Update app.py

app.py

@@ -1,130 +1,118 @@
 import streamlit as st
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 import torch
-from transformers import pipeline
-import textwrap
 
-# Page configuration
 st.set_page_config(
-    page_title="Bank Review Analyzer",
-    page_icon="🏦",
+    page_title="Review Assistant",
+    page_icon="📝",
     layout="centered"
 )
 
-# Custom CSS styling
-st.markdown("""
-<style>
-.analysis-section {
-    padding: 1.5rem;
-    margin: 1rem 0;
-}
-.response-box {
-    background-color: #F8FAFC;
-    padding: 1.5rem;
-    border-radius: 0.5rem;
-    margin: 1rem 0;
-}
-</style>
-""", unsafe_allow_html=True)
+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
+)
 
+# ------- Load Pipelines -------
 @st.cache_resource
-def load_models():
-    """Load ML models"""
-    with st.spinner("🚀 Loading AI models..."):
-        device = 0 if torch.cuda.is_available() else -1
-        
-        classifier = pipeline(
-            "zero-shot-classification",
-            model="MoritzLaurer/deberta-v3-base-zeroshot-v1",
-            device=device,
-            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
-        )
-        
-        sentiment_analyzer = pipeline(
-            "sentiment-analysis", 
-            model="cardiffnlp/twitter-roberta-base-sentiment-latest",
-            device=device
-        )
-        
-        response_generator = pipeline(
-            "text2text-generation",
-            model="Leo66277/custom-response-generator",
-            device=device
-        )
-        
-    return classifier, sentiment_analyzer, response_generator
-
-def analyze_review(text, models):
-    """Analysis pipeline"""
-    classifier, sentiment_analyzer, response_generator = models
+def load_pipelines():
+    # Topic Classification Model (Zero-shot classification)
+    topic_labels = [
+        "billing", "account access", "customer service", "loans",
+        "fraud", "technical issue", "credit card", "mobile app",
+        "branch service", "transaction delay", "account closure", "information error"
+    ]
     
-    # Topic classification
-    topics = ["customer service", "mobile app", "credit card", "account security"]
-    topic_result = classifier(text, topics, multi_label=False)
-    main_topic = topic_result['labels'][0]
+    device = 0 if torch.cuda.is_available() else -1
+    dtype = torch.float16 if torch.cuda.is_available() else torch.float32
     
-    # Sentiment analysis
-    sentiment_result = sentiment_analyzer(text)[0]
-    sentiment = "positive" if sentiment_result['label'] in ['POSITIVE', 'positive'] else "negative"
-    
-    # Generate response
-    prompt = f"""Review: {text}
-Topic: {main_topic}
-Sentiment: {sentiment}
-Generate response:"""
-    
-    response = response_generator(
-        prompt,
-        max_length=300,
-        num_return_sequences=1,
-        do_sample=True,
-        temperature=0.7
-    )[0]['generated_text'].strip()
-    
-    return main_topic, sentiment, response
-
-def main():
-    st.title("🏦 Bank Review Analysis")
+    topic_classifier = pipeline(
+        "zero-shot-classification",
+        model="MoritzLaurer/deberta-v3-base-zeroshot-v1",
+        device=device,
+        torch_dtype=dtype
+    )
     
-    # Main input
-    review = st.text_area(
-        "Enter customer review",
-        height=150,
-        placeholder="Paste your bank review here..."
+    # Sentiment Analysis Model
+    sentiment_classifier = pipeline(
+        "sentiment-analysis",
+        model="cardiffnlp/twitter-roberta-base-sentiment-latest",  # Best performing model from notebook
+        device=device
     )
     
-    # Load models
-    models = load_models()
+    # Reply Generation Model
+    model_name = "Leo66277/finetuned-tinyllama-customer-replies"  # Using the fine-tuned model in the notebook
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    model.to(device)
     
-    if st.button("Analyze Review"):
-        if review.strip():
-            topic, sentiment, response = analyze_review(review, models)
-            
-            # Display results
-            st.markdown("---")
-            st.markdown("### Analysis Results")
-            
-            # Results columns
-            col1, col2 = st.columns(2)
-            with col1:
-                st.markdown(f"**Detected Topic:** {topic}")
-                st.markdown(f"**Sentiment:** {sentiment.capitalize()}")
+    def generate_reply(text):
+        prompt_text = f"Please write a short, polite English customer service reply to the following customer comment:\n{text}"
+        inputs = tokenizer(prompt_text, return_tensors="pt", truncation=True, max_length=512).to(device)
+        
+        with torch.no_grad():
+            gen_ids = model.generate(
+                inputs.input_ids,
+                max_length=inputs.input_ids.shape[1] + 120,
+                pad_token_id=tokenizer.eos_token_id if tokenizer.eos_token_id else tokenizer.pad_token_id,
+                num_beams=3,
+                no_repeat_ngram_size=2,
+                early_stopping=True
+            )
             
-            # Response section
-            st.markdown("---")
-            st.markdown("### Suggested Response")
-            st.markdown(f'<div class="response-box">{textwrap.fill(response, width=80)}</div>', 
-                       unsafe_allow_html=True)
+        reply = tokenizer.decode(gen_ids[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip()
+        reply = reply.strip('"').replace('\n', ' ').replace('  ', ' ')
+        return reply
+    
+    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 ↓")
+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.):",
+    value=example_review,
+    height=120
+)
+
+if st.button("Analyze"):
+    if not user_review.strip():
+        st.warning("Please enter a valid review!")
+    else:
+        with st.spinner("Analyzing..."):
+            # Topic Classification
+            topic_result = topic_pipe(user_review, topic_labels, multi_label=False)
+            topic = topic_result['labels'][0]
+            topic_score = round(topic_result['scores'][0] * 100, 1)
             
-            # Download button
-            st.download_button(
-                label="Download Report",
-                data=f"Topic: {topic}\nSentiment: {sentiment}\n\nResponse:\n{response}",
-                file_name="review_analysis.txt",
-                mime="text/plain"
-            )
+            # Sentiment Analysis
+            sentiment_result = sentiment_pipe(user_review)
+            sentiment = sentiment_result[0]['label']
+            sentiment_score = round(sentiment_result[0]['score'] * 100, 1)
             
-        else:
-            st.warning("Please enter a review to analyze")
+            # Auto Reply Generation
+            reply_text = reply_generator(user_review)
+
+        # Output Results - card style
+        col1, col2 = st.columns(2)
+        with col1:
+            st.success(f"**Topic:** {topic} ({topic_score}%)")
+        with col2:
+            st.success(f"**Sentiment:** {sentiment} ({sentiment_score}%)")
+        
+        st.info("**Auto-reply Suggestion:**")
+        st.write(f"> {reply_text}")
 
-if __name__ == "__main__":
-    main()
+st.markdown(
+    """
+    <hr/>
+    <p style='text-align: center; color: #bbb;'>© 2024 Review AI Assistant</p>
+    """,
+    unsafe_allow_html=True
+)