Update app.py

app.py

@@ -1,20 +1,43 @@
 import streamlit as st
 from transformers import pipeline
 
-# Load the Hugging Face pipelines
+# Load the Hugging Face models
 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 with Sentiment Analysis")
+st.title("Customer Feedback Categorization and Sentiment Analysis")
 st.markdown(
     """
-    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.
+     This app can detect the topics and intent of customer feedback
+    and determine the sentiment (rating from 1 to 5) for each relevant category.
     """
 )
 
@@ -25,7 +48,7 @@ feedback_input = st.text_area(
     height=200
 )
 
-# Confidence threshold for zero-shot classification
+# Confidence threshold for displaying categories
 threshold = st.slider(
     "Confidence Threshold",
     min_value=0.0,
@@ -36,7 +59,7 @@ threshold = st.slider(
 )
 
 # Classify button
-if st.button("Classify Feedback"):
+if st.button("Analyze Feedback"):
     if not feedback_input.strip():
         st.error("Please provide valid feedback text.")
     else:
@@ -45,24 +68,28 @@ if st.button("Classify Feedback"):
 
         # Filter categories with scores above the threshold
         relevant_categories = {
-            label: round(score, 4)
+            label: score
             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 with Sentiment Analysis")
+            st.subheader("Categorized Feedback and Sentiment Ratings")
 
-            for category, score in relevant_categories.items():
-                # 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)
+            # Perform sentiment analysis for the feedback
+            sentiment_result = sentiment_analyzer(feedback_input)
+            sentiment_score = sentiment_result[0]["score"]
+            sentiment_label = sentiment_result[0]["label"]
 
-                # 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})")
+            # 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("---")
         else:
             st.warning("No categories matched the selected confidence threshold.")