Update app.py

app.py

@@ -8,13 +8,69 @@ st.set_page_config(
     layout="centered"
 )
 
-st.markdown(
-    """
-    <h2 style='text-align: center;'>Smart Review Analysis Assistant</h2>
-    <p style='text-align: center; color: #888;'>Topic Recognition, Sentiment Analysis, and Auto Reply in One Click</p>
-    """,
-    unsafe_allow_html=True
-)
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.2rem;
+        color: #1E3A8A;
+        text-align: center;
+        margin-bottom: 0;
+        padding-bottom: 0;
+    }
+    .sub-header {
+        font-size: 1rem;
+        color: #6B7280;
+        text-align: center;
+        margin-top: 0.3rem;
+        margin-bottom: 2rem;
+    }
+    .result-card {
+        padding: 1.2rem;
+        border-radius: 8px;
+        margin-bottom: 1rem;
+    }
+    .topic-card {
+        background-color: #ECFDF5;
+        border-left: 4px solid #10B981;
+    }
+    .sentiment-card {
+        background-color: #EFF6FF;
+        border-left: 4px solid #3B82F6;
+    }
+    .reply-card {
+        background-color: #F9FAFB;
+        border-left: 4px solid #6B7280;
+        padding: 1.5rem;
+    }
+    .result-label {
+        font-weight: bold;
+        margin-bottom: 0.5rem;
+    }
+    .stButton>button {
+        background-color: #2563EB;
+        color: white;
+        border: none;
+        padding: 0.5rem 2rem;
+        border-radius: 6px;
+        font-weight: 500;
+    }
+    .stButton>button:hover {
+        background-color: #1D4ED8;
+    }
+    .footer {
+        text-align: center;
+        color: #9CA3AF;
+        font-size: 0.8rem;
+        margin-top: 3rem;
+    }
+    .stSpinner {
+        text-align: center;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+st.markdown("<h1 class='main-header'>Smart Review Analysis Assistant</h1>", unsafe_allow_html=True)
+st.markdown("<p class='sub-header'>Topic Recognition, Sentiment Analysis, and Auto Reply in One Click</p>", unsafe_allow_html=True)
 
 # ------- Load Pipelines -------
 @st.cache_resource
@@ -26,6 +82,7 @@ def load_pipelines():
         "branch service", "transaction delay", "account closure", "information error"
     ]
     
+
     dtype = torch.float32  
     
     topic_classifier = pipeline(
@@ -36,14 +93,13 @@ def load_pipelines():
     # Sentiment Analysis Model
     sentiment_classifier = pipeline(
         "sentiment-analysis",
-        model="cardiffnlp/twitter-roberta-base-sentiment-latest",  
+        model="cardiffnlp/twitter-roberta-base-sentiment-latest",
     )
     
     # Reply Generation Model
     model_name = "Leo66277/finetuned-tinyllama-customer-replies"
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForCausalLM.from_pretrained(model_name)
-
     
     def generate_reply(text):
         prompt_text = f"Please write a short, polite English customer service reply to the following customer comment:\n{text}"
@@ -65,14 +121,12 @@ def load_pipelines():
     
     return topic_classifier, sentiment_classifier, generate_reply, topic_labels
 
-topic_pipe, sentiment_pipe, reply_generator, topic_labels = load_pipelines()
 
-# ------- Page Layout --------
-st.markdown("### Enter a review and get instant analysis ↓")
+st.markdown("### Enter a review for instant analysis")
 example_review = "The people at the call center are inexperienced and lack proper training. I had to call multiple times to resolve a simple issue."
 
 user_review = st.text_area(
-    "Please enter or paste a review (e.g.):",
+    "Please enter or paste a review below:",
     value=example_review,
     height=120
 )
@@ -81,34 +135,27 @@ if st.button("Analyze"):
     if not user_review.strip():
         st.warning("Please enter a valid review!")
     else:
-        with st.spinner("Analyzing..."):
+        with st.spinner("Analyzing your review..."):
+            if "topic_pipe" not in st.session_state:
+                st.session_state.topic_pipe, st.session_state.sentiment_pipe, st.session_state.reply_generator, st.session_state.topic_labels = load_pipelines()
+            
             # Topic Classification
-            topic_result = topic_pipe(user_review, topic_labels, multi_label=False)
+            topic_result = st.session_state.topic_pipe(user_review, st.session_state.topic_labels, multi_label=False)
             topic = topic_result['labels'][0]
-            topic_score = round(topic_result['scores'][0] * 100, 1)
             
             # Sentiment Analysis
-            sentiment_result = sentiment_pipe(user_review)
+            sentiment_result = st.session_state.sentiment_pipe(user_review)
             sentiment = sentiment_result[0]['label']
-            sentiment_score = round(sentiment_result[0]['score'] * 100, 1)
             
             # Auto Reply Generation
-            reply_text = reply_generator(user_review)
+            reply_text = st.session_state.reply_generator(user_review)
 
-        # Output Results - card style
         col1, col2 = st.columns(2)
         with col1:
-            st.success(f"**Topic:** {topic} ({topic_score}%)")
+            st.markdown(f"<div class='result-card topic-card'><p class='result-label'>Topic:</p>{topic}</div>", unsafe_allow_html=True)
         with col2:
-            st.success(f"**Sentiment:** {sentiment} ({sentiment_score}%)")
+            st.markdown(f"<div class='result-card sentiment-card'><p class='result-label'>Sentiment:</p>{sentiment}</div>", unsafe_allow_html=True)
         
-        st.info("**Auto-reply Suggestion:**")
-        st.write(f"> {reply_text}")
+        st.markdown(f"<div class='result-card reply-card'><p class='result-label'>Auto-reply Suggestion:</p>{reply_text}</div>", unsafe_allow_html=True)
 
-st.markdown(
-    """
-    <hr/>
-    <p style='text-align: center; color: #bbb;'>© 2024 Review AI Assistant</p>
-    """,
-    unsafe_allow_html=True
-)
+st.markdown("<div class='footer'>© 2024 Review AI Assistant</div>", unsafe_allow_html=True)