app.py

import streamlit as st
from PIL import Image
import random
import sahi.utils.file
import pandas as pd

IMAGE_TO_URL = {
    'factory_pid.png' : 'https://d1afc1j4569hs1.cloudfront.net/factory-pid.png',
    'plant_pid.png' : 'https://d1afc1j4569hs1.cloudfront.net/plant-pid.png',
    'processing_pid.png' : 'https://d1afc1j4569hs1.cloudfront.net/processing-pid.png'
    }

st.set_page_config(
    page_title="P&ID Object Detection",
    layout="wide",
    initial_sidebar_state="expanded"
    )

st.title('P&ID Object Detection')
st.subheader(' Identify valves and pumps with deep learning model ', divider='rainbow')
st.caption('Developed by Deep Drawings Co.')

col1, col2, col3 = st.columns(3, gap='medium')
with col1:
    with st.expander('How to use it'):
        st.markdown(
        '''
        1) Upload your P&ID or select example diagrams 📬
        2) Set confidence threshold 📈
        3) Press to perform inference 🚀
        4) Visualize model predictions 🔎
        '''
        )   

st.write('##')

with st.container(border = True):    
    col1, col2, col3, col4 = st.columns([1, 2, 2, 1], gap='large')
    with col2:
        st.markdown('##### Input File')
        # set input image by upload
        image_file = st.file_uploader("Upload your diagram", type=["pdf"])
        # set input images from examples
        def radio_func(option):
            option_to_id = {
                'factory_pid.png' : 'A',
                'plant_pid.png' : 'B',
                'processing_pid.png' : 'C',
            }
            return option_to_id[option]
        radio = st.radio(
            'Or select from example diagrams',
            options = ['factory_pid.png', 'plant_pid.png', 'processing_pid.png'],
            format_func = radio_func,
        )
    with col3:
        st.markdown('##### Preview')
        # visualize input image
        if image_file is not None:
            image = Image.open(image_file)
        else:
            image = sahi.utils.cv.read_image_as_pil(IMAGE_TO_URL[radio])
        with st.container(border = True):
            st.image(image, use_column_width = True)