Update app.py

app.py

@@ -6,62 +6,38 @@ import segmentation_models as sm
 from matplotlib import pyplot as plt
 import random
 
-
 from keras import backend as K
 from keras.models import load_model
 
 import gradio as gr
 
 def jaccard_coef(y_true, y_pred):
-  y_true_flatten = K.flatten(y_true)
-  y_pred_flatten = K.flatten(y_pred)
-  intersection = K.sum(y_true_flatten * y_pred_flatten)
-  final_coef_value = (intersection + 1.0) / (K.sum(y_true_flatten) + K.sum(y_pred_flatten) - intersection + 1.0)
-  return final_coef_value    
-    
-
-weights = [0.2,0.2,0.2,0.2,0.2]
-dice_loss = sm.losses.DiceLoss(class_weights = weights)
+    y_true_flatten = K.flatten(y_true)
+    y_pred_flatten = K.flatten(y_pred)
+    intersection = K.sum(y_true_flatten * y_pred_flatten)
+    final_coef_value = (intersection + 1.0) / (K.sum(y_true_flatten) + K.sum(y_pred_flatten) - intersection + 1.0)
+    return final_coef_value    
+
+weights = [0.2, 0.2, 0.2, 0.2, 0.2]
+dice_loss = sm.losses.DiceLoss(class_weights=weights)
 focal_loss = sm.losses.CategoricalFocalLoss()
 total_loss = dice_loss + (1 * focal_loss)
 
-
-satellite_model = load_model('model/satellite_segmentation_full.h5',custom_objects=({'dice_loss_plus_1focal_loss': total_loss,'jaccard_coef': jaccard_coef}))
+satellite_model = load_model('model/satellite_segmentation_full.h5', custom_objects={'dice_loss_plus_1focal_loss': total_loss, 'jaccard_coef': jaccard_coef})
 
 def process_input_image(image_source):
-  image = np.expand_dims(image_source, 0)
-
-  prediction = satellite_model.predict(image)
-  predicted_colored = np.argmax(prediction, axis=3)
-
-  predicted_colored = predicted_colored[0,:,:]
-  predicted_colored = predicted_colored * 50
-  return 'Predicted Masked Image', predicted_colored
-
-my_app = gr.Blocks()
+    image = np.expand_dims(image_source, 0)
 
+    prediction = satellite_model.predict(image)
+    predicted_colored = np.argmax(prediction, axis=3)
 
-with my_app:
-  gr.Markdown("Statellite Image Segmentation Application UI with Gradio")
-  with gr.Tabs():
-    with gr.TabItem ("Select your image"):
-      with gr.Row():
-        with gr.Column():
-            img_source = gr.Image(label="Please select source Image", shape=(256, 256))
-            source_image_loader = gr.Button("Load above Image")
-        with gr.Column():
-            output_label = gr.Label(label="Image Info")
-            img_output = gr.Image(label="Image Output")
-    source_image_loader.click(
-        process_input_image,
-        [
-            img_source
-        ],
-        [
-            output_label,
-            img_output
-        ]
-    )
+    predicted_colored = predicted_colored[0,:,:]
+    predicted_colored = predicted_colored * 50
+    return 'Predicted Masked Image', predicted_colored
 
+my_app = gr.Interface(fn=process_input_image, 
+                      inputs=gr.inputs.Image(label="Please select the source image", shape=(256, 256)), 
+                      outputs="image",
+                      title="Satellite Image Segmentation Application UI with Gradio")
 
-my_app.launch(debug=True)
+my_app.launch()