Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,723 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+# ============================
+# Image Caption Studio - Streamlit App
+# ============================
+
 import streamlit as st
+import torch
+from PIL import Image
+import transformers
+from transformers import BitsAndBytesConfig
+import time
+import warnings
+import random
+from pathlib import Path
+import base64
+import io
+warnings.filterwarnings('ignore')
 
-"""
-# Welcome to Streamlit!
+# ============================
+# PAGE CONFIGURATION
+# ============================
+
+st.set_page_config(
+    page_title="Image Caption Studio",
+    page_icon="🖼️",
+    layout="wide",
+    initial_sidebar_state="collapsed"
+)
+
+# ============================
+# CUSTOM CSS FOR BEAUTIFUL UI
+# ============================
+
+def load_css():
+    st.markdown("""
+    <style>
+    /* Main background */
+    .stApp {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    }
+    
+    /* Main container */
+    .main-container {
+        background: rgba(255, 255, 255, 0.95);
+        border-radius: 24px;
+        padding: 40px;
+        margin: 20px;
+        box-shadow: 0 20px 60px rgba(0,0,0,0.15);
+        backdrop-filter: blur(10px);
+    }
+    
+    /* Title styling */
+    .title-container {
+        text-align: center;
+        margin-bottom: 40px;
+        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        padding: 20px;
+        border-radius: 20px;
+    }
+    
+    .main-title {
+        font-size: 3.5rem !important;
+        font-weight: 800 !important;
+        margin-bottom: 10px !important;
+        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        text-shadow: 2px 2px 4px rgba(0,0,0,0.1);
+    }
+    
+    .subtitle {
+        font-size: 1.2rem !important;
+        color: #666 !important;
+        max-width: 800px;
+        margin: 0 auto;
+        line-height: 1.6;
+    }
+    
+    /* Logo container */
+    .logo-container {
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        gap: 20px;
+        margin-bottom: 20px;
+    }
+    
+    .logo-icon {
+        font-size: 4rem;
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+    }
+    
+    /* Image display */
+    .image-container {
+        border-radius: 20px;
+        overflow: hidden;
+        box-shadow: 0 10px 30px rgba(0,0,0,0.15);
+        margin: 30px auto;
+        max-width: 600px;
+        border: 3px solid #667eea;
+        position: relative;
+    }
+    
+    .image-container::before {
+        content: '';
+        position: absolute;
+        top: 0;
+        left: 0;
+        right: 0;
+        bottom: 0;
+        background: linear-gradient(45deg, transparent, rgba(102, 126, 234, 0.1));
+        z-index: 1;
+    }
+    
+    /* Cards */
+    .card {
+        background: white;
+        border-radius: 20px;
+        padding: 25px;
+        margin: 15px 0;
+        box-shadow: 0 8px 25px rgba(0,0,0,0.1);
+        border: 2px solid transparent;
+        transition: all 0.3s ease;
+    }
+    
+    .card:hover {
+        transform: translateY(-5px);
+        box-shadow: 0 15px 35px rgba(0,0,0,0.15);
+    }
+    
+    .short-card {
+        border-color: #4CAF50;
+        background: linear-gradient(135deg, #f8fff8 0%, #e8f5e9 100%);
+    }
+    
+    .tech-card {
+        border-color: #2196F3;
+        background: linear-gradient(135deg, #f8fbff 0%, #e3f2fd 100%);
+    }
+    
+    .human-card {
+        border-color: #FF9800;
+        background: linear-gradient(135deg, #fff8f8 0%, #fff3e0 100%);
+    }
+    
+    /* Badges */
+    .badge {
+        display: inline-block;
+        padding: 8px 16px;
+        border-radius: 50px;
+        font-weight: 600;
+        font-size: 0.9rem;
+        margin-bottom: 15px;
+    }
+    
+    .short-badge {
+        background: linear-gradient(135deg, #4CAF50 0%, #2E7D32 100%);
+        color: white;
+    }
+    
+    .tech-badge {
+        background: linear-gradient(135deg, #2196F3 0%, #0D47A1 100%);
+        color: white;
+    }
+    
+    .human-badge {
+        background: linear-gradient(135deg, #FF9800 0%, #EF6C00 100%);
+        color: white;
+    }
+    
+    /* Buttons */
+    .stButton > button {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        color: white;
+        border: none;
+        padding: 14px 28px;
+        border-radius: 50px;
+        font-weight: 600;
+        font-size: 1.1rem;
+        transition: all 0.3s ease;
+        width: 100%;
+        margin-top: 20px;
+        box-shadow: 0 4px 15px rgba(102, 126, 234, 0.3);
+    }
+    
+    .stButton > button:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 6px 20px rgba(102, 126, 234, 0.4);
+    }
+    
+    .upload-btn {
+        background: linear-gradient(135deg, #4CAF50 0%, #2E7D32 100%) !important;
+    }
+    
+    /* Progress bar */
+    .stProgress > div > div > div {
+        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+    }
+    
+    /* Sidebar */
+    .css-1d391kg {
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    }
+    
+    /* Success message */
+    .success-msg {
+        background: linear-gradient(135deg, #d4edda 0%, #c3e6cb 100%);
+        border: 2px solid #4CAF50;
+        border-radius: 15px;
+        padding: 20px;
+        margin: 20px 0;
+        text-align: center;
+        font-weight: 600;
+        color: #155724;
+    }
+    
+    /* Footer */
+    .footer {
+        text-align: center;
+        margin-top: 50px;
+        color: #666;
+        font-size: 0.9rem;
+        padding: 20px;
+        border-top: 2px solid #eee;
+    }
+    
+    /* Radio buttons */
+    .stRadio > div {
+        background: white;
+        padding: 20px;
+        border-radius: 15px;
+        box-shadow: 0 4px 15px rgba(0,0,0,0.05);
+    }
+    
+    /* Expander */
+    .streamlit-expanderHeader {
+        background: linear-gradient(135deg, #f8fbff 0%, #e3f2fd 100%) !important;
+        border-radius: 10px !important;
+        font-weight: 600 !important;
+    }
+    
+    /* Sliders */
+    .stSlider {
+        padding: 10px 0;
+    }
+    
+    /* Word counter */
+    .word-counter {
+        font-size: 0.9rem;
+        color: #666;
+        font-style: italic;
+        margin-top: 5px;
+    }
+    
+    /* Loading animation */
+    @keyframes pulse {
+        0% { transform: scale(1); }
+        50% { transform: scale(1.05); }
+        100% { transform: scale(1); }
+    }
+    
+    .loading {
+        animation: pulse 1.5s infinite;
+    }
+    
+    /* Responsive design */
+    @media (max-width: 768px) {
+        .main-title {
+            font-size: 2.5rem !important;
+        }
+        .main-container {
+            padding: 20px;
+            margin: 10px;
+        }
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# ============================
+# MODEL LOADING (CACHED)
+# ============================
+
+@st.cache_resource(show_spinner=False)
+def load_model():
+    """Load the Qwen2.5-VL model with aggressive optimization for free CPU."""
+    from transformers import BitsAndBytesConfig
+    import torch
+
+    model_id = "Qwen/Qwen2.5-VL-7B-Instruct"
+    
+    # AGGRESSIVE 4-bit quantization for CPU
+    quantization_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_compute_dtype=torch.float32,  # More stable on CPU
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_use_double_quant=True,
+    )
+    
+    try:
+        # KEY CHANGE: Force model to CPU and use memory mapping
+        model = transformers.Qwen2_5_VLForConditionalGeneration.from_pretrained(
+            model_id,
+            quantization_config=quantization_config,
+            device_map="cpu",  # <-- FORCE CPU
+            low_cpu_mem_usage=True,  # <-- CRITICAL for low memory
+            torch_dtype=torch.float32,
+            trust_remote_code=True
+        )
+        
+        processor = transformers.Qwen2_5_VLProcessor.from_pretrained(model_id)
+        st.success("✅ Model loaded in 4-bit (Optimized for CPU)")
+        return model, processor, "cpu"
+        
+    except Exception as e:
+        st.error(f"❌ Model loading failed on CPU: {e}")
+        # Fallback: Use a TINY model
+        st.info("🔄 Attempting to load a smaller model...")
+        return None, None, None
 
-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).
+# ============================
+# PROMPT TEMPLATES
+# ============================
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+class CaptionPrompts:
+    """Class containing prompt templates for different caption styles"""
+    
+    @staticmethod
+    def get_short_caption_prompt(word_limit=15):
+        """Generate short caption"""
+        return f"""<|im_start|>system
+You are an expert image captioning assistant. Generate a VERY SHORT caption describing the image.
+The caption should be concise, under {word_limit} words, and capture the main subject.
+Focus only on the most important elements.<|im_end|>
+<|im_start|>user
+<image>
+Describe this image in a single, very short sentence (under {word_limit} words).<|im_end|>
+<|im_start|>assistant
 """
+    
+    @staticmethod
+    def get_technical_caption_prompt(word_limit=35):
+        """Generate technical caption"""
+        return f"""<|im_start|>system
+You are a technical image analysis expert. Generate a detailed technical caption.
+Focus on objective observations, visual characteristics, composition, and technical aspects.
+Use precise terminology. Keep it under {word_limit} words.<|im_end|>
+<|im_start|>user
+<image>
+Provide a technical description of this image with precise observations (under {word_limit} words).<|im_end|>
+<|im_start|>assistant
+"""
+    
+    @staticmethod
+    def get_human_friendly_caption_prompt(word_limit=25):
+        """Generate human-friendly caption"""
+        return f"""<|im_start|>system
+You are a friendly storyteller. Generate an engaging, human-friendly caption.
+Make it descriptive, interesting, and easy to understand for general audiences.
+Use vivid language and keep it under {word_limit} words.<|im_end|>
+<|im_start|>user
+<image>
+Create a friendly, engaging caption for this image that tells a story (under {word_limit} words).<|im_end|>
+<|im_start|>assistant
+"""
+
+# ============================
+# CAPTION GENERATION FUNCTION
+# ============================
+
+def generate_caption(model, processor, device, image, prompt: str, max_new_tokens: int = 100) -> str:
+    """Generate caption for an image using the given prompt"""
+    try:
+        # Prepare inputs
+        messages = [
+            {"role": "user", "content": [
+                {"type": "image", "image": image},
+                {"type": "text", "text": prompt.split("<|im_start|>user\n")[-1].split("<|im_end|>")[0]}
+            ]}
+        ]
+        
+        text = processor.apply_chat_template(
+            messages, 
+            tokenize=False, 
+            add_generation_prompt=True
+        )
+        
+        # Prepare image inputs
+        image_inputs = processor(text=text, images=image, return_tensors="pt")
+        image_inputs = {k: v.to(device) for k, v in image_inputs.items()}
+        
+        # Generate caption
+        with torch.no_grad():
+            generated_ids = model.generate(
+                **image_inputs,
+                max_new_tokens=max_new_tokens,
+                do_sample=True,
+                temperature=0.7,
+                top_p=0.9,
+            )
+        
+        # Decode the generated text
+        generated_ids_trimmed = [
+            out_ids[len(in_ids):] 
+            for in_ids, out_ids in zip(image_inputs["input_ids"], generated_ids)
+        ]
+        
+        caption = processor.batch_decode(
+            generated_ids_trimmed, 
+            skip_special_tokens=True, 
+            clean_up_tokenization_spaces=True
+        )[0]
+        
+        return caption.strip()
+    
+    except Exception as e:
+        return f"Error generating caption: {str(e)}"
+
+# ============================
+# MAIN APP FUNCTION
+# ============================
+
+def main():
+    # Load custom CSS
+    load_css()
+    
+    # Main container
+    with st.container():
+        st.markdown('<div class="main-container">', unsafe_allow_html=True)
+        
+        # Header with logo and title
+        col1, col2, col3 = st.columns([1, 2, 1])
+        with col2:
+            st.markdown('<div class="logo-container">', unsafe_allow_html=True)
+            st.markdown('<span class="logo-icon">🖼️✨</span>', unsafe_allow_html=True)
+            st.markdown('</div>', unsafe_allow_html=True)
+            
+            st.markdown('<h1 class="main-title">Image Caption Studio</h1>', unsafe_allow_html=True)
+            st.markdown('<p class="subtitle">Transform your images into beautiful captions using advanced AI. Upload any image and get short, technical, and human-friendly captions instantly!</p>', unsafe_allow_html=True)
+        
+        # Load model (cached)
+        model, processor, device = load_model()
+        
+        if model is None:
+            st.error("⚠️ Model failed to load. Please refresh the page or check your connection.")
+            return
+        
+        # ============================
+        # SIDEBAR FOR SETTINGS
+        # ============================
+        
+        with st.sidebar:
+            st.markdown("## ⚙️ Settings")
+            
+            # Caption type selection
+            caption_type = st.radio(
+                "**Select Caption Style:**",
+                ["🎯 All Three Styles", "📝 Short Only", "🔬 Technical Only", "😊 Human-Friendly Only"],
+                help="Choose which caption styles to generate"
+            )
+            
+            # Advanced options expander
+            with st.expander("**⚙️ Advanced Options**", expanded=False):
+                st.markdown("### Word Limits")
+                
+                short_limit = st.slider(
+                    "**Short Caption Limit:**",
+                    min_value=5,
+                    max_value=25,
+                    value=15,
+                    help="Maximum words for short captions"
+                )
+                
+                tech_limit = st.slider(
+                    "**Technical Caption Limit:**",
+                    min_value=15,
+                    max_value=50,
+                    value=35,
+                    help="Maximum words for technical captions"
+                )
+                
+                human_limit = st.slider(
+                    "**Human-Friendly Limit:**",
+                    min_value=15,
+                    max_value=50,
+                    value=25,
+                    help="Maximum words for human-friendly captions"
+                )
+            
+            # Performance info
+            st.markdown("---")
+            st.markdown("### 📊 System Info")
+            st.info(f"**Device:** {device.upper()}\n\n**Model:** Qwen2.5-VL-7B\n\n**Status:** Ready ✅")
+        
+        # ============================
+        # MAIN CONTENT AREA
+        # ============================
+        
+        # Create two columns for layout
+        col_left, col_right = st.columns([1, 1])
+        
+        with col_left:
+            st.markdown("### 📤 Upload Your Image")
+            
+            # File uploader
+            uploaded_file = st.file_uploader(
+                "Choose an image...",
+                type=['jpg', 'jpeg', 'png', 'bmp', 'tiff'],
+                help="Supported formats: JPG, JPEG, PNG, BMP, TIFF"
+            )
+            
+            # Display uploaded image
+            if uploaded_file is not None:
+                try:
+                    image = Image.open(uploaded_file)
+                    
+                    # Resize for display
+                    max_size = (500, 500)
+                    image.thumbnail(max_size, Image.Resampling.LANCZOS)
+                    
+                    st.markdown('<div class="image-container">', unsafe_allow_html=True)
+                    st.image(image, use_column_width=True)
+                    st.markdown('</div>', unsafe_allow_html=True)
+                    
+                    # Image info
+                    st.success(f"✅ **{uploaded_file.name}** uploaded successfully!")
+                    st.caption(f"**Size:** {image.size[0]}x{image.size[1]} pixels | **Format:** {image.format}")
+                    
+                except Exception as e:
+                    st.error(f"Error loading image: {str(e)}")
+                    image = None
+            else:
+                # Display placeholder
+                st.markdown('<div class="image-container">', unsafe_allow_html=True)
+                st.image("https://via.placeholder.com/500x300/667eea/ffffff?text=Upload+an+Image", 
+                        use_column_width=True)
+                st.markdown('</div>', unsafe_allow_html=True)
+                st.info("👆 Upload an image to get started")
+                image = None
+        
+        with col_right:
+            st.markdown("### 🎨 Caption Settings")
+            
+            # Display current settings
+            if caption_type == "🎯 All Three Styles":
+                st.markdown("**Selected:** All caption styles")
+                cols = st.columns(3)
+                with cols[0]:
+                    st.markdown('<div class="badge short-badge">Short</div>', unsafe_allow_html=True)
+                with cols[1]:
+                    st.markdown('<div class="badge tech-badge">Technical</div>', unsafe_allow_html=True)
+                with cols[2]:
+                    st.markdown('<div class="badge human-badge">Human-Friendly</div>', unsafe_allow_html=True)
+            else:
+                st.markdown(f"**Selected:** {caption_type.split(' ')[1]}")
+            
+            # Generate button
+            generate_btn = st.button(
+                "🚀 Generate Captions",
+                type="primary",
+                disabled=uploaded_file is None,
+                use_container_width=True
+            )
+        
+        # ============================
+        # CAPTION GENERATION
+        # ============================
+        
+        if generate_btn and uploaded_file is not None and image is not None:
+            try:
+                # Progress bar
+                progress_bar = st.progress(0)
+                status_text = st.empty()
+                
+                # Generate captions based on selection
+                captions = {}
+                
+                if caption_type in ["🎯 All Three Styles", "📝 Short Only"]:
+                    status_text.text("🔍 Generating short caption...")
+                    short_prompt = CaptionPrompts.get_short_caption_prompt(short_limit)
+                    short_caption = generate_caption(model, processor, device, image, short_prompt, 50)
+                    
+                    # Enforce word limit
+                    short_words = short_caption.split()
+                    if len(short_words) > short_limit:
+                        short_caption = ' '.join(short_words[:short_limit]) + "..."
+                    captions['short'] = short_caption
+                    progress_bar.progress(33)
+                
+                if caption_type in ["🎯 All Three Styles", "🔬 Technical Only"]:
+                    status_text.text("🔬 Generating technical caption...")
+                    tech_prompt = CaptionPrompts.get_technical_caption_prompt(tech_limit)
+                    tech_caption = generate_caption(model, processor, device, image, tech_prompt, 100)
+                    
+                    # Enforce word limit
+                    tech_words = tech_caption.split()
+                    if len(tech_words) > tech_limit:
+                        tech_caption = ' '.join(tech_words[:tech_limit]) + "..."
+                    captions['technical'] = tech_caption
+                    progress_bar.progress(66 if caption_type == "🔬 Technical Only" else 66)
+                
+                if caption_type in ["🎯 All Three Styles", "😊 Human-Friendly Only"]:
+                    status_text.text("😊 Generating human-friendly caption...")
+                    human_prompt = CaptionPrompts.get_human_friendly_caption_prompt(human_limit)
+                    human_caption = generate_caption(model, processor, device, image, human_prompt, 100)
+                    
+                    # Enforce word limit
+                    human_words = human_caption.split()
+                    if len(human_words) > human_limit:
+                        human_caption = ' '.join(human_words[:human_limit]) + "..."
+                    captions['human'] = human_caption
+                    progress_bar.progress(100)
+                
+                status_text.text("✅ Captions generated successfully!")
+                time.sleep(0.5)
+                progress_bar.empty()
+                status_text.empty()
+                
+                # ============================
+                # DISPLAY RESULTS
+                # ============================
+                
+                st.markdown("---")
+                st.markdown("## 📋 Generated Captions")
+                
+                # Display appropriate cards
+                if 'short' in captions:
+                    st.markdown('<div class="card short-card loading">', unsafe_allow_html=True)
+                    st.markdown('<div class="badge short-badge">Short Caption</div>', unsafe_allow_html=True)
+                    st.markdown(f'**{captions["short"]}**')
+                    st.markdown(f'<div class="word-counter">📊 Words: {len(captions["short"].split())} / {short_limit}</div>', unsafe_allow_html=True)
+                    st.markdown('</div>', unsafe_allow_html=True)
+                
+                if 'technical' in captions:
+                    st.markdown('<div class="card tech-card loading">', unsafe_allow_html=True)
+                    st.markdown('<div class="badge tech-badge">Technical Caption</div>', unsafe_allow_html=True)
+                    st.markdown(f'**{captions["technical"]}**')
+                    st.markdown(f'<div class="word-counter">📊 Words: {len(captions["technical"].split())} / {tech_limit}</div>', unsafe_allow_html=True)
+                    st.markdown('</div>', unsafe_allow_html=True)
+                
+                if 'human' in captions:
+                    st.markdown('<div class="card human-card loading">', unsafe_allow_html=True)
+                    st.markdown('<div class="badge human-badge">Human-Friendly Caption</div>', unsafe_allow_html=True)
+                    st.markdown(f'**{captions["human"]}**')
+                    st.markdown(f'<div class="word-counter">📊 Words: {len(captions["human"].split())} / {human_limit}</div>', unsafe_allow_html=True)
+                    st.markdown('</div>', unsafe_allow_html=True)
+                
+                # Copy to clipboard button
+                if caption_type == "🎯 All Three Styles":
+                    all_captions = f"Short: {captions.get('short', '')}\n\nTechnical: {captions.get('technical', '')}\n\nHuman-Friendly: {captions.get('human', '')}"
+                else:
+                    all_captions = list(captions.values())[0]
+                
+                st.download_button(
+                    label="💾 Download All Captions",
+                    data=all_captions,
+                    file_name="captions.txt",
+                    mime="text/plain",
+                    use_container_width=True
+                )
+                
+                # Success message
+                st.markdown('<div class="success-msg">✨ Captions generated successfully! You can copy them or download as text.</div>', unsafe_allow_html=True)
+                
+            except Exception as e:
+                st.error(f"❌ Error generating captions: {str(e)}")
+        
+        # ============================
+        # FEATURES SECTION
+        # ============================
+        
+        st.markdown("---")
+        st.markdown("## ✨ Features")
+        
+        features_cols = st.columns(3)
+        
+        with features_cols[0]:
+            st.markdown("""
+            <div style="text-align: center; padding: 20px;">
+                <h3>🎯 Multiple Styles</h3>
+                <p>Short, technical, and human-friendly captions tailored to your needs</p>
+            </div>
+            """, unsafe_allow_html=True)
+        
+        with features_cols[1]:
+            st.markdown("""
+            <div style="text-align: center; padding: 20px;">
+                <h3>⚡ Fast & Accurate</h3>
+                <p>Powered by Qwen2.5-VL AI model for precise and quick results</p>
+            </div>
+            """, unsafe_allow_html=True)
+        
+        with features_cols[2]:
+            st.markdown("""
+            <div style="text-align: center; padding: 20px;">
+                <h3>🎨 Customizable</h3>
+                <p>Adjust word limits and choose specific caption styles</p>
+            </div>
+            """, unsafe_allow_html=True)
+        
+        # ============================
+        # FOOTER
+        # ============================
+        
+        st.markdown("---")
+        st.markdown('<div class="footer">', unsafe_allow_html=True)
+        st.markdown("""
+        <p>🎓 <strong>Final Year Project</strong> | MCA Department</p>
+        <p>🤖 Powered by Qwen2.5-VL AI Model | 🚀 Built with Streamlit</p>
+        <p>📧 Contact: student@college.edu | 🔗 GitHub Repository Available</p>
+        """, unsafe_allow_html=True)
+        st.markdown('</div>', unsafe_allow_html=True)
+        
+        st.markdown('</div>', unsafe_allow_html=True)  # Close main container
+
+# ============================
+# RUN THE APP
+# ============================
 
-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()