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Daniel Cerda Escobar
commited on
Commit
·
9af4bab
1
Parent(s):
1fc6f1f
Update app file
Browse files
app.py
CHANGED
@@ -21,7 +21,7 @@ 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='
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with col1:
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with st.expander('How to use it'):
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st.markdown(
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@@ -35,8 +35,8 @@ with col1:
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st.write('##')
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col1, col2, col3
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with
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st.markdown('##### Input File')
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# set input image by upload
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image_file = st.file_uploader("Upload your diagram", type=["pdf"])
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@@ -53,7 +53,7 @@ with col2:
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options = ['factory_pid.png', 'plant_pid.png', 'processing_pid.png'],
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format_func = radio_func,
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)
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with
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st.markdown('##### Preview')
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# visualize input image
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if image_file is not None:
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@@ -62,11 +62,8 @@ with col3:
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image = sahi.utils.cv.read_image_as_pil(IMAGE_TO_URL[radio])
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with st.container(border = True):
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st.image(image, use_column_width = True)
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-
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col1, col2, col3 = st.columns(3, gap='medium')
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with col2:
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st.markdown('#### Set model parameters')
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postprocess_match_threshold = st.slider(
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label = 'Select confidence threshold',
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@@ -74,4 +71,4 @@ with col2:
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max_value = 1.0,
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value = 0.8,
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step = 0.05
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)
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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='large')
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with col1:
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with st.expander('How to use it'):
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st.markdown(
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st.write('##')
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col1, col2, col3 = st.columns(3, gap='large')
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with col1:
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st.markdown('##### Input File')
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# set input image by upload
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image_file = st.file_uploader("Upload your diagram", type=["pdf"])
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options = ['factory_pid.png', 'plant_pid.png', 'processing_pid.png'],
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format_func = radio_func,
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)
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with col2:
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st.markdown('##### Preview')
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# visualize input image
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if image_file is not None:
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image = sahi.utils.cv.read_image_as_pil(IMAGE_TO_URL[radio])
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with st.container(border = True):
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st.image(image, use_column_width = True)
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with col3:
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st.markdown('#### Set model parameters')
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postprocess_match_threshold = st.slider(
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label = 'Select confidence threshold',
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max_value = 1.0,
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value = 0.8,
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step = 0.05
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)
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