Update app.py

app.py

@@ -7,10 +7,16 @@ from collections import Counter
 
 # --- Download model from HF Hub ---
 repo_id = "Ym420/Peptide-Function"  # replace with your HF repo
-model_filename = "xgb_multilabel_model_full_extendedFuture.pkl"
+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)
+
+# --- Debug: Check what we loaded ---
+print("Loaded model_package type:", type(model_package))
+print("model_package keys:", list(model_package.keys()))
+print("Type of model:", type(model_package['model']))
+
 model = model_package['model']
 feature_columns = model_package['feature_columns']
 
@@ -34,12 +40,12 @@ TARGET_CELLS = ["Gram+", "Fungus", "Mammalian Cell", "Cancer", "Gram-"]
 def extract_features_app(seq: str) -> pd.DataFrame:
     seq = seq.upper()
     
-    # --- 1. Dipeptide composition ---
+    # 1. Dipeptide composition
     count = Counter([seq[i:i+2] for i in range(len(seq)-1)])
     total = max(len(seq)-1, 1)
     dipep_features = [count.get(dp, 0) / total for dp in dipeptides]
 
-    # --- 2. Physicochemical features ---
+    # 2. Physicochemical features
     def g(aa, table): return table.get(aa, 0)
     def h(dp, table): return (g(dp[0], table) + g(dp[1], table)) / 2.0
 
@@ -77,18 +83,20 @@ def predict_peptide(sequence: str):
         return []
 
     X = extract_features_app(seq)
-    
-    #probs_list = model.predict_proba(X)  # list of arrays per target
-    # --- probs_list: list of arrays from each estimator ---
-    probs_list = [est.predict_proba(X) for est in model]  # model is a list
+
+    # --- Handle model being a list of estimators ---
+    if isinstance(model, list):
+        probs_list = [est.predict_proba(X) for est in model]
+    else:
+        probs_list = [model.predict_proba(X)]  # single model
 
     table = []
     for i, target in enumerate(TARGET_CELLS):
         prob = float(probs_list[i][0][1])
         table.append([target, round(prob, 4)])
+    
     return table
 
-
 # --- Gradio Interface ---
 custom_css = """
 footer, .footer {display:none !important;}
@@ -112,11 +120,9 @@ with gr.Blocks(css=custom_css, theme="default") as demo:
     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__": 
-#    demo.launch()
+    # Optional API endpoint for iOS app
+    # Note: use only if Gradio version supports `api`
+    # gr.api(predict_peptide, api_name="predict_peptide")
 
 if __name__ == "__main__": 
     demo.launch(show_error=True)