Create App.py

App.py

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+import os
+import cv2
+import random
+import numpy as np 
+from PIL import Image 
+os.environ["SM_FRAMEWORK"] = "tf.keras"
+import segmentation_models as sm
+from matplotlib import pyplot as plt
+
+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 = [0.1666, 0.1666, 0.1666, 0.1666, 0.1666, 0.1666]
+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}))
+
+
+def process_input_image(image_source):
+    # Convert the numpy array to a PIL Image object
+    image = Image.fromarray(np.uint8(image_source))
+    
+    # Resize the image
+    image = image.resize((256, 256))
+    
+    # Convert the image back to a numpy array
+    image = np.array(image)
+    
+    # Expand the dimensions of the image to match the expected input shape of the model
+    image = np.expand_dims(image, axis=0)
+    
+    # Predict the mask for the image
+    prediction = satellite_model.predict(image)
+    predicted_image = np.argmax(prediction, axis=3)
+
+    predicted_image = predicted_image[0,:,:]
+    predicted_image = predicted_image * 50
+    
+    return 'Predicted Masked Image', predicted_image
+
+my_app = gr.Blocks()
+
+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
+        ]
+    )
+
+my_app.launch(debug=True)