Create app.py

app.py

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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()
+  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",
+                                       "NORMAL-0.jpeg",
+                                       "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()