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import streamlit as st
from transformers import pipeline
import plotly.express as px
import pandas as pd
st.set_page_config(layout="wide")
@st.cache(allow_output_mutation = True)
def get_classifier_model():
return pipeline("zero-shot-classification", model="models/bart-large-mnli")
#return pipeline("zero-shot-classification",model="sentence-transformers/paraphrase-MiniLM-L6-v2")
#st.sidebar.image("Suncorp-Bank-logo.png",width=255)
st.image("Suncorp-Bank-logo.png",width=255)
st.title("Detecting Barriers from Conversations")
st.markdown("***")
text = st.text_area(label="Enter text to classify")
st.markdown("***")
col1, col2, col3 = st.columns((1,1,1))
col1.header("Select Sentiments")
sentiments = col1.multiselect("",["Happy","Sad","Anxious","Depressed","Empathetic"],["Happy","Sad","Anxious","Depressed","Empathetic"])
col2.header("Select Entities")
entities = col2.multiselect("",["Employee","Doctor","Family","Friends"],
["Employee","Doctor","Family","Friends"])
col3.header("Select Reasons")
reasons = col3.multiselect("",["Bullying","Alchohol","Abuse","Domestic_Violence",'Chronic_Pain','Driving','Hobbies','Treatment'],
["Bullying","Alchohol","Abuse","Domestic_Violence",'Chronic_Pain','Driving','Hobbies','Treatment'])
is_multi_class = st.checkbox("Can have more than one classes",value=True)
st.markdown("***")
classify_button_clicked = st.button("Classify")
def get_classification(candidate_labels):
classification_output = classifier(sequence_to_classify, candidate_labels, multi_class=is_multi_class)
data = {'Class': classification_output['labels'], 'Scores': classification_output['scores']}
df = pd.DataFrame(data)
df = df.sort_values(by='Scores', ascending=False)
fig = px.bar(df, x='Scores', y='Class', orientation='h', width=800, height=800)
fig.update_layout(
yaxis=dict(
autorange='reversed'
)
)
return fig
if classify_button_clicked:
if text:
st.markdown("***")
with st.spinner(" Please wait while the text is being classified.."):
classifier = get_classifier_model()
sequence_to_classify = text
# candidate_labels = sentiments + entities + reasons
if sentiments:
#print(classification_output)
fig = get_classification(sentiments)
# col5, col6= st.columns((1, 1))
col1.write(fig)
if entities:
#print(classification_output)
fig = get_classification(entities)
# col7, col8= st.columns((1, 1))
col2.write(fig)
if reasons:
#print(classification_output)
fig = get_classification(reasons)
# col7, col8= st.columns((1, 1))
col3.write(fig) |