app.py

import gradio as gr
import tensorflow as tf
import os
import numpy as np

model=tf.keras.models.load_model('model.h5')

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 Anomaly Detection", css="") as demo:
    with gr.Row():
        textmd = gr.Markdown('''
    # Chest X-Ray Anomaly Detection
    ''')
    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(573).jpg",
                                       "NORMAL2-IM-1345-0001-0002.jpeg",
                                       "person1946_bacteria_4875.jpeg",
                                       "Tuberculosis-658.png"], 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()