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import streamlit as st | |
from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer | |
from PIL import Image | |
# Load the pre-trained model and processor | |
model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
processor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
# Streamlit app title | |
st.title("Image to Text App") | |
# File uploader | |
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) | |
if uploaded_file is not None: | |
# Load and display the image | |
image = Image.open(uploaded_file) | |
st.image(image, caption='Uploaded Image', use_column_width=True) | |
# Process the image | |
pixel_values = processor(images=image, return_tensors="pt").pixel_values | |
# Generate text | |
output_ids = model.generate(pixel_values) | |
text = tokenizer.decode(output_ids[0], skip_special_tokens=True) | |
# Display the generated text | |
st.write("Generated Text:") | |
st.write(text) | |