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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')
#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 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 1",
"sample 2",
"sample 3",
"sample 4"
)
)
sample_name = f'sample_{sample_name.split()[-1].zfill(2)}.jpg'
sample_path = f'samples/{sample_name}'
image = Image.open(sample_path)
show = st.image(image, use_column_width=True)
show.image(image, 'Uploaded Image', use_column_width=True)
# For newline
st.sidebar.write('\n')
# if st.sidebar.button("Click here to get image caption"):
with st.spinner('Generating image caption ...'):
caption, tokens, token_ids = predict(image)
st.success(f'caption: {caption}')
st.success(f'tokens: {tokens}')
st.success(f'token ids: {token_ids}')
st.sidebar.header("ViT-GPT2 predicts:")
st.sidebar.write(f"caption: {caption}", '\n')
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