Daniel Cerda Escobar commited on
Commit
bd7c529
Β·
1 Parent(s): b574fc0

Update app file

Browse files
Files changed (1) hide show
  1. app.py +26 -3
app.py CHANGED
@@ -1,14 +1,16 @@
 
 
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  import streamlit as st
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  from PIL import Image
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  import random
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  import sahi.utils.file
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- import pandas as pd
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- import numpy as np
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  IMAGE_TO_URL = {
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  'factory_pid.png' : 'https://d1afc1j4569hs1.cloudfront.net/factory-pid.png',
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  'plant_pid.png' : 'https://d1afc1j4569hs1.cloudfront.net/plant-pid.png',
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- 'processing_pid.png' : 'https://d1afc1j4569hs1.cloudfront.net/processing-pid.png'
 
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  }
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  st.set_page_config(
@@ -21,6 +23,12 @@ st.title('P&ID Object Detection')
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  st.subheader(' Identify valves and pumps with deep learning model ', divider='rainbow')
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  st.caption('Developed by Deep Drawings Co.')
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  col1, col2, col3 = st.columns(3, gap='medium')
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  with col1:
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  with st.expander('How to use it'):
@@ -85,3 +93,18 @@ st.write('##')
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  col1, col2, col3 = st.columns([3, 1, 3])
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  with col2:
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  submit = st.button("πŸš€ Perform Prediction")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import pandas as pd
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+ import numpy as np
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  import streamlit as st
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  from PIL import Image
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  import random
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  import sahi.utils.file
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+ from streamlit_image_comparison import image_comparison
 
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  IMAGE_TO_URL = {
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  'factory_pid.png' : 'https://d1afc1j4569hs1.cloudfront.net/factory-pid.png',
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  'plant_pid.png' : 'https://d1afc1j4569hs1.cloudfront.net/plant-pid.png',
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+ 'processing_pid.png' : 'https://d1afc1j4569hs1.cloudfront.net/processing-pid.png',
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+ 'prediction_visual.png' : 'https://d1afc1j4569hs1.cloudfront.net/prediction_visual.png'
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  }
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  st.set_page_config(
 
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  st.subheader(' Identify valves and pumps with deep learning model ', divider='rainbow')
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  st.caption('Developed by Deep Drawings Co.')
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+ if "output_1" not in st.session_state:
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+ st.session_state["output_1"] = sahi.utils.cv.read_image_as_pil(IMAGE_TO_URL['plant_pid.png'])
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+
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+ if "output_2" not in st.session_state:
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+ st.session_state["output_2"] = sahi.utils.cv.read_image_as_pil(IMAGE_TO_URL['prediction_visual.png'])
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+
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  col1, col2, col3 = st.columns(3, gap='medium')
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  with col1:
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  with st.expander('How to use it'):
 
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  col1, col2, col3 = st.columns([3, 1, 3])
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  with col2:
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  submit = st.button("πŸš€ Perform Prediction")
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+
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+ st.write('##')
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+
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+ st.markdown(f"##### Uploaded Image vs Model Prediction:")
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+ static_component = image_comparison(
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+ img1=st.session_state["output_1"],
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+ img2=st.session_state["output_2"],
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+ label1='Uploaded Diagram',
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+ label2='Model Inference',
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+ width=700,
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+ starting_position=50,
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+ show_labels=True,
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+ make_responsive=True,
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+ in_memory=True,
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+ )