Update api.py

api.py

@@ -304,30 +304,6 @@ async def classify(image: UploadFile = File(...)):
         return {"error": "No image provided"}
 
 
-def make_gradcam_heatmap_Palm(img_array, model, base_model_name, last_conv_layer_name, pred_index=None):
-    base_model = model.get_layer(base_model_name)
-    last_conv_layer = base_model.get_layer(last_conv_layer_name)
-
-    grad_model = tf.keras.models.Model(
-        inputs=[model.inputs],
-        outputs=[last_conv_layer.output, model.output]
-    )
-
-    with tf.GradientTape() as tape:
-        conv_outputs, predictions = grad_model(img_array)
-        if pred_index is None:
-            pred_index = tf.argmax(predictions[0])
-        loss = predictions[:, pred_index]
-
-    grads = tape.gradient(loss, conv_outputs)
-    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
-    conv_outputs = conv_outputs[0]
-    heatmap = conv_outputs @ pooled_grads[..., tf.newaxis]
-    heatmap = tf.squeeze(heatmap)
-    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
-    return heatmap.numpy()
-
-
 @app.post("/palmclassify")
 async def palmclassify(image: UploadFile = File(...)):
     if image is not None:
@@ -340,14 +316,7 @@ async def palmclassify(image: UploadFile = File(...)):
         predicted_class_idx = int(predicted_class_idx)
 
         last_mb = "Conv_1"
-        img_array = tf.convert_to_tensor(img_array, dtype=tf.float32)
-        # heatmap = make_gradcam_heatmap(img_array, modelPalm, last_mb)
-        heatmap = make_gradcam_heatmap_Palm(
-            img_array,
-            modelPalm,
-            base_model_name='mobilenetv2_1.00_224',
-            last_conv_layer_name='Conv_1'
-        )
+        heatmap = make_gradcam_heatmap(img_array, modelPalm, last_mb)
 
         base64_image = display_gradcam( np.array(img), heatmap)