# -*- coding: utf-8 -*-
"""
Created on Fri Nov 25 21:37:33 2022

@author: Bharathraj C L
"""


import streamlit as st
import mmcv
import os
import numpy as np
from PIL import Image
from mmdet.apis import init_detector, inference_detector, show_result_pyplot
from pathlib import Path
st.set_option('deprecation.showPyplotGlobalUse', False)

st.title("Table Detection from Images")


@st.cache
def load_model():
    # Make sure to pass `pretrained` as `True` to use the pretrained weights:
    #new_model = tf.keras.models.load_model('mobilenetv2_100noise.h5')
    config_file = 'cascade_mask_rcnn_hrnetv2p_w32_20e.py'
    checkpoint_file = 'epoch_36.pth'
    model = init_detector(config_file, checkpoint_file, device='cuda:0')
    return new_model

def main():
    uploaded_file = st.file_uploader("Choose an image...", type="jpg")

    model = load_model()
    if uploaded_file is not None:
        image = Image.open(uploaded_file)
        st.image(image, caption='Uploaded Image.', use_column_width=True)
        directory = "tempDir"
        path = os.path.join(os.getcwd(), directory)
        p = Path(path)
        if not p.exists():
            os.mkdir(p)
        with open(os.path.join(path, uploaded_file.name),"wb") as f:
            f.write(uploaded_file.getbuffer()) 
        file_loc = os.path.join(path, uploaded_file.name)
        result = inference_detector(model, file_loc)
        st.pyplot(show_result_pyplot(file_loc, result,('Bordered', 'cell', 'Borderless'), score_thr=0.85))



if __name__ == '__main__':
    main()