Update app.py

app.py

@@ -19,21 +19,24 @@ def predict_xray(image):
     image = image.reshape(1, 150, 150, 3) / 255.0  # Normalization (if used in training)
     
     # Make prediction
-    prediction = model.predict(image)
-    prediction = prediction.argmax()  # Get class with highest probability
+    prediction = model.predict(image)[0]  # Get probabilities for both classes
     
-    # Class labels (adjust based on your dataset)
-    labels = ["Normal", "Pneumonia"]
+    # Class labels
+    labels = ["The Patient is Normal.", "The Patient has Pneumonia."]
     
-    return labels[prediction]
+    # Get predicted class and confidence scores
+    predicted_class = np.argmax(prediction)  # Class with highest probability
+    confidence = prediction[predicted_class] * 100  # Convert to percentage
+    
+    return f"{labels[predicted_class]} ({confidence:.2f}% confidence)"
 
 # Create Gradio UI
 iface = gr.Interface(
     fn=predict_xray,
     inputs=gr.Image(type="pil"),  # Accepts image input
-    outputs="text",  # Returns class label
+    outputs="text",  # Returns class label with confidence
     title="Pneumonia Detection",
-    description="Upload a chest X-ray image, and the model will predict if the patient has pneumonia or is normal."
+    description="Upload a chest X-ray image, and the model will predict if the patient has pneumonia or is normal, along with confidence scores."
 )
 
 # Launch the app