| import h2o | |
| import gradio as gr | |
| from PIL import Image | |
| import numpy as np | |
| import pandas as pd | |
| h2o.init() | |
| model = h2o.load_model('DeepLearning_grid_1_AutoML_11_20230827_202719_model_3') | |
| column_names = model._model_json['output']['names'][:-1] | |
| label_mapping = { | |
| 'p0': 'choroidal neovascularization', | |
| 'p1': 'diabetic macular edema', | |
| 'p2': 'drusen', | |
| 'p3': 'normal' | |
| } | |
| def predict(image): | |
| desired_size = (28, 28) | |
| resized_image = image.resize(desired_size) | |
| gray_image = resized_image.convert("L") | |
| image_array = np.asarray(gray_image) | |
| image_sample_flatten = image_array.flatten() | |
| print(image_sample_flatten.shape) | |
| df_sample = pd.DataFrame([image_sample_flatten], columns=column_names) | |
| image_sampleh2o = h2o.H2OFrame(df_sample) | |
| prediction = model.predict(image_sampleh2o) | |
| pandas_df = prediction.as_data_frame() | |
| prediction_dict = pandas_df.to_dict(orient="list") | |
| del prediction_dict['predict'] | |
| new_dict = {label_mapping[key]: value for key, value in prediction_dict.items()} | |
| return(new_dict) | |
| examples=[ | |
| ['choroidal neovascularization.png'], | |
| ['diabetic macular edema.png'], | |
| ['drusen.png'], | |
| ['normal.png'], | |
| ] | |
| demo = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), examples=examples, outputs="label", title="Retinal OCT Medical Image Classification", description='Retinal OCT Image Classification app using OCTMNIST dataset', article='Dataset rights: Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni. Yang, Jiancheng, et al. "MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification." Scientific Data, 2023.') | |
| demo.launch() | |