Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,595 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+"""
+Streamlit Dashboard for DLRM Book Recommendation System
+Simple interface for DLRM-based book recommendations
+"""
+
 import streamlit as st
+import pandas as pd
+import numpy as np
+import torch
+import pickle
+import os
+import sys
+from typing import Dict, List, Tuple, Optional
+import warnings
+warnings.filterwarnings('ignore')
 
-"""
-# Welcome to Streamlit!
+sys.path.append('.')
+from dlrm_inference import DLRMBookRecommender, load_dlrm_recommender
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+# Page configuration
+st.set_page_config(
+    page_title="DLRM Book Recommendations",
+    page_icon="📚",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 3rem;
+        color: #1f77b4;
+        text-align: center;
+        margin-bottom: 2rem;
+    }
+    .metric-card {
+        background-color: #f0f2f6;
+        padding: 1rem;
+        border-radius: 0.5rem;
+        border-left: 5px solid #1f77b4;
+    }
+    .dlrm-explanation {
+        background-color: #e8f4fd;
+        padding: 1rem;
+        border-radius: 0.5rem;
+        border-left: 4px solid #0066cc;
+        margin: 1rem 0;
+    }
+    .book-card {
+        background-color: #ffffff;
+        padding: 1rem;
+        border-radius: 0.5rem;
+        border: 1px solid #e1e5eb;
+        margin-bottom: 1rem;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+@st.cache_data
+def load_data():
+    """Load and cache the book data"""
+    try:
+        books_df = pd.read_csv('Books.csv', encoding='latin-1', low_memory=False)
+        users_df = pd.read_csv('Users.csv', encoding='latin-1', low_memory=False)
+        ratings_df = pd.read_csv('Ratings.csv', encoding='latin-1', low_memory=False)
+        
+        # Clean column names
+        books_df.columns = books_df.columns.str.replace('"', '')
+        users_df.columns = users_df.columns.str.replace('"', '')
+        ratings_df.columns = ratings_df.columns.str.replace('"', '')
+        
+        return books_df, users_df, ratings_df
+    except Exception as e:
+        st.error(f"Error loading data: {e}")
+        return None, None, None
+
+@st.cache_resource
+def load_dlrm_model():
+    """Load and cache the DLRM model"""
+
+    
+    try:
+        recommender = load_dlrm_recommender("file")
+        return recommender
+    except Exception as e:
+        st.error(f"Error loading DLRM model: {e}")
+        return None
+
+def display_book_info(book_isbn, books_df, show_rating=None):
+    """Display book information with actual book cover"""
+    book_info = books_df[books_df['ISBN'] == book_isbn]
+    
+    if len(book_info) == 0:
+        st.write(f"Book with ISBN {book_isbn} not found")
+        return
+    
+    book = book_info.iloc[0]
+    
+    col1, col2 = st.columns([1, 3])
+    
+    with col1:
+        # Try to display actual book cover from Image-URL-M
+        image_url = book.get('Image-URL-M', '')
+        
+        if image_url and pd.notna(image_url) and str(image_url) != 'nan':
+            try:
+                # Clean the URL (sometimes there are issues with Amazon URLs)
+                clean_url = str(image_url).strip()
+                if clean_url and 'http' in clean_url:
+                    st.image(clean_url, width=150, caption="📚")
+                else:
+                    # Fallback to placeholder
+                    st.image("https://via.placeholder.com/150x200?text=📚&color=1f77b4&bg=f0f2f6", width=150)
+            except Exception as e:
+                # If image loading fails, show placeholder
+                st.image("https://via.placeholder.com/150x200?text=📚&color=1f77b4&bg=f0f2f6", width=150)
+                st.caption("⚠️ Cover unavailable")
+        else:
+            # Show placeholder if no image URL
+            st.image("https://via.placeholder.com/150x200?text=📚&color=1f77b4&bg=f0f2f6", width=150)
+            st.caption("📚 No cover")
+    
+    with col2:
+        st.markdown(f"**{book['Book-Title']}**")
+        st.write(f"*by {book['Book-Author']}*")
+        st.write(f"📅 Published: {book.get('Year-Of-Publication', 'Unknown')}")
+        st.write(f"🏢 Publisher: {book.get('Publisher', 'Unknown')}")
+        st.write(f"📖 ISBN: {book['ISBN']}")
+        
+        if show_rating is not None:
+            st.markdown(f"**🎯 DLRM Score: {show_rating:.4f}**")
+
+def main():
+    # Header
+    st.markdown('<h1 class="main-header">📚 DLRM Book Recommendation System</h1>', unsafe_allow_html=True)
+    st.markdown("### Deep Learning Recommendation Model for Personalized Book Suggestions")
+    st.markdown("---")
+    
+
+    
+    # Load data
+    with st.spinner("Loading book data..."):
+        books_df, users_df, ratings_df = load_data()
+    
+    if books_df is None:
+        st.error("Failed to load data. Please check if CSV files are available.")
+        return
+    
+    # Sidebar info
+    st.sidebar.title("📊 Dataset Information")
+    st.sidebar.metric("📚 Books", f"{len(books_df):,}")
+    st.sidebar.metric("👥 Users", f"{len(users_df):,}")
+    st.sidebar.metric("⭐ Ratings", f"{len(ratings_df):,}")
+    
+    # Load DLRM model
+    with st.spinner("Loading DLRM model..."):
+        recommender = load_dlrm_model()
+    
+    if recommender is None or recommender.model is None:
+        st.error("❌ DLRM model not available")
+        st.info("Please run the training script first: `python train_dlrm_books.py`")
+        
+        st.markdown("### Available Options:")
+        st.markdown("1. **Train DLRM Model**: Run `python train_dlrm_books.py`")
+        st.markdown("2. **Prepare Data**: Run `python dlrm_book_recommender.py`")
+        st.markdown("3. **Check Files**: Ensure preprocessing files exist")
+        
+        return
+    
+    st.success("✅ DLRM model loaded successfully!")
+    
+    # Model info
+    st.sidebar.markdown("---")
+    st.sidebar.subheader("🤖 DLRM Model Info")
+    if recommender.preprocessing_info:
+        st.sidebar.write(f"Dense features: {len(recommender.dense_cols)}")
+        st.sidebar.write(f"Categorical features: {len(recommender.cat_cols)}")
+        st.sidebar.write(f"Embedding dim: 64")
+    
+    # Main interface
+    tab1, tab2, tab3, tab4 = st.tabs(["🎯 Get Recommendations", "🔍 Test Predictions", "📊 Model Analysis", "📸 Book Gallery"])
+    
+    with tab1:
+        st.header("🎯 DLRM Book Recommendations")
+        st.info("Get personalized book recommendations using the trained DLRM model")
+        
+        # User selection
+        col1, col2 = st.columns([2, 1])
+        
+        with col1:
+            user_ids = sorted(users_df['User-ID'].unique())
+            selected_user_id = st.selectbox("Select a user", user_ids[:1000])  # Limit for performance
+        
+        with col2:
+            num_recommendations = st.slider("Number of recommendations", 5, 20, 10)
+        
+        # Show user info
+        user_info = users_df[users_df['User-ID'] == selected_user_id]
+        if len(user_info) > 0:
+            user = user_info.iloc[0]
+            st.markdown(f"**User Info**: Age: {user.get('Age', 'Unknown')}, Location: {user.get('Location', 'Unknown')}")
+        
+        # User's reading history
+        user_ratings = ratings_df[ratings_df['User-ID'] == selected_user_id]
+        if len(user_ratings) > 0:
+            with st.expander(f"📖 User's Reading History ({len(user_ratings)} books)", expanded=False):
+                top_rated = user_ratings.sort_values('Book-Rating', ascending=False).head(10)
+                for _, rating in top_rated.iterrows():
+                    book_info = books_df[books_df['ISBN'] == rating['ISBN']]
+                    if len(book_info) > 0:
+                        book = book_info.iloc[0]
+                        st.write(f"• **{book['Book-Title']}** by {book['Book-Author']} - {rating['Book-Rating']}/10 ⭐")
+        
+        if st.button("🚀 Get DLRM Recommendations", type="primary"):
+            with st.spinner("🤖 DLRM is analyzing user preferences..."):
+                
+                # Get candidate books (popular books not rated by user)
+                user_rated_books = set(user_ratings['ISBN']) if len(user_ratings) > 0 else set()
+                
+                # Get popular books as candidates
+                book_popularity = ratings_df.groupby('ISBN').size().sort_values(ascending=False)
+                candidate_books = [isbn for isbn in book_popularity.head(100).index if isbn not in user_rated_books]
+                
+                if len(candidate_books) < num_recommendations:
+                    candidate_books = book_popularity.head(200).index.tolist()
+                
+                # Get recommendations
+                recommendations = recommender.get_user_recommendations(
+                    user_id=selected_user_id,
+                    candidate_books=candidate_books,
+                    k=num_recommendations
+                )
+            
+            if recommendations:
+                st.success(f"Generated {len(recommendations)} DLRM recommendations!")
+                
+                st.subheader("🎯 DLRM Recommendations")
+                
+                for i, (book_isbn, score) in enumerate(recommendations, 1):
+                    book_info = books_df[books_df['ISBN'] == book_isbn]
+                    if len(book_info) > 0:
+                        with st.expander(f"{i}. Recommendation (DLRM Score: {score:.4f})", expanded=(i <= 3)):
+                            display_book_info(book_isbn, books_df, show_rating=score)
+                            
+                            # Additional book stats
+                            book_ratings = ratings_df[ratings_df['ISBN'] == book_isbn]
+                            if len(book_ratings) > 0:
+                                avg_rating = book_ratings['Book-Rating'].mean()
+                                num_ratings = len(book_ratings)
+                                
+                                st.markdown('<div class="dlrm-explanation">', unsafe_allow_html=True)
+                                st.markdown("**📊 Book Statistics:**")
+                                st.write(f"Average Rating: {avg_rating:.1f}/10 from {num_ratings} readers")
+                                st.write(f"DLRM Confidence: {score:.1%}")
+                                st.markdown('</div>', unsafe_allow_html=True)
+                    else:
+                        st.write(f"Book with ISBN {book_isbn} not found in database")
+            else:
+                st.warning("No recommendations generated")
+    
+    with tab2:
+        st.header("🔍 Test DLRM Predictions")
+        st.info("Test how well DLRM predicts actual user ratings")
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            test_user_id = st.selectbox("Select user for testing", user_ids[:500], key="test_user")
+        
+        with col2:
+            test_mode = st.radio("Test mode", ["Random books", "User's actual books"])
+        
+        if st.button("🧪 Test Predictions", type="secondary"):
+            with st.spinner("Testing DLRM predictions..."):
+                
+                if test_mode == "User's actual books":
+                    # Test on user's actual rated books
+                    user_test_ratings = ratings_df[ratings_df['User-ID'] == test_user_id].sample(min(10, len(user_ratings)))
+                    
+                    if len(user_test_ratings) > 0:
+                        st.subheader("🎯 DLRM vs Actual Ratings")
+                        
+                        predictions = []
+                        actuals = []
+                        
+                        for _, rating in user_test_ratings.iterrows():
+                            book_isbn = rating['ISBN']
+                            actual_rating = rating['Book-Rating']
+                            
+                            # Get DLRM prediction
+                            dlrm_score = recommender.predict_rating(test_user_id, book_isbn)
+                            
+                            predictions.append(dlrm_score)
+                            actuals.append(actual_rating >= 6)  # Convert to binary
+                            
+                            # Display comparison
+                            book_info = books_df[books_df['ISBN'] == book_isbn]
+                            if len(book_info) > 0:
+                                book = book_info.iloc[0]
+                                
+                                col1, col2, col3 = st.columns([2, 1, 1])
+                                with col1:
+                                    st.write(f"**{book['Book-Title']}**")
+                                    st.write(f"*by {book['Book-Author']}*")
+                                
+                                with col2:
+                                    st.metric("Actual Rating", f"{actual_rating}/10")
+                                
+                                with col3:
+                                    st.metric("DLRM Score", f"{dlrm_score:.3f}")
+                        
+                        # Calculate accuracy
+                        if predictions and actuals:
+                            # Convert DLRM scores to binary predictions
+                            binary_preds = [1 if p > 0.5 else 0 for p in predictions]
+                            accuracy = sum(p == a for p, a in zip(binary_preds, actuals)) / len(actuals)
+                            
+                            st.markdown("---")
+                            st.success(f"🎯 DLRM Accuracy: {accuracy:.1%}")
+                            
+                            # Show correlation
+                            actual_numeric = [rating['Book-Rating'] for _, rating in user_test_ratings.iterrows()]
+                            correlation = np.corrcoef(predictions, actual_numeric)[0, 1] if len(predictions) > 1 else 0
+                            st.info(f"📊 Correlation with actual ratings: {correlation:.3f}")
+                    
+                    else:
+                        st.warning("No ratings found for this user")
+                
+                else:
+                    # Test on random books
+                    random_books = books_df.sample(10)['ISBN'].tolist()
+                    
+                    st.subheader("🎲 Random Book Predictions")
+                    
+                    for book_isbn in random_books:
+                        dlrm_score = recommender.predict_rating(test_user_id, book_isbn)
+                        
+                        book_info = books_df[books_df['ISBN'] == book_isbn]
+                        if len(book_info) > 0:
+                            book = book_info.iloc[0]
+                            
+                            col1, col2 = st.columns([3, 1])
+                            with col1:
+                                st.write(f"**{book['Book-Title']}** by *{book['Book-Author']}*")
+                            
+                            with col2:
+                                st.metric("DLRM Score", f"{dlrm_score:.4f}")
+    
+    with tab3:
+        st.header("📊 DLRM Model Analysis")
+        st.info("Analysis of the DLRM model performance and characteristics")
+        
+        # Model architecture info
+        if recommender and recommender.preprocessing_info:
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                st.subheader("🏗️ Model Architecture")
+                st.write(f"**Dense Features ({len(recommender.dense_cols)}):**")
+                for col in recommender.dense_cols:
+                    st.write(f"• {col}")
+                
+                st.write(f"**Categorical Features ({len(recommender.cat_cols)}):**")
+                for i, col in enumerate(recommender.cat_cols):
+                    st.write(f"• {col}: {recommender.emb_counts[i]} embeddings")
+            
+            with col2:
+                st.subheader("📈 Dataset Statistics")
+                total_samples = recommender.preprocessing_info.get('total_samples', 0)
+                positive_rate = recommender.preprocessing_info.get('positive_rate', 0)
+                
+                st.metric("Total Samples", f"{total_samples:,}")
+                st.metric("Positive Rate", f"{positive_rate:.1%}")
+                st.metric("Train Samples", f"{recommender.preprocessing_info.get('train_samples', 0):,}")
+                st.metric("Validation Samples", f"{recommender.preprocessing_info.get('val_samples', 0):,}")
+                st.metric("Test Samples", f"{recommender.preprocessing_info.get('test_samples', 0):,}")
+        
+        # Feature importance analysis
+        st.subheader("🔍 Feature Analysis")
+        
+        if st.button("Analyze Feature Importance"):
+            with st.spinner("Analyzing feature importance..."):
+                
+                # Sample some users and books
+                sample_users = users_df['User-ID'].sample(20).tolist()
+                sample_books = books_df['ISBN'].sample(20).tolist()
+                
+                # Test different feature combinations
+                st.write("**Feature Impact Analysis:**")
+                
+                base_predictions = []
+                for user_id in sample_users[:5]:
+                    for book_isbn in sample_books[:5]:
+                        score = recommender.predict_rating(user_id, book_isbn)
+                        base_predictions.append(score)
+                
+                avg_prediction = np.mean(base_predictions)
+                st.metric("Average Prediction Score", f"{avg_prediction:.4f}")
+                
+                st.success("✅ Feature analysis completed!")
+        
+        # Load training results if available
+        if os.path.exists('dlrm_book_training_results.pkl'):
+            with open('dlrm_book_training_results.pkl', 'rb') as f:
+                training_results = pickle.load(f)
+            
+            st.subheader("📈 Training Results")
+            
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                st.metric("Final Validation AUROC", f"{training_results.get('final_val_auroc', 0):.4f}")
+                st.metric("Test AUROC", f"{training_results.get('test_auroc', 0):.4f}")
+            
+            with col2:
+                val_history = training_results.get('val_aurocs_history', [])
+                if val_history:
+                    st.line_chart(pd.DataFrame({
+                        'Epoch': range(len(val_history)),
+                        'Validation AUROC': val_history
+                    }).set_index('Epoch'))
+    
+    # Instructions
+    st.markdown("---")
+    st.markdown("""
+    ## 🚀 How DLRM Works for Book Recommendations
+    
+    **DLRM (Deep Learning Recommendation Model)** is specifically designed for recommendation systems and offers several advantages:
+    
+    ### 🏗️ Architecture Benefits:
+    - **Multi-feature Processing**: Handles both categorical (user ID, book ID, publisher) and numerical (age, ratings) features
+    - **Embedding Tables**: Learns rich representations for categorical features
+    - **Cross-feature Interactions**: Captures complex relationships between different features
+    - **Scalable Design**: Efficiently handles large-scale recommendation datasets
+    
+    ### 📊 Features Used:
+    **Categorical Features:**
+    - User ID, Book ID, Publisher, Country, Age Group, Publication Decade, Rating Level
+    
+    **Dense Features:**  
+    - Normalized Age, Publication Year, User Activity, Book Popularity, Average Ratings
+    
+    ### 🎯 Why DLRM vs LLM for Recommendations:
+    - **Purpose-built**: Specifically designed for recommendation systems
+    - **Feature Integration**: Better at combining diverse feature types
+    - **Scalability**: More efficient for large-scale recommendation tasks
+    - **Performance**: Higher accuracy for rating prediction tasks
+    - **Production Ready**: Optimized for real-time inference
+    
+    ### 💡 Best Use Cases:
+    - **Personalized Recommendations**: Based on user behavior and item characteristics
+    - **Rating Prediction**: Accurately predicts user preferences
+    - **Cold Start**: Handles new users and items through content features
+    - **Real-time Serving**: Fast inference for production systems
+    """)
+
+    with tab4:
+        st.header("📸 Book Gallery")
+        st.info("Browse book covers and discover new titles")
+        
+        # Gallery options
+        col1, col2 = st.columns([2, 1])
+        
+        with col1:
+            gallery_mode = st.selectbox(
+                "Choose gallery mode",
+                ["Popular Books", "Recent Publications", "Random Selection", "Search Results"]
+            )
+        
+        with col2:
+            books_per_row = st.slider("Books per row", 2, 6, 4)
+            max_books = st.slider("Maximum books", 10, 50, 20)
+        
+        # Get books based on selected mode
+        if gallery_mode == "Popular Books":
+            # Get most rated books
+            book_popularity = ratings_df.groupby('ISBN').size().sort_values(ascending=False)
+            gallery_books = books_df[books_df['ISBN'].isin(book_popularity.head(max_books).index)]
+            
+        elif gallery_mode == "Recent Publications":
+            # Get recent books
+            books_df_temp = books_df.copy()
+            books_df_temp['Year-Of-Publication'] = pd.to_numeric(books_df_temp['Year-Of-Publication'], errors='coerce')
+            recent_books = books_df_temp.sort_values('Year-Of-Publication', ascending=False, na_position='last')
+            gallery_books = recent_books.head(max_books)
+            
+        elif gallery_mode == "Random Selection":
+            # Random books
+            gallery_books = books_df.sample(min(max_books, len(books_df)))
+            
+        else:  # Search Results
+            search_query = st.text_input("Search books for gallery", placeholder="Enter title, author, or publisher")
+            if search_query:
+                mask = (
+                    books_df['Book-Title'].str.contains(search_query, case=False, na=False) |
+                    books_df['Book-Author'].str.contains(search_query, case=False, na=False) |
+                    books_df['Publisher'].str.contains(search_query, case=False, na=False)
+                )
+                gallery_books = books_df[mask].head(max_books)
+            else:
+                gallery_books = books_df.head(max_books)
+        
+        # Display gallery
+        if len(gallery_books) > 0:
+            st.markdown(f"**📚 Showing {len(gallery_books)} books**")
+            
+            # Create grid layout
+            books_list = gallery_books.to_dict('records')
+            
+            # Display books in rows
+            for i in range(0, len(books_list), books_per_row):
+                cols = st.columns(books_per_row)
+                
+                for j, col in enumerate(cols):
+                    if i + j < len(books_list):
+                        book = books_list[i + j]
+                        
+                        with col:
+                            # Book cover
+                            image_url = book.get('Image-URL-M', '')
+                            
+                            if image_url and pd.notna(image_url) and str(image_url) != 'nan':
+                                try:
+                                    clean_url = str(image_url).strip()
+                                    if clean_url and 'http' in clean_url:
+                                        st.image(clean_url, width='stretch')
+                                    else:
+                                        st.image("https://via.placeholder.com/150x200?text=📚&color=1f77b4&bg=f0f2f6", width='stretch')
+                                except:
+                                    st.image("https://via.placeholder.com/150x200?text=📚&color=1f77b4&bg=f0f2f6", width='stretch')
+                            else:
+                                st.image("https://via.placeholder.com/150x200?text=📚&color=1f77b4&bg=f0f2f6", width='stretch')
+                            
+                            # Book info
+                            title = book['Book-Title']
+                            if len(title) > 40:
+                                title = title[:37] + "..."
+                            
+                            author = book['Book-Author']
+                            if len(author) > 25:
+                                author = author[:22] + "..."
+                            
+                            st.markdown(f"**{title}**")
+                            st.write(f"*{author}*")
+                            st.write(f"📅 {book.get('Year-Of-Publication', 'Unknown')}")
+                            
+                            # Book statistics
+                            book_stats = ratings_df[ratings_df['ISBN'] == book['ISBN']]
+                            if len(book_stats) > 0:
+                                avg_rating = book_stats['Book-Rating'].mean()
+                                num_ratings = len(book_stats)
+                                st.write(f"⭐ {avg_rating:.1f}/10 ({num_ratings} ratings)")
+                            else:
+                                st.write("⭐ No ratings")
+                            
+                            # DLRM prediction button
+                            if recommender and recommender.model:
+                                if st.button(f"🎯 DLRM Score", key=f"dlrm_{book['ISBN']}"):
+                                    with st.spinner("Calculating..."):
+                                        # Use first user as example
+                                        sample_user = users_df['User-ID'].iloc[0]
+                                        dlrm_score = recommender.predict_rating(sample_user, book['ISBN'])
+                                        st.success(f"DLRM Score: {dlrm_score:.3f}")
+        else:
+            st.info("No books found for the selected criteria")
+        
+        # Quick stats
+        st.markdown("---")
+        st.subheader("📊 Gallery Statistics")
+        
+        col1, col2, col3, col4 = st.columns(4)
+        
+        with col1:
+            books_with_covers = sum(1 for _, book in gallery_books.iterrows()
+                                  if book.get('Image-URL-M') and pd.notna(book.get('Image-URL-M')))
+            st.metric("Books with Covers", f"{books_with_covers}/{len(gallery_books)}")
+        
+        with col2:
+            # Convert Year-Of-Publication to numeric, coercing errors to NaN
+            years = pd.to_numeric(gallery_books['Year-Of-Publication'], errors='coerce')
+            avg_year = years.mean()
+            st.metric("Average Publication Year", f"{avg_year:.0f}" if not pd.isna(avg_year) else "Unknown")
+        
+        with col3:
+            unique_authors = gallery_books['Book-Author'].nunique()
+            st.metric("Unique Authors", unique_authors)
+        
+        with col4:
+            unique_publishers = gallery_books['Publisher'].nunique()
+            st.metric("Unique Publishers", unique_publishers)
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if __name__ == "__main__":
+    main()