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d26d4dc 8b557b8 b5b09f6 8b557b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | import gradio as gr
import numpy as np
from tensorflow.keras.models import load_model
from PIL import Image
def load_h5_model(model_path):
loaded_model = load_model(model_path)
return(loaded_model)
def prepro_img(img):
# resize the image to 130x130
img = img.resize((130,130))
# converto it to array with shape (1,130,130,3)
img_array = np.array(img)
img_array = np.array([img_array])
# return result
return(img_array)
def make_prediction(img):
# preprocess image
img = prepro_img(img)
# make prediction
prediction = model.predict(img)
prediction = int(prediction[0][0])
# return prediction label
if prediction == 1:
return('Uninfected cell')
else:
return('Parasitized cell')
model = load_h5_model('cell_classifier_model.h5')
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
cell_img = gr.Image(label="Cell Image",
type='pil')
examples = gr.Examples(['para_1.png', 'para_2.png', 'para_3.png'],
inputs=cell_img,
label='Parasitized Cells')
examples = gr.Examples(['uninf_1.png', 'uninf_2.png', 'uninf_3.png'],
inputs=cell_img,
label='Uninfected Cells')
with gr.Column():
cell_class = gr.Label(value='...')
predict_btn = gr.Button("Predict")
predict_btn.click(fn=make_prediction, inputs=cell_img, outputs=cell_class)
demo.launch(debug=True) |