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import gradio as gr
import tensorflow as tf
import numpy as np
import os
os.system("tar -zxvf examples.tar.gz")
model = tf.keras.models.load_model('model_cv.h5')
def detect(img):
img = img.reshape(1, 100, 100, 3)
prediction = np.around(model.predict(img)[0], decimals=0)[0]
if prediction == 1:
return "Pneumonia Detected!"
return "Pneumonia Not Detected!"
input = gr.inputs.Image(shape=(100, 100))
examples = ['examples/n1.jpeg', 'examples/n2.jpeg', 'examples/n3.jpeg', 'examples/n4.jpeg', 'examples/n5.jpeg',
'examples/n6.jpeg', 'examples/n7.jpeg', 'examples/n8.jpeg', 'examples/p6.jpeg', 'examples/p7.jpeg',
'examples/p1.jpeg', 'examples/p2.jpeg', 'examples/p3.jpeg', 'examples/p4.jpeg', 'examples/p8.jpeg']
title = "PneumoDetect: Pneumonia Detection from Chest X-Rays"
iface = gr.Interface(fn=detect, inputs=input, outputs="text", examples=examples, examples_per_page=20, title=title)
iface.launch(inline=False)
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