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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()
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