| import zipfile | |
| import os | |
| import gradio as gr | |
| import numpy as np | |
| from keras.models import load_model | |
| from PIL import Image | |
| from keras.utils import img_to_array | |
| from keras.applications.resnet import preprocess_input | |
| zip_path = 'resnet101_model.zip' | |
| extract_dir = 'resnet101_model' | |
| if not os.path.isdir(extract_dir): | |
| with zipfile.ZipFile(zip_path, 'r') as zip_ref: | |
| zip_ref.extractall(extract_dir) | |
| model = load_model(os.path.join(extract_dir, 'resnet101_model')) | |
| class_names = ['cloudy', 'desert', 'green_area', 'water'] | |
| def predict_image(file_path): | |
| if file_path is None: | |
| return "Please upload a satellite image for prediction" | |
| with open(file_path, "rb") as f: | |
| imageUploadedByUser = Image.open(f) | |
| imageUploadedByUser = imageUploadedByUser.resize((224, 224)) | |
| if imageUploadedByUser.mode != 'RGB': | |
| imageUploadedByUser = imageUploadedByUser.convert('RGB') | |
| image_to_arr = img_to_array(imageUploadedByUser) | |
| image_to_arr_preprocess_input = preprocess_input(image_to_arr) | |
| image_to_arr_preprocess_input_expand_dims = np.expand_dims( | |
| image_to_arr_preprocess_input, axis=0) | |
| prediction = model.predict( | |
| image_to_arr_preprocess_input_expand_dims)[0] | |
| prediction_argmax = np.argmax(prediction) | |
| prediction_final_result = class_names[prediction_argmax] | |
| return f'The predicted satellite image is {prediction_final_result}.' | |
| custom_css = """ | |
| .desc { text-align: center; } | |
| """ | |
| interfaceOfGradio = gr.Interface( | |
| fn=predict_image, | |
| inputs=gr.File(type="filepath", | |
| label="Upload Satellite Image", file_count="single"), | |
| outputs="text", | |
| description="<p class='desc'>" + | |
| "Upload a satellite image on the left and see the prediction result on the right." + "</p>", | |
| css=custom_css | |
| ) | |
| interfaceOfGradio.launch() | |