updated the UI for sentiment

app.py

@@ -2,7 +2,9 @@ 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.
@@ -63,15 +65,41 @@ st.title("NLP use cases")
 with st.sidebar:
     st.title("NLP tasks")
     select_task=st.selectbox(label="Select task from drop down menu",
-                 options=['Detect Sentiment','Zero Shot Classification'])
+                 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")
-    response=st.button("Calculate")
-    if response:
-        sentiments = classify_sentiment(input_texts)
+    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]}",