aftersix
commited on
Commit
•
1cc6ea3
1
Parent(s):
b802ec0
adding presidio
Browse files
app.py
CHANGED
@@ -2,6 +2,9 @@ import streamlit as st
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import streamlit as st
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from st_aggrid import AgGrid
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import pandas as pd
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#resource list to display after the assessment is complete
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resourceList = pd.read_csv('resources.csv')
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@@ -23,6 +26,11 @@ with open('auditorytext.csv', newline='') as csvfile:
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#set page config
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st.set_page_config(page_title='auditory skills resources', page_icon='icon-128x128.png')
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#define session variables
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if 'one' not in st.session_state:
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st.session_state['one'] = 'value'
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@@ -108,6 +116,9 @@ def main():
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submitted = st.form_submit_button("Get Results")
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if submitted:
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#load everything
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from transformers import pipeline
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#define classifier for zero shot classification
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@@ -119,7 +130,13 @@ def main():
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while x < 35:
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if details[x] != "none":
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st.markdown(questions[x])
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-
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sequence = details[x]
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print(details[x])
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output = classifier(sequence, sequence_labels)
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import streamlit as st
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from st_aggrid import AgGrid
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import pandas as pd
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#for the PII masking
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#resource list to display after the assessment is complete
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resourceList = pd.read_csv('resources.csv')
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#set page config
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st.set_page_config(page_title='auditory skills resources', page_icon='icon-128x128.png')
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#masking set up
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# Call analyzer to get results
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#define session variables
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if 'one' not in st.session_state:
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st.session_state['one'] = 'value'
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submitted = st.form_submit_button("Get Results")
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if submitted:
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#load everything
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from presidio_analyzer import AnalyzerEngine
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from presidio_anonymizer import AnonymizerEngine
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analyzer = AnalyzerEngine()
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from transformers import pipeline
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#define classifier for zero shot classification
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while x < 35:
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if details[x] != "none":
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st.markdown(questions[x])
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text=details[x]
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results = analyzer.analyze(text=text,
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language='en')
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anonymizer = AnonymizerEngine()
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anonymized_text = anonymizer.anonymize(text=text,analyzer_results=results)
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maskedText = anonymized_text.text
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st.markdown("**"+maskedText+"**")
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sequence = details[x]
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print(details[x])
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output = classifier(sequence, sequence_labels)
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