Create app.py

app.py

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+from keras.models import load_model
+from tensorflow.keras.utils import load_img, img_to_array 
+import numpy as np
+import cv2
+
+model = load_model('/little_imporved_model.h5')
+
+def predict_from_img(img):
+  img = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
+  img = img/255.0
+  img = np.expand_dims(img,axis = 0)
+  output = model.predict(img)[0][0]
+  return {'NORMAL':float(output),'PNEUMONIA':float(1-output)}
+
+import gradio as gr
+image = gr.inputs.Image(shape=(150,150))
+label = gr.outputs.Label(num_top_classes=2)
+gr.Interface(fn=predict_from_img, inputs=image, outputs=label).launch(share=True)