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| import streamlit as st | |
| # Designing the interface | |
| st.title("🖼️ Image Captioning Demo 📝") | |
| st.write("[Yih-Dar SHIEH](https://huggingface.co/ydshieh)") | |
| st.sidebar.markdown( | |
| """ | |
| An image captioning model [ViT-GPT2](https://huggingface.co/flax-community/vit-gpt2) by combining the ViT model with the GPT2 model. | |
| [Part of the [Huggingface JAX/Flax event](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/).]\n | |
| The encoder (ViT) and decoder (GPT2) are combined using Hugging Face transformers' `FlaxVisionEncoderDecoderModel`. | |
| The pretrained weights of both models are loaded, with a set of randomly initialized cross-attention weights. | |
| The model is trained on the COCO 2017 dataset for about 6900 steps (batch_size=256). | |
| """ | |
| ) | |
| #image = Image.open('samples/val_000000039769.jpg') | |
| #show = st.image(image, use_column_width=True) | |
| #show.image(image, 'Preloaded Image', use_column_width=True) | |
| with st.spinner('Loading and compiling ViT-GPT2 model ...'): | |
| from model import * | |
| # st.sidebar.write(f'Vit-GPT2 model loaded :)') | |
| st.sidebar.title("Select a sample image") | |
| sample_name = st.sidebar.selectbox( | |
| "Please choose an image", | |
| sample_fns | |
| ) | |
| sample_name = f"COCO_val2014_{sample_name.replace('.jpg', '').zfill(12)}.jpg" | |
| sample_path = os.path.join(sample_dir, sample_name) | |
| image = Image.open(sample_path) | |
| show = st.image(image, width=480) | |
| show.image(image, '\n\nSelected Image', width=480) | |
| # For newline | |
| st.sidebar.write('\n') | |
| with st.spinner('Generating image caption ...'): | |
| caption = predict(image) | |
| caption_en = caption | |
| st.header(f'**Prediction (in English)**: {caption_en}') | |
| # caption_en = translator.translate(caption, src='fr', dest='en').text | |
| # st.header(f'**Prediction (in French) **{caption}') | |
| # st.header(f'**English Translation**: {caption_en}') | |
| st.sidebar.header("ViT-GPT2 predicts:") | |
| st.sidebar.write(f"**English**: {caption}") | |
| image.close() | |