import streamlit as st
from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image

# Cache model & processor to avoid reloading every time
@st.cache_resource
def load_model():
    processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
    model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
    return processor, model

processor, model = load_model()

# Streamlit App
st.set_page_config(page_title="🖼️ Image Caption Generator", page_icon="🖼️", layout="centered")

st.title("🖼️ Image Caption Generator")
st.write("Upload an image and get a descriptive caption generated by AI.")

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

if uploaded_file:
    image = Image.open(uploaded_file).convert('RGB')
    st.image(image, caption='Uploaded Image', use_column_width=True)

    if st.button("Generate Caption"):
        with st.spinner("Generating caption..."):
            inputs = processor(image, return_tensors="pt")
            out = model.generate(**inputs)
            caption = processor.decode(out[0], skip_special_tokens=True)
            st.subheader("📋 Generated Caption:")
            st.write(f"**{caption}**")