Update app.py

app.py

@@ -1,11 +1,16 @@
 import gradio as gr
 import numpy as np
-from tensorflow.keras.models import load_model  # ✅ switched to tf.keras
+from tensorflow.keras.models import load_model
 from PIL import Image
+import traceback
 
-
-# Load the trained model (no custom_objects needed if using built-in metrics only)
-model = load_model("pneumonia_cnn_model.h5")
+# Load the trained model
+try:
+    model = load_model("pneumonia_cnn_model.h5", compile=False)
+    print("✅ Model loaded successfully!")
+except Exception as e:
+    print("❌ Failed to load model:", e)
+    raise
 
 # Preprocessing function for uploaded X-ray images
 def preprocess_image(image):
@@ -13,7 +18,8 @@ def preprocess_image(image):
     image = image.resize((150, 150))  # Resize to model's expected input
     image_array = np.array(image) / 255.0  # Normalize pixel values
     image_array = np.expand_dims(image_array, axis=-1)  # Add channel dimension
-    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+    image_array = np.expand_dims(image_array, axis=0)   # Add batch dimension
+    print("📷 Preprocessed image shape:", image_array.shape)
     return image_array
 
 # Prediction function
@@ -21,11 +27,13 @@ def predict(image):
     try:
         image_array = preprocess_image(image)
         prediction = model.predict(image_array)[0][0]
-        label = "Pneumonia" if prediction > 0.5 else "Normal"
+        label = "🦠 Pneumonia" if prediction > 0.5 else "✅ Normal"
         confidence = prediction if prediction > 0.5 else 1 - prediction
         return f"{label} ({confidence * 100:.2f}% confidence)"
     except Exception as e:
-        return f"Error: {str(e)}"
+        traceback_str = traceback.format_exc()
+        print("❌ Prediction error:\n", traceback_str)
+        return f"Error during prediction: {str(e)}"
 
 # Gradio interface
 interface = gr.Interface(
@@ -38,5 +46,5 @@ interface = gr.Interface(
     allow_flagging="never"
 )
 
-# Important: Always call launch() outside of __main__ block on Hugging Face Spaces
+# Launch interface (for Hugging Face Spaces)
 interface.launch(debug=True)