Update app.py

app.py

@@ -5,10 +5,12 @@ os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
 import numpy as np
 import gradio as gr
 import joblib
-import spaces
+import spaces  # Still needed for the dummy function
 from tensorflow.keras.models import load_model
 
-# Load models and saved objects
+# ============================================================
+# LOAD MODELS & SCALERS
+# ============================================================
 knn_model = joblib.load("knn_model.pkl")
 svm_model = joblib.load("rbf_svm_model.pkl")
 ann_model = load_model("ann_model.keras")
@@ -16,7 +18,9 @@ ann_model = load_model("ann_model.keras")
 scaler_rfe = joblib.load("scaler_rfe.pkl")
 selected_features = joblib.load("selected_features.pkl")
 
-# Encoding mapping definitions
+# ============================================================
+# ENCODING MAPS
+# ============================================================
 gender_map = {"Female": 0, "Male": 1}
 binary_map = {"No": 0, "Yes": 1}
 ordinal_map = {"Low": 0, "Medium": 1, "High": 2}
@@ -32,31 +36,50 @@ def encode_input_value(feature, value):
     if feature == 'alcohol': return alcohol_map[value]
     return float(value)
 
+# ============================================================
+# ZERO-GPU WORKAROUND
+# ============================================================
+# This satisfies Hugging Face's ZeroGPU requirement without 
+# crashing your actual Scikit-Learn/TensorFlow models.
 @spaces.GPU
-def predict_heart_disease(model_choice, *feature_values):
-    encoded_inputs = [encode_input_value(f, v) for f, v in zip(selected_features, feature_values)]
-    X_input = np.array([encoded_inputs], dtype=float)
-    X_scaled = scaler_rfe.transform(X_input)
-
-    if model_choice == "KNN":
-        y_pred = knn_model.predict(X_scaled)[0]
-        y_prob = knn_model.predict_proba(X_scaled)[0][1]
-    elif model_choice == "SVM":
-        y_pred = svm_model.predict(X_scaled)[0]
-        if hasattr(svm_model, "predict_proba"):
-            y_prob = svm_model.predict_proba(X_scaled)[0][1]
-        else:
-            score = svm_model.decision_function(X_scaled)[0]
-            y_prob = 1 / (1 + np.exp(-score))
-    else:
-        y_prob = float(ann_model.predict(X_scaled, verbose=0)[0][0])
-        y_pred = int(y_prob >= 0.5)
-
-    status = "Yes" if int(y_pred) == 1 else "No"
-    probability_text = f"{y_prob:.4f} ({y_prob * 100:.2f}%)"
-
-    return status, probability_text, model_choice
+def dummy_startup():
+    pass
 
+# ============================================================
+# PREDICTION FUNCTION (NO GPU DECORATOR)
+# ============================================================
+def predict_heart_disease(model_choice, *feature_values):
+    try:
+        encoded_inputs = [encode_input_value(f, v) for f, v in zip(selected_features, feature_values)]
+        X_input = np.array([encoded_inputs], dtype=float)
+        X_scaled = scaler_rfe.transform(X_input)
+
+        if model_choice == "KNN":
+            y_pred = knn_model.predict(X_scaled)[0]
+            y_prob = knn_model.predict_proba(X_scaled)[0][1]
+        elif model_choice == "SVM":
+            y_pred = svm_model.predict(X_scaled)[0]
+            if hasattr(svm_model, "predict_proba"):
+                y_prob = svm_model.predict_proba(X_scaled)[0][1]
+            else:
+                score = svm_model.decision_function(X_scaled)[0]
+                y_prob = 1 / (1 + np.exp(-score))
+        else: # ANN
+            y_prob = float(ann_model.predict(X_scaled, verbose=0)[0][0])
+            y_pred = int(y_prob >= 0.5)
+
+        status = "Yes" if int(y_pred) == 1 else "No"
+        probability_text = f"{y_prob:.4f} ({y_prob * 100:.2f}%)"
+
+        return status, probability_text, model_choice
+        
+    except Exception as e:
+        # If an error happens, this will print it directly to the Gradio UI
+        return f"Error: {str(e)}", "Error", model_choice
+
+# ============================================================
+# UI DEFINITION
+# ============================================================
 feature_labels = {
     'age': 'Age', 'gender': 'Gender', 'blood_pressure': 'Blood Pressure',
     'cholesterol': 'Cholesterol Level', 'exercise': 'Exercise Habits',