Update app.py

app.py

@@ -2,53 +2,32 @@ import streamlit as st
 from transformers import pipeline
 
 # Load the sentiment analysis model pipeline
-classifier = pipeline("text-classification",model='Ryleeeee/CustomSentimentModel', return_all_scores=True)
+sentiment_classifier = pipeline("text-classification",model='Ryleeeee/CustomSentimentModel', return_all_scores=True)
 
 # Streamlit application title and background image
 st.image("./header.png", use_column_width=True)
-st.title("Step 1: Sentiment Analysis")
+st.header("Customer Review Analysis")
 
 st.write("Setiment classification: positive, netural, negative")
 
 # User can enter the customer review
-text = st.text_area("Enter the customer review", "")
+review = st.text_area("Enter the customer review", "")
 
-# Perform sentiment analysis when the user clicks the "Classify sentiment" button
-if st.button("Classify sentiment"):
-    # Perform sentiment analysis on the input text
-    results = classifier(text)[0]
-
-    # Display the classification result
+def sentiment_class(text):
+    results = sentiment_classifier(text)[0]
     max_score = float('-inf')
     max_label = ''
-
     for result in results:
         if result['score'] > max_score:
             max_score = result['score']
             max_label = result['label']
+    return max_score, max_label
 
-    st.write("This review sentiment is:", max_label)
-    st.write("Accuracy rate is:", max_score)
-
-
-if max_lable == "negative":
-    # Streamlit application title
-    st.title("Product categories of negative review")
-    st.write("Product classification of this negative review: smartTv, books, mobile, mobile accessories and refrigerators")
-
-    # Perform product classification analysis when the user clicks the "Classify product" button
-    if st.button("Classify product"):
-        # Perform product classification analysis on the input text
-        results_1 = classifier(text)[0]
-
-        # Display the classification result
-        max_score_1 = float('-inf')
-        max_label_1 = ''
-
-        for result_1 in results_1:
-            if result_1['score_1'] > max_score_1:
-                max_score_1 = result_1['score_1']
-                max_label_1 = result_1['label_1']
+    
+# Perform sentiment analysis when the user clicks the "Classify Sentiment" button
+if review is not None and st.button("Classify Sentiment"):
+    # Perform sentiment analysis on the input text
+    sentiment_result = sentiment_class(review)
+    st.write("This review sentiment is ", sentiment_result[1])
+    st.write("Prediction score is ", sentiment_result[0])
 
-        st.write("This negative review belongs to:", max_label_1)
-        st.write("Accuracy rate is:", max_score_1)