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# import shutil
# import cv2
# from PIL import Image
# from collections import deque, Counter
import time, os, json, onnx, onnxruntime
# import torch
import pandas as pd
import streamlit as st
import requests
from utils import *
import args
from streamlit_lottie import st_lottie
st.set_page_config(
page_title=args.PAGE_TITLE,
page_icon=args.PAGE_ICON, layout=args.LAYOUT, initial_sidebar_state='auto'
)
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
# Configure
options = onnxruntime.SessionOptions()
options.intra_op_num_threads = 8
options.inter_op_num_threads = 8
lottie_penguin = load_lottieurl('https://assets10.lottiefiles.com/datafiles/Yv8B88Go8kHRZ5T/data.json')
st_lottie(lottie_penguin, height=200)
hide_streamlit_style = """
<style>
footer {
visibility: hidden;
}
footer:after {
content:'© 2021 Vu Minh Chien';
visibility: visible;
display: block;
position: relative;
#background-color: red;
padding: 5px;
top: 2px;
}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
st.write(args.LANDINGPAGE_TITLE)
st.sidebar.title(args.SIDEBAR_TITLE)
method = st.sidebar.radio('Choose input source 👇', options=['Image'])
# Load model
@st.cache(suppress_st_warning=False)
def initial_setup():
df_train = pd.read_csv('full_set.csv')
sub_test_list = sorted(list(df_train['Image'].map(lambda x: get_image(x))))
with open('embeddings.npy', 'rb') as f:
embeddings = np.load(f)
PATH = 'model_onnx.onnx'
ort_session = onnxruntime.InferenceSession(PATH, sess_options=options)
input_name = ort_session.get_inputs()[0].name
return df_train, sub_test_list, embeddings, ort_session, input_name
df_train, sub_test_list, embeddings, ort_session, input_name = initial_setup()
if method == 'Image':
st.sidebar.markdown('---')
st.sidebar.header('Options')
content_file, col2 = show_original()
image_input(
content_file, df_train, sub_test_list, embeddings, ort_session, input_name, col2
)
else:
webcam_input(
df_train, sub_test_list, embeddings, ort_session, input_name
)
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