app.py

import gradio as gr
from transformers import AutoModelForImageClassification, pipeline, AutoImageProcessor
from torchvision import transforms

model = AutoModelForImageClassification.from_pretrained("Nicole-M/Dataset1-SwinV2")
image_processor = AutoImageProcessor.from_pretrained("Nicole-M/Dataset1-SwinV2")
clf = pipeline(model=model, task="image-classification", image_processor=image_processor)

class_names = ['Benign', 'Malignant']

def predict_image(img):
  img = transforms.ToPILImage()(img)
  img = transforms.Resize((224,224))(img)
  prediction=clf.predict(img)
  return {class_names[i]: float(prediction[i]["score"]) for i in range(2)}

image = gr.Image(label="Select a mammogram image", sources=['upload'])
label = gr.Label(num_top_classes=2)

gr.Interface(fn=predict_image, inputs=image, outputs=label, title="Mammogram classification").launch()