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| ##importing the libraries | |
| import numpy as np | |
| import pandas as pd | |
| from PIL import Image | |
| import tensorflow as tf | |
| import os | |
| from tensorflow.keras.models import load_model | |
| import gradio as gr | |
| # Load your trained model | |
| model = load_model('tb_pretrained.h5') | |
| ### Preprocess the new image | |
| def predict_image(test_image): | |
| # img = cv2.imread(test_image) | |
| img = np.array(test_image) | |
| image_1 = tf.image.resize(img, (256,256)) | |
| image_processed = np.expand_dims(image_1/256, 0) | |
| ##prediction | |
| yhat = model.predict(image_processed) | |
| ## setting a threshold | |
| if yhat[0][1] > 0.70: | |
| return (f'There is {round((yhat[0][1])*100,2)}% chance of the image being normal') | |
| elif yhat[0][0] > 0.9: | |
| return (f'There is {round((yhat[0][0])*100,2)}% chance of an abnormality either than TB being present') | |
| else: | |
| return (f'There is a chance of TB being present') | |
| platform = gr.Interface( fn = predict_image, | |
| title ="TB CADx", | |
| inputs = "image", | |
| outputs = "label", | |
| description="""This is a computer aided detection tool that helps | |
| clinicians quickly classify chest X-ray images into either normal, | |
| unhealthy but no TB or High chance of TB""", | |
| article = """This tool is for research and by all means not meant to replace | |
| the WHO recommended guidelines on diagnosing TB""" ) | |
| platform.launch(inline=True, share=True) |