app.py

from transformers import pipeline
import streamlit as st
from PIL import Image

# Initialize the classifier pipeline
classifier = pipeline("image-classification")

# Streamlit interface
st.title("Image Classification with Hugging Face")

# Upload image
uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png"])

if uploaded_image:
    try:
        # Open the image as a PIL object
        image = Image.open(uploaded_image).convert("RGB")  # Convert to RGB to handle all formats

        # Display the uploaded image
        st.image(image, caption="Uploaded Image", use_column_width=True)

        # Pass the PIL image directly to the classifier
        results = classifier(image)

        # Display the results
        st.write("Classification Results:")
        for result in results:
            st.write(f"Label: {result['label']}, Score: {result['score']:.4f}")

    except Exception as e:
        st.error(f"Error processing the image: {e}")