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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=""" | |
| Introducing a revolutionary computer-aided detection tool designed to enhance the efficiency of clinicians in the classification of chest X-ray images. | |
| This innovative system facilitates the swift classification of images into three key categories: normal, indicating no abnormalities; | |
| unhealthy but not indicative of Tuberculosis (TB); and those with a high likelihood of TB presence. | |
| By streamlining the classification process, this tool aims to expedite diagnostic assessments and aid clinicians in making informed decisions regarding patient care. | |
| """, | |
| article = """ | |
| It is crucial to emphasize that while this tool serves as a valuable research aid, | |
| it is not intended to replace clinical guidelines, | |
| nor should it substitute for the wealth of clinical knowledge | |
| and experience possessed by healthcare professionals. | |
| The algorithm is meant to complement and support the diagnostic process, | |
| providing an additional layer of analysis for consideration in conjunction with the clinician's expertise. | |
| Users are encouraged to interpret the algorithm's output in conjunction with their clinical judgment, | |
| and the tool should be viewed as a supplementary resource rather than a standalone diagnostic solution. | |
| """ ) | |
| platform.launch(inline=True, share=True) |