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app.py

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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)