app.py

import streamlit as st
from transformers import VisionEncoderDecoderModel, ViTImageProcessor, GPT2Tokenizer
import torch
from PIL import Image

# Load the model and tokenizer
model = VisionEncoderDecoderModel.from_pretrained("ashok2216/vit-gpt2-image-captioning_COCO_FineTuned")
processor = ViTImageProcessor.from_pretrained("ashok2216/vit-gpt2-image-captioning_COCO_FineTuned")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Streamlit app title
st.title("Image Captioning with ViT-GPT2 Model")
st.write("Upload an image, and the model will generate a descriptive caption.")

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

if uploaded_file is not None:
    # Load and display the uploaded image
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Image", use_column_width=True)
    
    # Preprocess the image for the model
    inputs = processor(images=image, return_tensors="pt")
    pixel_values = inputs.pixel_values
    
    # Generate the caption
    with st.spinner("Generating caption..."):
        output = model.generate(pixel_values)
        caption = tokenizer.decode(output[0], skip_special_tokens=True)
    
    # Display the generated caption
    st.success("Generated Caption:")
    st.write(caption)