Create app.py

app.py

@@ -0,0 +1,26 @@
+
+import random
+from keras.models import load_model
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+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
+    
+model = load_model('/content/drive/MyDrive/S_model.h5',custom_objects=({"jaccard_coef":jaccard_coef}))
+image = gr.inputs.Image(shape = (256,256))
+def  predict_image(image):
+    test_image_input = np.expand_dims(image, 0)
+    prediction = model.predict(test_image_input)
+    predicted_image = np.argmax(prediction, axis=3)
+    predicted_image = predicted_image[0,:,:]
+    fig = plt.figure()
+    plt.imshow(predicted_image)
+   
+    return fig
+
+gr.Interface(fn = predict_image,inputs = image, outputs=['plot'],interpretation = 'default').launch(share = True)