Update app.py

app.py

@@ -1,43 +1,20 @@
 import streamlit as st
 from transformers import pipeline
 
-# Load the Hugging Face models
+# Load the Hugging Face pipelines
 classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
 sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
 
 # Define the categories for customer feedback
 CATEGORIES = ["Pricing", "Feature", "Customer Service", "Delivery", "Quality"]
 
-# Function to map sentiment to a rating (1 to 5)
-def sentiment_to_rating(sentiment_label, sentiment_score):
-    """
-    Convert sentiment analysis results (label and score) to a rating from 1 (most negative) to 5 (most positive).
-    """
-    if sentiment_label == "POSITIVE":
-        if sentiment_score >= 0.8:
-            return 5  # Most positive
-        elif sentiment_score >= 0.6:
-            return 4
-        elif sentiment_score >= 0.4:
-            return 3
-        else:
-            return 2
-    elif sentiment_label == "NEGATIVE":
-        if sentiment_score >= 0.8:
-            return 1  # Most negative
-        elif sentiment_score >= 0.6:
-            return 2
-        elif sentiment_score >= 0.4:
-            return 3
-        else:
-            return 4
-
 # Streamlit app UI
-st.title("Customer Feedback Categorization and Sentiment Analysis")
+st.title("Customer Feedback Categorization with Sentiment Analysis")
 st.markdown(
     """
-     This app can detect the topics and intent of customer feedback
-    and determine the sentiment (rating from 1 to 5) for each relevant category.
+    This app uses Hugging Face models to detect the topics and intent of customer feedback
+    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.
     """
 )
 
@@ -48,7 +25,7 @@ feedback_input = st.text_area(
     height=200
 )
 
-# Confidence threshold for displaying categories
+# Confidence threshold for zero-shot classification
 threshold = st.slider(
     "Confidence Threshold",
     min_value=0.0,
@@ -59,7 +36,7 @@ threshold = st.slider(
 )
 
 # Classify button
-if st.button("Analyze Feedback"):
+if st.button("Classify Feedback"):
     if not feedback_input.strip():
         st.error("Please provide valid feedback text.")
     else:
@@ -68,29 +45,24 @@ if st.button("Analyze Feedback"):
 
         # Filter categories with scores above the threshold
         relevant_categories = {
-            label: score
+            label: round(score, 4)
             for label, score in zip(classification_result["labels"], classification_result["scores"])
             if score >= threshold
         }
 
+        # Check if there are any relevant categories
         if relevant_categories:
-            st.subheader("Categorized Feedback and Sentiment Ratings")
-
-            # Perform sentiment analysis for the feedback
-            sentiment_result = sentiment_analyzer(feedback_input)
-            sentiment_score = sentiment_result[0]["score"]
-            sentiment_label = sentiment_result[0]["label"]
+            st.subheader("Categorized Feedback with Sentiment Analysis")
 
-            # Display results for each category
             for category, score in relevant_categories.items():
-                sentiment_rating = sentiment_to_rating(sentiment_label, sentiment_score)
-                st.write(
-                    f"**Category**: {category}\n"
-                    f"**Category Score**: {score:.4f}\n"
-                    f"**Sentiment**: {sentiment_label} ({sentiment_score:.4f})\n"
-                    f"**Rating**: {sentiment_rating}/5"
-                )
-                st.markdown("---")
+                # Extract the part of feedback relevant to the category for sentiment analysis
+                sentiment_result = sentiment_analyzer(feedback_input)
+                sentiment_label = sentiment_result[0]["label"]
+                sentiment_score = round(sentiment_result[0]["score"], 4)
+
+                # Display the category, confidence score, and sentiment result
+                st.write(f"### **{category}**")
+                st.write(f"- Confidence: {score}")
+                st.write(f"- Sentiment: {sentiment_label} (Score: {sentiment_score})")
         else:
-            st.warning("No categories matched the selected confidence threshold.")
-            
+            st.warning("No categories matched the selected confidence threshold.")