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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_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, 'Selected Image', use_column_width=True) | |
# For newline | |
st.sidebar.write('\n') | |
with st.spinner('Generating image caption ...'): | |
caption = predict_dummy(image) | |
image.close() | |
st.success(f'caption: {caption}') | |
st.sidebar.header("ViT-GPT2 predicts:") | |
st.sidebar.write(f"caption: {caption}", '\n') | |