Update app.py

app.py

@@ -15,19 +15,58 @@ CATEGORIES = ["Pricing", "Feature", "Customer Service", "Delivery", "Quality"]
 # Define the fixed confidence threshold
 CONFIDENCE_THRESHOLD = 0.8
 
+# Custom CSS for background colors
+st.markdown(
+    """
+    <style>
+    /* Background color for the title */
+    .title-container {
+        background-color: #e6f3ff;  /* Light blue background */
+        padding: 10px;
+        border-radius: 5px;
+    }
+    /* Background color for the description (st.markdown) */
+    .description-container {
+        background-color: #f0f0f0;  /* Light gray background */
+        padding: 10px;
+        border-radius: 5px;
+    }
+    /* Background color for the text area (feedback_input) */
+    .stTextArea textarea {
+        background-color: #fff5e6;  /* Light peach background */
+        border-radius: 5px;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
 # Streamlit app UI
-st.title("Customer Feedback Categorization with Sentiment Analysis")
+# Title with icon
 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.
-    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.
+    <div class="title-container">
+        <h1>📢 Customer Feedback Categorization with Sentiment Analysis</h1>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+# Description with background color
+st.markdown(
     """
+    <div class="description-container">
+        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.
+        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.
+    </div>
+    """,
+    unsafe_allow_html=True
 )
 
-# Input text box for customer feedback
+# Input text box for customer feedback with background color
 feedback_input = st.text_area(
     "Enter customer feedback:",
     placeholder="Type your feedback here...",
@@ -90,7 +129,7 @@ if st.button("Classify Feedback"):
         st.subheader("Categorized Feedback with Sentiment Analysis")
         found_categories = False
 
-        for category, results in category_results.items():
+        for i, (category, results) in enumerate(category_results.items()):
             if results:  # If the category has any sentences
                 found_categories = True
                 st.write(f"### **{category}**")
@@ -104,7 +143,9 @@ if st.button("Classify Feedback"):
                         f"(Score: {result['sentiment_score']})",
                         unsafe_allow_html=True
                     )
-                st.write("")  # Add a blank line for readability
+                # Add a horizontal divider after each category (except the last one)
+                if i < len(category_results) - 1 and any(category_results[cat] for cat in list(category_results.keys())[i+1:]):
+                    st.markdown("---")  # Horizontal line to separate categories
 
         if not found_categories:
             st.warning("No categories met the confidence threshold of 0.8.")