Update app file

app.py

@@ -1,14 +1,16 @@
+import pandas as pd
+import numpy as np
 import streamlit as st
 from PIL import Image
 import random
 import sahi.utils.file
-import pandas as pd
-import numpy as np
+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'
+    'processing_pid.png' : 'https://d1afc1j4569hs1.cloudfront.net/processing-pid.png',
+    'prediction_visual.png' : 'https://d1afc1j4569hs1.cloudfront.net/prediction_visual.png'
     }
 
 st.set_page_config(
@@ -21,6 +23,12 @@ 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.')
 
+if "output_1" not in st.session_state:
+    st.session_state["output_1"] = sahi.utils.cv.read_image_as_pil(IMAGE_TO_URL['plant_pid.png'])
+
+if "output_2" not in st.session_state:
+    st.session_state["output_2"] = sahi.utils.cv.read_image_as_pil(IMAGE_TO_URL['prediction_visual.png'])
+
 col1, col2, col3 = st.columns(3, gap='medium')
 with col1:
     with st.expander('How to use it'):
@@ -85,3 +93,18 @@ st.write('##')
 col1, col2, col3 = st.columns([3, 1, 3])
 with col2:
     submit = st.button("🚀 Perform Prediction")
+
+st.write('##')
+
+st.markdown(f"##### Uploaded Image vs Model Prediction:")
+static_component = image_comparison(
+    img1=st.session_state["output_1"],
+    img2=st.session_state["output_2"],
+    label1='Uploaded Diagram',
+    label2='Model Inference',
+    width=700,
+    starting_position=50,
+    show_labels=True,
+    make_responsive=True,
+    in_memory=True,
+)