Update app.py

app.py

@@ -1,30 +1,233 @@
 import streamlit as st
+import torch
 from transformers import pipeline
+import textwrap
 
-# Load the text classification model pipeline
-classifier = pipeline("text-classification",model='isom5240ust/bert-base-uncased-emotion', return_all_scores=True)
+# Page configuration
+st.set_page_config(
+    page_title="Bank Review Analyzer",
+    page_icon="🏦",
+    layout="centered"
+)
 
-# Streamlit application title
-st.title("Text Classification for you")
-st.write("Classification for 6 emotions: sadness, joy, love, anger, fear, surprise")
+# Custom CSS styling
+st.markdown("""
+<style>
+.analysis-card {
+    padding: 1.5rem;
+    border-radius: 0.5rem;
+    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
+    margin: 1rem 0;
+}
+.positive { border-left: 0.5rem solid #10B981 !important; }
+.negative { border-left: 0.5rem solid #EF4444 !important; }
+.step-indicator {
+    font-size: 1.2rem;
+    font-weight: 600;
+    color: #6B7280;
+    margin: 1.5rem 0;
+}
+</style>
+""", unsafe_allow_html=True)
 
-# Text input for user to enter the text to classify
-text = st.text_area("Enter the text to classify", "")
+# Response templates
+response_templates = {
+    "billing": {
+        "positive": "Thank you for your positive feedback on our billing services. We're delighted to hear about your experience...",
+        "negative": "We sincerely apologize for the issues with our billing services. Your concerns are important to us..."
+    },
+    # Add other template categories...
+}
 
-# Perform text classification when the user clicks the "Classify" button
-if st.button("Classify"):
-    # Perform text classification on the input text
-    results = classifier(text)[0]
+@st.cache_resource
+def load_models():
+    """Load all required ML models"""
+    with st.spinner("🚀 Loading AI models..."):
+        device = 0 if torch.cuda.is_available() else -1
+        
+        # Zero-shot classification model
+        topic_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 analysis model
+        sentiment_analyzer = pipeline(
+            "sentiment-analysis", 
+            model="cardiffnlp/twitter-roberta-base-sentiment-latest",
+            device=device
+        )
+        
+        # Response generation model
+        response_generator = pipeline(
+            "text2text-generation",
+            model="Leo66277/custom-response-generator",
+            device=device
+        )
+        
+    return topic_classifier, sentiment_analyzer, response_generator
 
-    # Display the classification result
-    max_score = float('-inf')
-    max_label = ''
+def analyze_review(text, models):
+    """Full analysis pipeline"""
+    topic_classifier, sentiment_analyzer, response_generator = models
+    
+    # Step 1: Topic classification
+    with st.spinner("🔍 Analyzing review topic..."):
+        topic_labels = list(response_templates.keys())
+        topic_result = topic_classifier(text, topic_labels, multi_label=False)
+        main_topic = topic_result['labels'][0]
+        topic_confidence = topic_result['scores'][0]
+    
+    # Step 2: Sentiment analysis
+    with st.spinner("💡 Detecting sentiment..."):
+        sentiment_result = sentiment_analyzer(text)[0]
+        sentiment_label = "positive" if sentiment_result['label'] in ['POSITIVE', 'positive'] else "negative"
+        sentiment_score = sentiment_result['score']
+    
+    # Step 3: Generate response
+    with st.spinner("✍️ Generating professional response..."):
+        prompt = f"""Review: {text}
+Topic: {main_topic}
+Sentiment: {sentiment_label}
+Generate response:"""
+        
+        generated_response = response_generator(
+            prompt,
+            max_length=300,
+            num_return_sequences=1,
+            do_sample=True,
+            temperature=0.7
+        )[0]['generated_text'].strip()
+    
+    return {
+        "topic": main_topic,
+        "topic_confidence": f"{topic_confidence:.1%}",
+        "sentiment": sentiment_label,
+        "sentiment_score": f"{sentiment_score:.1%}",
+        "response": generated_response,
+        "template_response": response_templates.get(main_topic, {}).get(sentiment_label)
+    }
 
-    for result in results:
-        if result['score'] > max_score:
-            max_score = result['score']
-            max_label = result['label']
+def format_text(text, line_width=80):
+    """Text formatting"""
+    return "\n".join(textwrap.wrap(text, width=line_width))
 
-    st.write("Text:", text)
-    st.write("Label:", max_label)
-    st.write("Score:", max_score)
+# Main interface
+def main():
+    st.title("🏦 Bank Review Analysis System")
+    st.markdown("---")
+    
+    # Sidebar configuration
+    with st.sidebar:
+        st.header("Settings")
+        show_details = st.checkbox("Show analysis details", True)
+        show_template = st.checkbox("Show template response", True)
+        st.markdown("---")
+        st.caption("Version: 1.0 | Developer: Leo66277")
+    
+    # Main input area
+    with st.form(key="analysis_form"):
+        col1, col2 = st.columns([3, 1])
+        with col1:
+            review_text = st.text_area(
+                "Enter customer review",
+                placeholder="Paste your bank review here...",
+                height=150
+            )
+        with col2:
+            st.markdown("### Example Reviews")
+            st.caption("▶️ Difficulty logging into mobile banking")
+            st.caption("▶️ Unreasonable credit card annual fee")
+            st.caption("▶️ Excellent counter service attitude")
+        
+        submitted = st.form_submit_button("Start Analysis", type="primary")
+
+    # Load models
+    models = load_models()
+
+    if submitted and review_text.strip():
+        # Perform analysis
+        result = analyze_review(review_text, models)
+        
+        # Display results
+        with st.container():
+            st.markdown("## Analysis Results")
+            
+            # Sentiment indicators
+            sentiment_icon = "✅" if result['sentiment'] == "positive" else "⚠️"
+            
+            # Main metrics card
+            with st.expander("Key Metrics", expanded=True):
+                cols = st.columns(4)
+                cols[0].metric("Detected Topic", result['topic'])
+                cols[1].metric("Topic Confidence", result['topic_confidence'])
+                cols[2].metric("Sentiment", f"{sentiment_icon} {result['sentiment'].capitalize()}")
+                cols[3].metric("Sentiment Strength", result['sentiment_score'])
+            
+            # Detailed analysis
+            if show_details:
+                with st.expander("Detailed Analysis", expanded=False):
+                    tab1, tab2, tab3 = st.tabs(["Original Review", "Topic Distribution", "Sentiment Analysis"])
+                    
+                    with tab1:
+                        st.code(format_text(review_text), language="text")
+                        
+                    with tab2:
+                        st.caption("Topic Probability Distribution")
+                        topic_data = {
+                            'Topic': result['topic_distribution']['labels'],
+                            'Confidence': result['topic_distribution']['scores']
+                        }
+                        st.bar_chart(topic_data, x='Topic', y='Confidence')
+                    
+                    with tab3:
+                        st.caption("Sentiment Analysis Raw Results")
+                        st.json({
+                            "label": result['sentiment'],
+                            "score": float(result['sentiment_score'].strip('%'))/100
+                        })
+            
+            # Response generation
+            st.markdown("## 💬 Response Suggestions")
+            
+            col1, col2 = st.columns(2)
+            with col1:
+                with st.container(border=True):
+                    st.markdown("### 🚀 AI Generated Response")
+                    st.markdown(f'<div class="analysis-card positive">{format_text(result["response"])}</div>', 
+                               unsafe_allow_html=True)
+            
+            if show_template and result['template_response']:
+                with col2:
+                    with st.container(border=True):
+                        st.markdown("### 📋 Template Response")
+                        st.markdown(f'<div class="analysis-card">{format_text(result["template_response"])}</div>', 
+                                   unsafe_allow_html=True)
+            
+            # Download report
+            st.download_button(
+                label="Download Full Report",
+                data=f"""Analysis Report
+                
+                Original Review:
+                {review_text}
+                
+                Topic Analysis: {result['topic']} ({result['topic_confidence']})
+                Sentiment Analysis: {result['sentiment'].capitalize()} ({result['sentiment_score']})
+                
+                AI Generated Response:
+                {result['response']}
+                
+                {"Template Response: " + result['template_response'] if result['template_response'] else ""}
+                """,
+                file_name="bank_review_analysis.txt",
+                mime="text/plain"
+            )
+
+    elif submitted:
+        st.warning("⚠️ Please enter valid review content")
+
+if __name__ == "__main__":
+    main()