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import gradio as gr | |
import numpy as np | |
import tensorflow as tf | |
from tensorflow.keras.preprocessing import image | |
model = tf.keras.models.load_model('model.hdf5') | |
def predict(img_path): | |
test_image = image.load_img(img_path, target_size=(224,224)) | |
test_image = image.img_to_array(test_image) | |
test_image = test_image/255.0 | |
test_image = np.expand_dims(test_image, axis = 0) | |
prediction = model.predict(test_image) | |
result = np.argmax(prediction, axis=1) | |
if result[0] == 0: | |
prediction = 'COVID DETECTED' | |
elif result[0] == 1: | |
prediction = 'HEALTHY' | |
elif result[0] == 2: | |
prediction = 'LUNG CANCER DETECTED' | |
else: | |
prediction = 'PNEUMONIA DETECTED' | |
return prediction | |
iface = gr.Interface(fn=predict, inputs="text", outputs="text") | |
iface.launch(share=True) |