app.py

import streamlit as st
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import torch

# Load the model and feature extractor
model_name = "google/vit-base-patch16-224"
model = ViTForImageClassification.from_pretrained(model_name)
feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)

# Streamlit app
st.title("Image Classifier")
st.write("Upload an image to classify it into categories.")

# File uploader
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_file:
    # Load and display the image
    image = Image.open(uploaded_file).convert("RGB")
    st.image(image, caption="Uploaded Image", use_column_width=True)

    # Preprocess the image
    inputs = feature_extractor(images=image, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        predicted_class_idx = logits.argmax(-1).item()
    
    # Get classification label
    label = model.config.id2label[predicted_class_idx]
    
    # Display results
    st.write(f"Prediction: **{label}**")