app.py

import streamlit as st
from PIL import Image
import torch
from transformers import AutoProcessor, AutoModelForVision2Seq

# Load the PaliGemma model and processor
@st.cache_resource
def load_model():
    model_name = "google/paligemma2-3b-mix-224"
    processor = AutoProcessor.from_pretrained(model_name)
    model = AutoModelForVision2Seq.from_pretrained(model_name)
    return processor, model

processor, model = load_model()

# Streamlit UI
st.title("🖼️ Image Q&A using PaliGemma")

uploaded_file = st.file_uploader("Upload an Image", type=["png", "jpg", "jpeg"])

if uploaded_file:
    image = Image.open(uploaded_file).convert("RGB")
    st.image(image, caption="Uploaded Image", use_column_width=True)
    
    question = st.text_input("Ask a question about the image:")
    if question:
        # Process the image and question
        inputs = processor(text=question, images=image, return_tensors="pt")
        with torch.no_grad():
            output = model.generate(**inputs)
        
        answer = processor.batch_decode(output, skip_special_tokens=True)[0]
        st.success(f"Answer: {answer}")