Update app.py

app.py

@@ -3,59 +3,54 @@ from tensorflow import keras
 import numpy as np
 from PIL import Image
 import cv2
+import tensorflow as tf
 
-# Load model
 model = keras.models.load_model('my_model (2).h5')
 
-# Class labels
 CLASS_NAMES = ['Necrotic Tumor', 'Non-Tumor', 'Viable Tumor']
 
-def make_gradcam_heatmap(img_array, model, last_conv_layer_name="last_conv_layer"):
-    grad_model = keras.models.Model(
-        model.inputs,
-        [model.get_layer(last_conv_layer_name).output, model.output]
+def make_gradcam_heatmap(img_array, model):
+    last_conv_layer = model.get_layer('last_conv_layer')
+    grad_model = tf.keras.models.Model(
+        [model.inputs],
+        [last_conv_layer.output, model.output]
     )
     
-    with keras.backend.GradientTape() as tape:
+    with tf.GradientTape() as tape:
         conv_outputs, predictions = grad_model(img_array)
-        pred_index = np.argmax(predictions[0])
+        pred_index = tf.argmax(predictions[0])
         class_channel = predictions[:, pred_index]
     
     grads = tape.gradient(class_channel, conv_outputs)
-    pooled_grads = np.mean(grads, axis=(0, 1, 2))
+    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
     
     conv_outputs = conv_outputs[0]
-    heatmap = conv_outputs @ pooled_grads[..., np.newaxis]
-    heatmap = np.squeeze(heatmap)
-    heatmap = np.maximum(heatmap, 0) / (np.max(heatmap) + 1e-8)
+    heatmap = conv_outputs @ pooled_grads[..., tf.newaxis]
+    heatmap = tf.squeeze(heatmap)
+    heatmap = tf.maximum(heatmap, 0) / (tf.math.reduce_max(heatmap) + 1e-8)
     
-    return heatmap
+    return heatmap.numpy()
 
 def predict(input_image):
-    # Preprocess
     img = Image.fromarray(input_image).convert('RGB')
     img = img.resize((224, 224))
     img_array = np.array(img) / 255.0
     img_array = np.expand_dims(img_array, axis=0)
     
-    # Predict
     predictions = model.predict(img_array)
     pred_class = CLASS_NAMES[np.argmax(predictions[0])]
     confidence = float(np.max(predictions[0])) * 100
     
-    # Create result text
     result_text = f"Prediction: {pred_class}\nConfidence: {confidence:.2f}%\n\n"
     result_text += "All Probabilities:\n"
     for i, name in enumerate(CLASS_NAMES):
         result_text += f"  {name}: {predictions[0][i]*100:.2f}%\n"
     
-    # Generate Grad-CAM
     try:
         heatmap = make_gradcam_heatmap(img_array, model)
         heatmap = cv2.resize(heatmap, (224, 224))
         heatmap = np.uint8(255 * heatmap)
         heatmap_colored = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
-        
         original = np.array(img)
         superimposed = cv2.addWeighted(original, 0.6, heatmap_colored, 0.4, 0)
         output_image = superimposed
@@ -76,19 +71,4 @@ demo = gr.Interface(
     description="Upload an H&E stained histopathology image to classify as Non-Tumor, Viable Tumor, or Necrotic Tumor."
 )
 
-demo.launch()
-```
-
-4. Click **Commit new file to main**
-
----
-
-## Also update `requirements.txt`
-
-You need to add `opencv-python`. Edit your `requirements.txt` to:
-```
-tensorflow
-gradio
-numpy
-opencv-python-headless
-pillow
+demo.launch()