Update app.py

app.py

@@ -10,25 +10,22 @@ model = tf.keras.models.load_model("Brain Tumors.h5")
 def img_pred(upload):
     # Convert the Gradio input image to OpenCV format
     opencvImage = cv2.cvtColor(np.array(upload), cv2.COLOR_RGB2BGR)
-    img = cv2.resize(opencvImage, (224, 224))
-    img = img.reshape(1, 224, 224, 3)
+    img = cv2.resize(opencvImage, (150, 150))
+    img = img.reshape(1, 150, 150, 3)
     
-    # Predict using the model
-    p = model.predict(img)
-    confidence = np.max(p)
-    p = np.argmax(p, axis=1)[0]
+    # Predict using the model and get confidence
+    predictions = model.predict(img)[0]  # Get probabilities for each class
+    predicted_class = np.argmax(predictions)  # Index of the predicted class
+    confidence = predictions[predicted_class]  # Confidence of the predicted class
     
-    # Map prediction to tumor type
-    if p == 0:
-        result = 'Glioma Tumor'
-    elif p == 1:
-        result = 'Meningioma Tumor'
-    elif p == 2:
-        result = 'No Tumor'
+    # Determine if tumor is present
+    if predicted_class == 0:  
+        result = "No Tumor"
     else:
-        result = 'Pituitary Tumor'
+        result = "Tumor Detected"
     
-    return f'The Model predicts: {result} with confidence {confidence}'
+    # Return result with confidence
+    return f"The Model predicts: {result} with a confidence of {confidence:.2%}"
 
 # Define Gradio interface
 iface = gr.Interface(