First commit of app.py

This is the hosted inference engine for the fine-tuned ViT model on approximately 35k images of Mammograms from the Mammogram V1 dataset.

app.py

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+import gradio as gr
+from transformers import AutoModelForImageClassification, Trainer, ViTImageProcessor, ViTForImageClassification, pipeline, AutoImageProcessor
+from torchvision import transforms
+
+model = AutoModelForImageClassification.from_pretrained("Nicole-M/Dataset1-ViT")
+image_processor = AutoImageProcessor.from_pretrained("Nicole-M/Dataset1-ViT")
+clf = pipeline(model=model, task="image-classification", image_processor=image_processor)
+
+class_names = ['Benign', 'Malignant']
+
+def predict_image(img):
+  img=img.reshape(224,224,3)
+  img = transforms.ToPILImage()(img)
+  prediction=clf.predict(img)
+  return {class_names[i]: float(prediction[i]["score"]) for i in range(2)}
+
+image = gr.inputs.Image(shape=(224,224))
+label = gr.outputs.Label(num_top_classes=2)
+
+gr.Interface(fn=predict_image, inputs=image, outputs=label,interpretation='default').launch()