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import streamlit as st
from PIL import Image
import numpy as np
# Designing the interface
st.title("French Image Caption App")
# For newline
st.write('\n')
st.markdown(
"""
An image caption model by combining the ViT model and a French 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 pretained weights of both models are loaded, with a set of randomly initialized cross-attention weigths.
The model is trained on 65000 images from the COCO dataset for about 1500 steps, with the original english cpationis are translated to french for training purpose.
"""
)
#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 the Model",
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, use_column_width=True)
show.image(image, '\n\nSelected Image', use_column_width=True)
# For newline
st.sidebar.write('\n')
with st.spinner('Generating image caption ...'):
caption = predict(image)
image.close()
# st.success(f'{caption}')
st.header(f'{caption}')
st.sidebar.header("ViT-GPT2 predicts:")
st.sidebar.write(f"{caption}", '\n')
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