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import gradio as gr
import tensorflow as tf
import os
import numpy as np
model = tf.keras.models.load_model('model.hdf5')
LABELS = ['NORMAL', 'TUBERCULOSIS', 'PNEUMONIA', 'COVID19']
def predict_input_image(img):
img_4d=img.reshape(-1,128,128,3)/255.0
print(img_4d.min())
print(img_4d.max())
prediction=model.predict(img_4d)[0]
return {LABELS[i]: float(prediction[i]) for i in range(4)}
def k():
return gr.update(value=None)
with gr.Blocks(title="Chest X-Ray Disease Classification", css="") as demo:
with gr.Row():
textmd = gr.Markdown('''
# Chest X-Ray Disease Classification
View the full training code at <a href="https://www.kaggle.com/code/mushfirat/chest-x-ray-disease-classification"><b>kaggle</b></a>
''')
with gr.Row():
with gr.Column(scale=1, min_width=600):
image = gr.inputs.Image(shape=(128,128))
with gr.Row():
clear_btn = gr.Button("Clear")
submit_btn = gr.Button("Submit", elem_id="warningk", variant='primary')
examples = gr.Examples(examples=["COVID19-0.jpg",
"COVID19-1.jpg",
"PNEUMONIA-0.jpeg"], inputs=image)
label = gr.outputs.Label(num_top_classes=4)
clear_btn.click(k, inputs=[], outputs=image)
submit_btn.click(predict_input_image, inputs=image, outputs=label)
demo.launch() |