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app.py

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+import gradio as gr
+import tensorflow as tf
+import gdown
+from PIL import Image
+
+import os
+import cv2
+import numpy as np
+import keras.backend as K
+
+
+#from tensorflow import keras
+
+input_shape = (32, 32, 3)
+resized_shape = (768, 768, 3)
+IMG_SCALING = (1, 1)
+
+
+# Download the model file
+def download_model():
+    url = "https://drive.google.com/uc?id=1FhICkeGn6GcNXWTDn1s83ctC-6Mo1UXk"
+    output = "seg_unet_model.h5"
+    gdown.download(url, output, quiet=False)
+    return output
+
+model_file = download_model()
+
+#Custom objects for model
+
+def Combo_loss(y_true, y_pred, eps=1e-9, smooth=1):
+    targets = tf.dtypes.cast(K.flatten(y_true), tf.float32)
+    inputs = tf.dtypes.cast(K.flatten(y_pred), tf.float32)
+    
+    intersection = K.sum(targets * inputs)
+    dice = (2. * intersection + smooth) / (K.sum(targets) + K.sum(inputs) + smooth)
+    inputs = K.clip(inputs, eps, 1.0 - eps)
+    out = - (ALPHA * ((targets * K.log(inputs)) + ((1 - ALPHA) * (1.0 - targets) * K.log(1.0 - inputs))))
+    weighted_ce = K.mean(out, axis=-1)
+    combo = (CE_RATIO * weighted_ce) - ((1 - CE_RATIO) * dice)
+    return combo
+
+def dice_coef(y_true, y_pred, smooth=1):
+    y_pred = tf.dtypes.cast(y_pred, tf.int32)
+    y_true = tf.dtypes.cast(y_true, tf.int32)
+    intersection = K.sum(y_true * y_pred, axis=[1,2,3])                     
+    union = K.sum(y_true, axis=[1,2,3]) + K.sum(y_pred, axis=[1,2,3])           
+    return K.mean((2 * intersection + smooth) / (union + smooth), axis=0)
+
+# 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")
+# outputs = gr.outputs.HTML() #uncomment for single class output 
+
+def gen_pred(img=inputs, model=seg_model):
+    #rgb_path = os.path.join(test_image_dir,img)
+    #img = cv2.imread(rgb_path)
+    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 inputs, pred
+
+
+title = "<h1 style='text-align: center;'>Semantic Segmentation</h1>"
+description = "Upload an image and get prediction mask"
+# css_code='body{background-image:url("file=wave.mp4");}'
+
+gr.Interface(fn=gen_pred, 
+             inputs=inputs, 
+             outputs="image", 
+             title=title, 
+             examples=[["003e2c95d.jpg"], ["003b50a15.jpg"], ["003b48a9e.jpg"], ["0038cbe45.jpg"], ["00371aa92.jpg"]],
+             # css=css_code,
+             description=description,
+            enable_queue=True).launch()

requirements.txt

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+tensorflow
+gdown
+gradio
+os
+pillow