "xgb_multilabel_model_full.pkl"
+
+model_path = hf_hub_download(repo_id=repo_id, filename=model_filename)
+model_package = joblib.load(model_path)
 model = model_package['model']
 feature_columns = model_package['feature_columns']
 
@@ -45,36 +50,47 @@ def extract_features(sequence: str) -> pd.DataFrame:
     df = df.reindex(columns=feature_columns, fill_value=0)
     return df
 
-# --- Prediction ---
-def predict(sequence):
-    sequence = sequence.strip()
-    if not sequence:
-        return "Sequence cannot be empty.", None
-
-    X = extract_features(sequence)
-    probs = model.predict_proba(X)  # List of arrays per target
+# --- Prediction function ---
+def predict_peptide(sequence: str):
+    seq = "".join(sequence.split()).upper()
+    if not seq:
+        return []
 
-    # Format output
-    output = []
-    for i, col in enumerate(model.classes_):
-        output.append({
-            "Target Cell": col,
-            "Probability of Efficacy/Toxicity": float(probs[i][0][1])
-        })
+    X = extract_features(seq)
+    probs_list = model.predict_proba(X)  # list of arrays per target cell
 
-    return sequence, output
+    # Format output as table: Target Cell | Probability
+    table = []
+    for i, target in enumerate(model.classes_):
+        table.append([target, float(probs_list[i][0][1])])
+    return table
 
 # --- Gradio Interface ---
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Textbox(lines=2, placeholder="Enter peptide sequence here..."),
-    outputs=[
-        gr.Textbox(label="Input Sequence"),
-        gr.Dataframe(headers=["Target Cell", "Probability of Efficacy/Toxicity"], datatype="json")
-    ],
-    title="Peptide Antimicrobial Predictor",
-    description="Enter a peptide sequence to predict its antimicrobial efficacy/toxicity against target cells."
-)
+custom_css = """
+footer, .footer {display:none !important;}
+"""
+
+with gr.Blocks(css=custom_css, theme="default") as demo:
+    gr.Markdown("## Peptide Antimicrobial Predictor\nEnter a peptide sequence to predict efficacy/toxicity.")
+    
+    seq_input = gr.Textbox(label="Enter Peptide Sequence")
+    
+    with gr.Row():
+        predict_btn = gr.Button("Predict", variant="primary")
+        clear_btn = gr.Button("Clear")
+    
+    table_output = gr.Dataframe(
+        headers=["Target Cell", "Probability of Efficacy/Toxicity"],
+        datatype=["str","number"],
+        interactive=False
+    )
+    
+    predict_btn.click(fn=predict_peptide, inputs=seq_input, outputs=table_output)
+    clear_btn.click(fn=lambda: ("", []), outputs=[seq_input, table_output])
+    
+    # API endpoint for iOS app
+    gr.api(predict_peptide, api_name="predict_peptide")
 
 if __name__ == "__main__":
-    iface.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch()
+