app.py

import os
import streamlit as st
from PIL import Image

from inference import get_predictions


st.title('Handwriting Recognition Demo')

sample_files = os.listdir('./data/sample_images')
tot_index = len(sample_files)
sample_path = './data/sample_images'

if 'image_index' not in st.session_state:
    st.session_state['image_index'] = 4

if 'which_button' not in st.session_state:
    st.session_state['which_button'] = 'sample_button'

st.write('**Select from sample images**')

st.write("Select one from these available samples: ")
current_index = st.session_state['image_index']
current_image = Image.open(os.path.join(sample_path, sample_files[current_index]))

# next = st.button('next_image')
prev_button, next_button = st.columns(2)
with prev_button:
    prev = st.button('prev_image')
with next_button:
    next = st.button('next_image')
if prev:
    current_index = (current_index - 1) % tot_index
if next:
    current_index = (current_index + 1) % tot_index
st.session_state['image_index'] = current_index
sample_image = Image.open(os.path.join(sample_path, sample_files[current_index]))
st.image(sample_image, caption='Chosen image')

# use_sample_image = st.button("Use this Sample")
# if use_sample_image is True:
#     st.session_state['which_button'] = 'sample_button'

predict_clicked = st.button("Get prediction")
if predict_clicked:
    which_button = st.session_state['which_button']
    if which_button == 'sample_button':
        predictions = get_predictions(sample_image)
    st.markdown('**The model prediction is :**')
    st.write(predictions)