Update app.py

app.py

@@ -2,13 +2,13 @@ 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)
+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", use_column_width=True)
 
-st.write("Sentiment classification: positive, neutral, negative")
+st.write("Setiment classification: positive, netural, negative")
 
 # User can enter the customer review
 text = st.text_area("Enter the customer review", "")
@@ -30,27 +30,25 @@ if st.button("Classify sentiment"):
     st.write("This review sentiment is:", max_label)
     st.write("Accuracy rate is:", max_score)
 
-    if max_label == "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")
-
-        # Load the product classification model pipeline
-        product_classifier = pipeline("text-classification", model='model_name', return_all_scores=True)
-
-        # 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 = product_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'] > max_score_1:
-                    max_score_1 = result_1['score'] 
-                    max_label_1 = result_1['label'] 
-            
-            st.write("This negative review belongs to:", max_label_1) 
-            st.write("Accuracy rate is:", max_score_1)
+
+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']
+
+        st.write("This negative review belongs to:", max_label_1)
+        st.write("Accuracy rate is:", max_score_1)