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import streamlit as st |
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from transformers import pipeline |
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import plotly.express as px |
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import pandas as pd |
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st.set_page_config(layout="wide") |
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@st.cache(allow_output_mutation = True) |
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def get_classifier_model(): |
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return pipeline("zero-shot-classification", model="typeform/distilbert-base-uncased-mnli") |
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st.title("Review Analyzer") |
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st.markdown("***") |
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text = st.text_area(label="Paste/Type the review here..") |
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st.markdown("***") |
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col1, col2, col3 = st.columns((1,1,1)) |
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col1.header("Select Sentiments") |
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sentiments = col1.multiselect("",["Happy","Sad","Neutral"],["Happy","Sad","Neutral"]) |
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col1.markdown(" \n") |
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col1.markdown(" \n") |
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additional_sentiments = col1.text_input("Enter comma separated sentiments.") |
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if additional_sentiments: |
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sentiments = sentiments + additional_sentiments.split(",") |
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col2.header("Select Topics") |
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entities = col2.multiselect("",["Bank Account","Credit Card","Home Loan","Motor Loan"], |
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["Bank Account","Credit Card","Home Loan","Motor Loan"]) |
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additional_entities= col2.text_input("Enter comma separated entities.") |
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if additional_entities: |
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entities = entities + additional_entities.split(",") |
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col3.header("Select Reasons") |
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reasons = col3.multiselect("",["Poor Service","No Empathy","Abuse"], |
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["Poor Service","No Empathy","Abuse"]) |
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additional_reasons= col3.text_input("Enter comma separated reasons.") |
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if additional_reasons: |
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reasons = reasons + additional_reasons.split(",") |
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is_multi_class = st.checkbox("Can have more than one classes",value=True) |
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st.markdown("***") |
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classify_button_clicked = st.button("Classify") |
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def get_classification(candidate_labels): |
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classification_output = classifier(sequence_to_classify, candidate_labels, multi_class=is_multi_class) |
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data = {'Class': classification_output['labels'], 'Scores': classification_output['scores']} |
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df = pd.DataFrame(data) |
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df = df.sort_values(by='Scores', ascending=False) |
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fig = px.bar(df, x='Scores', y='Class', orientation='h', width=400, height=500) |
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fig.update_layout( |
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yaxis=dict( |
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autorange='reversed' |
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) |
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) |
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return fig |
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if classify_button_clicked: |
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if text: |
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st.markdown("***") |
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with st.spinner(" Please wait while the text is being classified.."): |
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classifier = get_classifier_model() |
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sequence_to_classify = text |
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if sentiments: |
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fig = get_classification(sentiments) |
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col1.markdown(" \n") |
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col1.write(fig) |
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if entities: |
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fig = get_classification(entities) |
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col2.write(fig) |
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if reasons: |
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fig = get_classification(reasons) |
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col3.write(fig) |
