app.py

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

# Load the BLIP model
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")

# Streamlit app
st.title("Image Captioning with BLIP")

# Uploading the image
uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_image is not None:
    image = Image.open(uploaded_image).convert('RGB')
    st.image(image, caption='Uploaded Image', use_column_width=True)

    # Perform conditional image captioning
    captioning_mode = st.selectbox("Captioning Mode", ["Conditional", "Unconditional"])
    if captioning_mode == "Conditional":
        text = st.text_input("Provide a condition for the captioning (e.g., 'a photo of', 'an illustration of'): ", "a photo of")
        if text:  # Only proceed if the user has provided a text
            inputs = processor(image, text, return_tensors="pt")
            out = model.generate(**inputs)
            caption = processor.decode(out[0], skip_special_tokens=True)
            st.write(f"Generated Caption: {caption}")
    else:  # Unconditional captioning
        inputs = processor(image, return_tensors="pt")
        out = model.generate(**inputs)
        caption = processor.decode(out[0], skip_special_tokens=True)
        st.write(f"Generated Caption: {caption}")