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app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+import PIL
+import tensorflow as tf
+from tensorflow import keras
+from keras import layers
+from keras.models import Sequential
+from keras.preprocessing.image import ImageDataGenerator
+import pathlib
+from keras.models import Model
+from PIL import Image
+
+model = keras.models.load_model('model.h5')
+class_names = ['sea', 'glacier']
+
+def predict_image(img):
+  img_4d=img.reshape(1,224,224,3)
+  prediction=model.predict(img_4d)[0]
+  return {class_names[i]: float(prediction[i]) for i in range(2)}
+
+image = gr.inputs.Image(shape=(224,224))
+label = gr.outputs.Label(num_top_classes=2)
+
+gr.Interface(css=None,
+             fn=predict_image,
+             inputs=image,
+             description="Please upload a Chest X-Ray image in JPG, JPEG or PNG.",
+             title='Identifying Adenoid by Convolutional neural network',outputs=label).launch(share=None)
+
+gr.launch()