| import streamlit as st | |
| from transformers import ViTImageProcessor, ViTForImageClassification | |
| from PIL import Image as img | |
| x = st.file_uploader("Upload Images", type=["png","jpg","jpeg"]) | |
| if x is not None: | |
| st.image(img.open(x),width=255) | |
| i = img.open(x) | |
| processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224') | |
| model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') | |
| inputs = processor(images=i, return_tensors="pt") | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| predicted_class_idx = logits.argmax(-1).item() | |
| st.text("Our Model Predicts : ") | |
| st.write(model.config.id2label[predicted_class_idx]) |