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import streamlit as st
from transformers import AutoProcessor, VisionEncoderDecoderModel
import requests
from PIL import Image
import torch
# Load processor and model
st.title("Image to Text Captioning App")
st.write("This app converts an image into a text description using the ViT-GPT2 model.")
@st.cache_resource
def load_model():
processor = AutoProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
return processor, model
processor, model = load_model()
# Upload image
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
image = Image.open(uploaded_file).convert("RGB")
st.image(image, caption="Uploaded Image", use_column_width=True)
# Preprocessing image
pixel_values = processor(images=image, return_tensors="pt").pixel_values
# Generate caption
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
st.write("Generated Caption: ")
st.success(generated_text)
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