Create app.py

app.py

@@ -0,0 +1,16 @@
+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])