| import gradio as gr | |
| from fastai.vision.all import * | |
| import skimage | |
| learn = load_learner('export.pkl') | |
| labels = learn.dls.vocab | |
| def predict(img): | |
| img = PILImage.create(img) | |
| pred, pred_idx, probs = learn.predict(img) | |
| return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
| title = "Breast cancer detection with Deep Transfer Learning(ResNet18)." | |
| description = "<p style='text-align: center'><b>As a radiologist or oncologist, it is crucial to know what is wrong with a breast x-ray image.</b><br><b>Upload the breast X-ray image to know what is wrong with a patient's breast with or without implant. This product is from the findings of my (Team) published research paper: <a href='https://iopscience.iop.org/article/10.1088/2057-1976/ad3cdf' target='_blank' style='color: blue;'>read paper</a>. Learn more about me: <a href='https://www.linkedin.com/in/fosberg-addai-53a6991a7/' target='_blank' style='color: blue;'>Fosberg Addai</a></b></p>" | |
| article = "<p style='text-align: center'><b>Web app is built and managed by Addai Fosberg</b></p>" | |
| examples = ['img1.jpeg', 'img2.jpeg'] | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(shape=(512, 512)), | |
| outputs=gr.Label(num_top_classes=3), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| enable_queue=True | |
| ) | |
| iface.launch() |