Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import tensorflow as tf
 import gdown
+import urllib.request
 from PIL import Image
 
 import os
@@ -14,6 +15,9 @@ import keras.backend as K
 resized_shape = (768, 768, 3)
 IMG_SCALING = (1, 1)
 
+def get_opencv_img_from_buffer(buffer, flags=cv2.IMREAD_COLOR):
+    bytes_as_np_array = np.frombuffer(buffer.read(), dtype=np.uint8)
+    return cv2.imdecode(bytes_as_np_array, flags)
 
 # Download the model file
 def download_model():
@@ -49,15 +53,11 @@ def dice_coef(y_true, y_pred, smooth=1):
 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 = get_opencv_img_from_buffer(inputs)
 image_output = gr.outputs.Image(type="pil", label="Output Image")
 # outputs = gr.outputs.HTML() #uncomment for single class output 
 
 def gen_pred(img=inputs, model=seg_model):
-
-    img = cv2.imread('./003e2c95d.jpg')
-    path = os.getcwd()
-    tmp = os.listdir(path)
-    print(tmp)
     #rgb_path = os.path.join(test_image_dir,img)
     #img = cv2.imread(rgb_path)
     img = img[::IMG_SCALING[0], ::IMG_SCALING[1]]