app.py

import torch
from transformers import AutoProcessor, AutoModel, VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
from PIL import Image
import streamlit as st

# Load the saved model state dictionary
model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')))

# Load the necessary components
feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")

# Function to generate a caption for an image
@st.cache_resource
def generate_caption(image):
    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
    output_ids = model.generate(pixel_values, max_length=100, num_beams=5, early_stopping=True)
    caption = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
    return caption

# Streamlit app
def main():
    st.title("Image Captioning")
    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

    if uploaded_file is not None:
        image = Image.open(uploaded_file)
        st.image(image, caption="Uploaded Image", use_column_width=True)

        caption = generate_caption(image)
        st.write(f"Caption: {caption}")

if __name__ == "__main__":
    main()