app.py

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

from streamlit_image_comparison import image_comparison

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',
    'prediction_visual.png' : 'https://d1afc1j4569hs1.cloudfront.net/prediction_visual.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.')

@st.cache_data(show_spinner=False)
def download_comparison_images():
    sahi.utils.file.download_from_url(
        'https://d1afc1j4569hs1.cloudfront.net/plant-pid.png',
        'plant_pid.png',
    )
    sahi.utils.file.download_from_url(
        'https://d1afc1j4569hs1.cloudfront.net/prediction_visual.png',
        'prediction_visual.png',
    )

download_comparison_images()

if "output_1" not in st.session_state:
    st.session_state["output_1"] = Image.open('plant_pid.png')

if "output_2" not in st.session_state:
    st.session_state["output_2"] = Image.open('prediction_visual.png')

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('##')
   
col1, col2, col3 = st.columns(3, gap='large')
with col1:
    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 the following examples',
        options = ['factory_pid.png', 'plant_pid.png', 'processing_pid.png'],
        format_func = radio_func,
    )
with col2:
    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)
        
with col3:
    st.markdown('##### Set model parameters')
    postprocess_match_threshold = st.slider(
        label = 'Select confidence threshold',
        min_value = 0.0,
        max_value = 1.0,
        value = 0.75,
        step = 0.25
    )
    postprocess_match_metric = st.slider(
        label = 'Select IoU threshold',
        min_value = 0.0,
        max_value = 1.0,
        value = 0.75,
        step = 0.25
    )

st.write('##')

col1, col2, col3 = st.columns([3, 1, 3])
with col2:
    submit = st.button("🚀 Perform Prediction")

st.write('##')

col1, col2, col3 = st.columns([1, 3, 1])
with col2:
    st.markdown(f"#### Object Detection Result")
    with st.container(border = True):
        static_component = image_comparison(
            img1=st.session_state["output_1"],
            img2=st.session_state["output_2"],
            label1='Uploaded Diagram',
            label2='Model Inference',
            width=1000,
            starting_position=50,
            show_labels=True,
            make_responsive=True,
            in_memory=True,
        )