Update app.py

app.py

@@ -52,8 +52,7 @@ def dice_coef(y_true, y_pred, smooth=1):
 # Load the model
 seg_model = tf.keras.models.load_model('seg_unet_model.h5', custom_objects={'Combo_loss': Combo_loss, 'dice_coef': dice_coef})
 
-# inputs = gr.inputs.Image(type="pil", label="Upload an image", source="upload")
-inputs = Image.open("./003e2c95d.jpg")
+inputs = gr.inputs.Image(type="pil", label="Upload an image", source="upload")
 image_output = gr.outputs.Image(type="numpy", label="Output Image")
 # outputs = gr.outputs.HTML() #uncomment for single class output 
 
@@ -61,13 +60,17 @@ def gen_pred(img=inputs, model=seg_model):
     # rgb_path = os.path.join(test_image_dir,img)
     # img = cv2.imread(rgb_path)
     # img = cv2.imread("./003e2c95d.jpg")
+    pil_image = PIL.Image.open('./003b50a15.jpg')
+    img = cv2.cvtColor(numpy.array(pil_image), cv2.COLOR_RGB2BGR)
     img = img[::IMG_SCALING[0], ::IMG_SCALING[1]]
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     img = img/255
     img = tf.expand_dims(img, axis=0)
     pred = model.predict(img)
     pred = np.squeeze(pred, axis=0)
-    return "IN PROGRESS..."
+    color_coverted = cv2.cvtColor(pred, cv2.COLOR_BGR2RGB)
+    pil_image = Image.fromarray(color_coverted)
+    return pil_image
 
 
 title = "<h1 style='text-align: center;'>Semantic Segmentation</h1>"