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import pandas as pd
import streamlit as st
from streamlit_text_rating.st_text_rater import st_text_rater
from sentiment import classify_sentiment
from sentiment_onnx_classify import classify_sentiment_onnx, classify_sentiment_onnx_quant
from zeroshot_clf import zero_shot_classification
import time
st.set_page_config( # Alternate names: setup_page, page, layout
layout="wide", # Can be "centered" or "wide". In the future also "dashboard", etc.
initial_sidebar_state="auto", # Can be "auto", "expanded", "collapsed"
page_title='None', # String or None. Strings get appended with "• Streamlit".
)
padding_top = 0
st.markdown(f"""
<style>
.reportview-container .main .block-container{{
padding-top: {padding_top}rem;
}}
</style>""",
unsafe_allow_html=True,
)
def set_page_title(title):
st.sidebar.markdown(unsafe_allow_html=True, body=f"""
<iframe height=0 srcdoc="<script>
const title = window.parent.document.querySelector('title') \
const oldObserver = window.parent.titleObserver
if (oldObserver) {{
oldObserver.disconnect()
}} \
const newObserver = new MutationObserver(function(mutations) {{
const target = mutations[0].target
if (target.text !== '{title}') {{
target.text = '{title}'
}}
}}) \
newObserver.observe(title, {{ childList: true }})
window.parent.titleObserver = newObserver \
title.text = '{title}'
</script>" />
""")
set_page_title('NLP use cases')
# Hide Menu Option
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
st.title("NLP use cases")
with st.sidebar:
st.title("NLP tasks")
select_task=st.selectbox(label="Select task from drop down menu",
options=['README',
'Detect Sentiment','Zero Shot Classification'])
if select_task=='README':
st.header("NLP Summary")
if select_task=='Detect Sentiment':
st.header("You are now performing Sentiment Analysis")
input_texts = st.text_input(label="Input texts separated by comma")
c1,c2,c3=st.columns(3)
with c1:
response1=st.button("Normal runtime")
with c2:
response2=st.button("ONNX runtime")
with c3:
response3=st.button("ONNX runtime with Quantization")
if any([response1,response2,response3]):
if response1:
start=time.time()
sentiments = classify_sentiment(input_texts)
end=time.time()
st.write(f"Time taken for computation {(end-start)*1000:.1f} ms")
elif response2:
start = time.time()
sentiments=classify_sentiment_onnx(input_texts)
end = time.time()
st.write(f"Time taken for computation {(end - start) * 1000:.1f} ms")
elif response3:
start = time.time()
sentiments=classify_sentiment_onnx_quant(input_texts)
end = time.time()
st.write(f"Time taken for computation {(end - start) * 1000:.1f} ms")
else:
pass
for i,t in enumerate(input_texts.split(',')):
if sentiments[i]=='Positive':
response=st_text_rater(t + f"--> This statement is {sentiments[i]}",
color_background='rgb(154,205,50)',key=t)
else:
response = st_text_rater(t + f"--> This statement is {sentiments[i]}",
color_background='rgb(233, 116, 81)',key=t)
if select_task=='Zero Shot Classification':
st.header("You are now performing Zero Shot Classification")
input_texts = st.text_input(label="Input text to classify into topics")
input_lables = st.text_input(label="Enter labels separated by commas")
response = st.button("Calculate")
if response:
output=zero_shot_classification(input_texts, input_lables)
config = {'displayModeBar': False}
st.plotly_chart(output,config=config)
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