| 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() |