Update app.py

app.py

@@ -2,7 +2,7 @@ import streamlit as st
 from transformers import pipeline
 import nltk
 
-# Download NLTK data for sentence tokenization (updated to punkt_tab)
+# Download NLTK data for sentence tokenization
 nltk.download('punkt_tab')
 
 # Load the Hugging Face pipelines
@@ -12,6 +12,9 @@ sentiment_analyzer = pipeline("sentiment-analysis", model="SarahMakk/CustomModel
 # Define the categories for customer feedback
 CATEGORIES = ["Pricing", "Feature", "Customer Service", "Delivery", "Quality"]
 
+# Define the fixed confidence threshold
+CONFIDENCE_THRESHOLD = 0.8
+
 # Streamlit app UI
 st.title("Customer Feedback Categorization with Sentiment Analysis")
 st.markdown(
@@ -20,6 +23,7 @@ st.markdown(
     and determine the sentiment (positive or negative) for each relevant category.
     A single feedback may belong to multiple categories, such as Pricing, Feature, and Customer Service.
     The feedback is split into sentences, and each sentence is categorized and analyzed for sentiment.
+    Only categories with a confidence score >= 0.8 are displayed.
     """
 )
 
@@ -31,16 +35,6 @@ feedback_input = st.text_area(
     value="I was shocked to see the price tag on this new gadget—it’s way too expensive for what it offers, especially compared to competitors! Despite the issues I faced with my order, the customer service team's effort to rectify the situation was commendable, though their follow-up could have used some improvement for full satisfaction."
 )
 
-# Confidence threshold for zero-shot classification
-threshold = st.slider(
-    "Confidence Threshold",
-    min_value=0.0,
-    max_value=1.0,
-    value=0.2,
-    step=0.05,
-    help="Categories with scores above this threshold will be displayed."
-)
-
 # Classify button
 if st.button("Classify Feedback"):
     if not feedback_input.strip():
@@ -62,7 +56,7 @@ if st.button("Classify Feedback"):
 
             # Get categories with scores above the threshold
             for label, score in zip(classification_result["labels"], classification_result["scores"]):
-                if score >= threshold:
+                if score >= CONFIDENCE_THRESHOLD:
                     # Perform sentiment analysis on the sentence
                     sentiment_result = sentiment_analyzer(sentence)
                     sentiment_label = sentiment_result[0]["label"]
@@ -91,4 +85,4 @@ if st.button("Classify Feedback"):
                 st.write("")  # Add a blank line for readability
 
         if not found_categories:
-            st.warning("No categories matched the selected confidence threshold.")
+            st.warning("No categories met the confidence threshold of 0.8.")