| import gradio as gr |
| from fastai.vision.all import * |
| import skimage |
|
|
| learn = load_learner('export 1.pkl') |
|
|
| labels = learn.dls.vocab |
| def predict(img): |
| img = PILImage.create(img) |
| pred,pred_idx,probs = learn.predict(img) |
| prediction = str(pred) |
| |
| return prediction |
|
|
|
|
| title = "Lung cancer detection with Deep Transfer Learning(ResNet152 model)" |
| description = "<p style='text-align: center'><b>As a radiologist or oncologist, it is crucial to know what is wrong with a lung CT image.<b><br><b>Upload the breast X-ray image to know what is wrong with a patients breast with or without inplant<b><p>" |
| article="<p style='text-align: center'>Web app is built and managed by Mr.<b></p>" |
| examples = ['img 1.png', 'img 2.png'] |
| enable_queue=True |
| |
|
|
| gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,enable_queue=enable_queue).launch() |
|
|
