udpated perplexity factor

README.md

@@ -1,2 +1,281 @@
-# cult-scraper
+# Discord Data Analysis & Visualization Suite
 
+A comprehensive toolkit for scraping, processing, and analyzing Discord chat data with advanced visualization capabilities.
+
+## 🌟 Features
+
+### 📥 Data Collection
+- **Discord Bot Scraper**: Automated extraction of complete message history from Discord servers
+- **Image Downloader**: Downloads and processes images from Discord attachments with base64 conversion
+- **Text Embeddings**: Generate semantic embeddings for chat messages using sentence transformers
+
+### 📊 Visualization & Analysis
+- **Interactive Chat Visualizer**: 2D visualization of chat messages using dimensionality reduction (PCA, t-SNE)
+- **Clustering Analysis**: Automated grouping of similar messages with DBSCAN and HDBSCAN
+- **Image Dataset Viewer**: Browse and explore downloaded images by channel
+
+### 🔧 Data Processing
+- **Batch Processing**: Process multiple CSV files with embeddings
+- **Metadata Extraction**: Comprehensive message metadata including timestamps, authors, and content
+- **Data Filtering**: Advanced filtering by authors, channels, and timeframes
+
+## 📁 Repository Structure
+
+```
+cult-scraper-1/
+├── scripts/                          # Core data collection scripts
+│   ├── bot.py                        # Discord bot for message scraping
+│   ├── image_downloader.py           # Download and convert Discord images
+│   ├── embedder.py                   # Batch text embedding processor
+│   └── embed_class.py                # Text embedding utilities
+├── apps/                             # Interactive applications
+│   ├── cluster_map/                  # Chat message clustering & visualization
+│   │   ├── main.py                   # Main Streamlit application
+│   │   ├── data_loader.py            # Data loading utilities
+│   │   ├── clustering.py             # Clustering algorithms
+│   │   ├── visualization.py          # Plotting and visualization
+│   │   └── requirements.txt          # Dependencies
+│   └── image_viewer/                 # Image dataset browser
+│       ├── image_viewer.py           # Streamlit image viewer
+│       └── requirements.txt          # Dependencies
+├── discord_chat_logs/                # Exported CSV files from Discord
+└── images_dataset/                   # Downloaded images and metadata
+    └── images_dataset.json           # Image dataset with base64 data
+```
+
+## 🚀 Quick Start
+
+### 1. Discord Data Scraping
+
+First, set up and run the Discord bot to collect message data:
+
+```bash
+cd scripts
+# Configure your bot token in bot.py
+python bot.py
+```
+
+**Requirements:**
+- Discord bot token with message content intent enabled
+- Bot must have read permissions in target channels
+
+### 2. Generate Text Embeddings
+
+Process the collected chat data to add semantic embeddings:
+
+```bash
+cd scripts
+python embedder.py
+```
+
+This will:
+- Process all CSV files in `discord_chat_logs/`
+- Add embeddings to message content using sentence transformers
+- Save updated files with embedding vectors
+
+### 3. Download Images
+
+Extract and download images from Discord attachments:
+
+```bash
+cd scripts
+python image_downloader.py
+```
+
+Features:
+- Downloads images from attachment URLs
+- Converts to base64 for storage
+- Handles multiple image formats (PNG, JPG, GIF, WebP, etc.)
+- Implements retry logic and rate limiting
+
+### 4. Visualize Chat Data
+
+Launch the interactive chat visualization tool:
+
+```bash
+cd apps/cluster_map
+pip install -r requirements.txt
+streamlit run main.py
+```
+
+**Capabilities:**
+- 2D visualization using PCA or t-SNE
+- Interactive clustering with DBSCAN/HDBSCAN
+- Filter by channels, authors, and time periods
+- Hover to see message content and metadata
+
+### 5. Browse Image Dataset
+
+View downloaded images in an organized interface:
+
+```bash
+cd apps/image_viewer
+pip install -r requirements.txt
+streamlit run image_viewer.py
+```
+
+**Features:**
+- Channel-based organization
+- Navigation controls (previous/next)
+- Image metadata display
+- Responsive layout
+
+## 📋 Data Formats
+
+### Discord Chat Logs (CSV)
+```csv
+message_id,timestamp_utc,author_id,author_name,author_nickname,content,attachment_urls,embeds,content_embedding
+1234567890,2025-08-11 12:34:56,9876543210,username,nickname,"Hello world!","https://cdn.discord.com/...",{},"[0.123, -0.456, ...]"
+```
+
+### Image Dataset (JSON)
+```json
+{
+  "metadata": {
+    "created_at": "2025-08-11 12:34:56 UTC",
+    "summary": {
+      "total_images": 42,
+      "channels": ["memes", "general"],
+      "total_size_bytes": 1234567,
+      "file_extensions": [".png", ".jpg"],
+      "authors": ["user1", "user2"]
+    }
+  },
+  "images": [
+    {
+      "url": "https://cdn.discordapp.com/attachments/...",
+      "channel": "memes",
+      "author_name": "username",
+      "timestamp_utc": "2025-08-11 12:34:56+00:00",
+      "content": "Message text",
+      "file_extension": ".png",
+      "file_size": 54321,
+      "base64_data": "iVBORw0KGgoAAAANSUhEUgAA..."
+    }
+  ]
+}
+```
+
+## 🔧 Configuration
+
+### Discord Bot Setup
+1. Create a Discord application at https://discord.com/developers/applications
+2. Create a bot and copy the token
+3. Enable the following intents:
+   - Message Content Intent
+   - Server Members Intent (optional)
+4. Invite bot to your server with appropriate permissions
+
+### Environment Variables
+```bash
+# Set in scripts/bot.py
+BOT_TOKEN = "your_discord_bot_token_here"
+```
+
+### Embedding Models
+The system uses sentence-transformers models. Default: `all-MiniLM-L6-v2`
+
+Supported models:
+- `all-MiniLM-L6-v2` (lightweight, fast)
+- `all-mpnet-base-v2` (higher quality)
+- `sentence-transformers/all-roberta-large-v1` (best quality, slower)
+
+## 📊 Visualization Features
+
+### Chat Message Clustering
+- **Dimensionality Reduction**: PCA, t-SNE, UMAP
+- **Clustering Algorithms**: DBSCAN, HDBSCAN with automatic parameter tuning
+- **Interactive Controls**: Filter by source files, authors, and clusters
+- **Hover Information**: View message content, author, timestamp on hover
+
+### Image Analysis
+- **Channel Organization**: Browse images by Discord channel
+- **Metadata Display**: Author, timestamp, message context
+- **Navigation**: Previous/next controls with slider
+- **Format Support**: PNG, JPG, GIF, WebP, BMP, TIFF
+
+## 🛠️ Dependencies
+
+### Core Scripts
+- `discord.py` - Discord bot framework
+- `pandas` - Data manipulation
+- `sentence-transformers` - Text embeddings
+- `requests` - HTTP requests for image downloads
+
+### Visualization Apps
+- `streamlit` - Web interface framework
+- `plotly` - Interactive plotting
+- `scikit-learn` - Machine learning algorithms
+- `numpy` - Numerical computations
+- `umap-learn` - Dimensionality reduction
+- `hdbscan` - Density-based clustering
+
+## 📈 Use Cases
+
+### Research & Analytics
+- **Community Analysis**: Understand conversation patterns and topics
+- **Sentiment Analysis**: Track mood and sentiment over time
+- **User Behavior**: Analyze posting patterns and engagement
+- **Content Moderation**: Identify problematic content clusters
+
+### Data Science Projects
+- **NLP Research**: Experiment with text embeddings and clustering
+- **Social Network Analysis**: Study communication patterns
+- **Visualization Techniques**: Explore dimensionality reduction methods
+- **Image Processing**: Analyze visual content sharing patterns
+
+### Content Management
+- **Archive Creation**: Preserve Discord community history
+- **Content Discovery**: Find similar messages and discussions
+- **Moderation Tools**: Identify spam or inappropriate content
+- **Backup Solutions**: Create comprehensive data backups
+
+## 🔒 Privacy & Ethics
+
+- **Data Protection**: All processing happens locally
+- **User Consent**: Ensure proper permissions before scraping
+- **Compliance**: Follow Discord's Terms of Service
+- **Anonymization**: Consider removing or hashing user IDs for research
+
+## 🤝 Contributing
+
+1. Fork the repository
+2. Create a feature branch
+3. Make your changes
+4. Test thoroughly
+5. Submit a pull request
+
+## 📄 License
+
+This project is intended for educational and research purposes. Please ensure compliance with Discord's Terms of Service and applicable privacy laws when using this toolkit.
+
+## 🆘 Troubleshooting
+
+### Common Issues
+
+**Bot can't read messages:**
+- Ensure Message Content Intent is enabled
+- Check bot permissions in Discord server
+- Verify bot token is correct
+
+**Embeddings not generating:**
+- Install sentence-transformers: `pip install sentence-transformers`
+- Check available GPU memory for large models
+- Try a smaller model like `all-MiniLM-L6-v2`
+
+**Images not downloading:**
+- Check internet connectivity
+- Verify Discord CDN URLs are accessible
+- Increase retry limits for unreliable connections
+
+**Visualization not loading:**
+- Ensure all requirements are installed
+- Check that CSV files have embeddings
+- Try reducing dataset size for better performance
+
+## 📚 Additional Resources
+
+- [Discord.py Documentation](https://discordpy.readthedocs.io/)
+- [Sentence Transformers Models](https://www.sbert.net/docs/pretrained_models.html)
+- [Streamlit Documentation](https://docs.streamlit.io/)
+- [scikit-learn Clustering](https://scikit-learn.org/stable/modules/clustering.html)

apps/cluster_map/README.md

@@ -0,0 +1,58 @@
+# Discord Chat Embeddings Visualizer
+
+A Streamlit application that visualizes Discord chat messages using their vector embeddings in 2D space.
+
+## Features
+
+- **2D Visualization**: View chat messages plotted using PCA or t-SNE dimension reduction
+- **Interactive Plotting**: Hover over points to see message content, author, and timestamp
+- **Filtering**: Filter by source chat log files and authors
+- **Multiple Datasets**: Automatically loads all CSV files from the discord_chat_logs folder
+
+## Installation
+
+1. Install the required dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+
+Run the Streamlit application:
+
+```bash
+streamlit run streamlit_app.py
+```
+
+The app will automatically load all CSV files from the `../../discord_chat_logs/` directory.
+
+## Data Format
+
+The application expects CSV files with the following columns:
+- `message_id`: Unique identifier for the message
+- `timestamp_utc`: When the message was sent
+- `author_id`: Author's Discord ID
+- `author_name`: Author's username
+- `author_nickname`: Author's server nickname
+- `content`: The message content
+- `attachment_urls`: Any attached files
+- `embeds`: Embedded content
+- `content_embedding`: Vector embedding of the message content (as a string representation of a list)
+
+## Visualization Options
+
+- **PCA**: Principal Component Analysis - faster, good for getting an overview
+- **t-SNE**: t-Distributed Stochastic Neighbor Embedding - slower but may reveal better clusters
+
+## Controls
+
+- **Dimension Reduction Method**: Choose between PCA and t-SNE
+- **Filter by Source Files**: Select which chat log files to include
+- **Filter by Authors**: Select which authors to display
+- **Show Data Table**: View the underlying data in table format
+
+## Performance Notes
+
+- For large datasets, consider filtering by authors or source files to improve performance
+- t-SNE is computationally intensive and may take longer with large datasets
+- The app caches data and computations for better performance

apps/cluster_map/cluster.py

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+"""
+Discord Chat Embeddings Visualizer - Legacy Entry Point
+
+This file serves as a compatibility layer for the original cluster.py.
+The application has been refactored into modular components for better maintainability.
+"""
+
+# Import and run the main application
+from main import main
+
+if __name__ == "__main__":
+    main()

apps/cluster_map/clustering.py

@@ -0,0 +1,226 @@
+"""
+Clustering algorithms and evaluation metrics.
+"""
+
+import numpy as np
+import streamlit as st
+from sklearn.cluster import SpectralClustering, AgglomerativeClustering, OPTICS
+from sklearn.mixture import GaussianMixture
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import silhouette_score, calinski_harabasz_score
+import hdbscan
+import pandas as pd
+from collections import Counter
+import re
+from config import DEFAULT_RANDOM_STATE
+
+
+def summarize_cluster_content(cluster_messages, max_words=3):
+    """
+    Generate a meaningful name for a cluster based on its message content.
+    
+    Args:
+        cluster_messages: List of message contents in the cluster
+        max_words: Maximum number of words in the cluster name
+        
+    Returns:
+        str: Generated cluster name
+    """
+    if not cluster_messages:
+        return "Empty Cluster"
+    
+    # Combine all messages and clean text
+    all_text = " ".join([str(msg) for msg in cluster_messages if pd.notna(msg)])
+    if not all_text.strip():
+        return "Empty Content"
+    
+    # Basic text cleaning
+    text = all_text.lower()
+    
+    # Remove URLs, mentions, and special characters
+    text = re.sub(r'http[s]?://\S+', '', text)  # Remove URLs
+    text = re.sub(r'<@\d+>', '', text)  # Remove Discord mentions
+    text = re.sub(r'<:\w+:\d+>', '', text)  # Remove custom emojis
+    text = re.sub(r'[^\w\s]', ' ', text)  # Remove punctuation
+    text = re.sub(r'\s+', ' ', text).strip()  # Normalize whitespace
+    
+    if not text:
+        return "Special Characters"
+    
+    # Split into words and filter out common words
+    words = text.split()
+    
+    # Common stop words to filter out
+    stop_words = {
+        'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with',
+        'by', 'from', 'up', 'about', 'into', 'through', 'during', 'before', 'after',
+        'above', 'below', 'between', 'among', 'until', 'without', 'under', 'over',
+        'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had',
+        'do', 'does', 'did', 'will', 'would', 'could', 'should', 'may', 'might',
+        'i', 'you', 'he', 'she', 'it', 'we', 'they', 'me', 'him', 'her', 'us', 'them',
+        'my', 'your', 'his', 'her', 'its', 'our', 'their', 'this', 'that', 'these', 'those',
+        'just', 'like', 'get', 'know', 'think', 'see', 'go', 'come', 'say', 'said',
+        'yeah', 'yes', 'no', 'oh', 'ok', 'okay', 'well', 'so', 'but', 'if', 'when',
+        'what', 'where', 'why', 'how', 'who', 'which', 'than', 'then', 'now', 'here',
+        'there', 'also', 'too', 'very', 'really', 'pretty', 'much', 'more', 'most',
+        'some', 'any', 'all', 'many', 'few', 'little', 'big', 'small', 'good', 'bad'
+    }
+    
+    # Filter out stop words and very short/long words
+    filtered_words = [
+        word for word in words 
+        if word not in stop_words 
+        and len(word) >= 3 
+        and len(word) <= 15
+        and word.isalpha()  # Only alphabetic words
+    ]
+    
+    if not filtered_words:
+        return f"Chat ({len(cluster_messages)} msgs)"
+    
+    # Count word frequencies
+    word_counts = Counter(filtered_words)
+    
+    # Get most common words
+    most_common = word_counts.most_common(max_words * 2)  # Get more than needed for filtering
+    
+    # Select diverse words (avoid very similar words)
+    selected_words = []
+    for word, count in most_common:
+        # Avoid adding very similar words
+        if not any(word.startswith(existing[:4]) or existing.startswith(word[:4]) 
+                  for existing in selected_words):
+            selected_words.append(word)
+            if len(selected_words) >= max_words:
+                break
+    
+    if not selected_words:
+        return f"Discussion ({len(cluster_messages)} msgs)"
+    
+    # Create cluster name
+    cluster_name = " + ".join(selected_words[:max_words]).title()
+    
+    # Add message count for context
+    cluster_name += f" ({len(cluster_messages)})"
+    
+    return cluster_name
+
+
+def generate_cluster_names(filtered_df, cluster_labels):
+    """
+    Generate names for all clusters based on their content.
+    
+    Args:
+        filtered_df: DataFrame with message data
+        cluster_labels: Array of cluster labels for each message
+        
+    Returns:
+        dict: Mapping from cluster_id to cluster_name
+    """
+    if cluster_labels is None:
+        return {}
+    
+    cluster_names = {}
+    unique_clusters = np.unique(cluster_labels)
+    
+    for cluster_id in unique_clusters:
+        if cluster_id == -1:
+            cluster_names[cluster_id] = "Noise/Outliers"
+            continue
+            
+        # Get messages in this cluster
+        cluster_mask = cluster_labels == cluster_id
+        cluster_messages = filtered_df[cluster_mask]['content'].tolist()
+        
+        # Generate name
+        cluster_name = summarize_cluster_content(cluster_messages)
+        cluster_names[cluster_id] = cluster_name
+    
+    return cluster_names
+
+
+def apply_clustering(embeddings, clustering_method="None", n_clusters=5):
+    """
+    Apply clustering algorithm to embeddings and return labels and metrics.
+    
+    Args:
+        embeddings: High-dimensional embeddings to cluster
+        clustering_method: Name of clustering algorithm
+        n_clusters: Number of clusters (for methods that require it)
+        
+    Returns:
+        tuple: (cluster_labels, silhouette_score, calinski_harabasz_score)
+    """
+    if clustering_method == "None" or len(embeddings) <= n_clusters:
+        return None, None, None
+    
+    # Standardize embeddings for better clustering
+    scaler = StandardScaler()
+    scaled_embeddings = scaler.fit_transform(embeddings)
+    
+    cluster_labels = None
+    silhouette_avg = None
+    calinski_harabasz = None
+    
+    try:
+        if clustering_method == "HDBSCAN":
+            min_cluster_size = max(2, len(embeddings) // 20)  # Adaptive min cluster size
+            clusterer = hdbscan.HDBSCAN(min_cluster_size=min_cluster_size, 
+                                      min_samples=1, cluster_selection_epsilon=0.5)
+            cluster_labels = clusterer.fit_predict(scaled_embeddings)
+            
+        elif clustering_method == "Spectral Clustering":
+            clusterer = SpectralClustering(n_clusters=n_clusters, random_state=DEFAULT_RANDOM_STATE,
+                                         affinity='rbf', gamma=1.0)
+            cluster_labels = clusterer.fit_predict(scaled_embeddings)
+            
+        elif clustering_method == "Gaussian Mixture":
+            clusterer = GaussianMixture(n_components=n_clusters, random_state=DEFAULT_RANDOM_STATE,
+                                      covariance_type='full', max_iter=200)
+            cluster_labels = clusterer.fit_predict(scaled_embeddings)
+            
+        elif clustering_method == "Agglomerative (Ward)":
+            clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward')
+            cluster_labels = clusterer.fit_predict(scaled_embeddings)
+            
+        elif clustering_method == "Agglomerative (Complete)":
+            clusterer = AgglomerativeClustering(n_clusters=n_clusters, linkage='complete')
+            cluster_labels = clusterer.fit_predict(scaled_embeddings)
+            
+        elif clustering_method == "OPTICS":
+            min_samples = max(2, len(embeddings) // 50)
+            clusterer = OPTICS(min_samples=min_samples, xi=0.05, min_cluster_size=0.1)
+            cluster_labels = clusterer.fit_predict(scaled_embeddings)
+        
+        # Calculate clustering quality metrics
+        if cluster_labels is not None and len(np.unique(cluster_labels)) > 1:
+            # Only calculate if we have multiple clusters and no noise-only clustering
+            valid_labels = cluster_labels[cluster_labels != -1]  # Remove noise points for HDBSCAN/OPTICS
+            valid_embeddings = scaled_embeddings[cluster_labels != -1]
+            
+            if len(valid_labels) > 0 and len(np.unique(valid_labels)) > 1:
+                silhouette_avg = silhouette_score(valid_embeddings, valid_labels)
+                calinski_harabasz = calinski_harabasz_score(valid_embeddings, valid_labels)
+                
+    except Exception as e:
+        st.warning(f"Clustering failed: {str(e)}")
+        cluster_labels = None
+    
+    return cluster_labels, silhouette_avg, calinski_harabasz
+
+
+def get_cluster_statistics(cluster_labels):
+    """Get basic statistics about clustering results"""
+    if cluster_labels is None:
+        return {}
+    
+    unique_clusters = np.unique(cluster_labels)
+    n_clusters = len(unique_clusters[unique_clusters != -1])  # Exclude noise cluster (-1)
+    n_noise = np.sum(cluster_labels == -1)
+    
+    return {
+        "n_clusters": n_clusters,
+        "n_noise_points": n_noise,
+        "cluster_distribution": np.bincount(cluster_labels[cluster_labels != -1]) if n_clusters > 0 else [],
+        "unique_clusters": unique_clusters
+    }

apps/cluster_map/config.py

@@ -0,0 +1,75 @@
+"""
+Configuration settings and constants for the Discord Chat Embeddings Visualizer.
+"""
+
+# Application settings
+APP_TITLE = "The Cult - Visualised"
+APP_ICON = "🗨️"
+APP_LAYOUT = "wide"
+
+# File paths
+CHAT_LOGS_PATH = "../../discord_chat_logs"
+
+# Algorithm parameters
+DEFAULT_RANDOM_STATE = 42
+DEFAULT_N_COMPONENTS = 2
+DEFAULT_N_CLUSTERS = 5
+DEFAULT_DIMENSION_REDUCTION_METHOD = "t-SNE"
+DEFAULT_CLUSTERING_METHOD = "None"
+
+# Visualization settings
+DEFAULT_POINT_SIZE = 8
+DEFAULT_POINT_OPACITY = 0.7
+MAX_DISPLAYED_AUTHORS = 10
+MESSAGE_CONTENT_PREVIEW_LENGTH = 200
+MESSAGE_CONTENT_DISPLAY_LENGTH = 100
+
+# Performance thresholds
+LARGE_DATASET_WARNING_THRESHOLD = 1000
+
+# Color palettes
+PRIMARY_COLORS = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", 
+                  "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf"]
+
+# Clustering method categories
+CLUSTERING_METHODS_REQUIRING_N_CLUSTERS = [
+    "Spectral Clustering", 
+    "Gaussian Mixture", 
+    "Agglomerative (Ward)", 
+    "Agglomerative (Complete)"
+]
+
+COMPUTATIONALLY_INTENSIVE_METHODS = {
+    "dimension_reduction": ["t-SNE", "Spectral Embedding"],
+    "clustering": ["Spectral Clustering", "OPTICS"]
+}
+
+# Method explanations
+METHOD_EXPLANATIONS = {
+    "dimension_reduction": {
+        "PCA": "Linear, fast, preserves global variance",
+        "t-SNE": "Non-linear, good for local structure, slower",
+        "UMAP": "Balanced speed/quality, preserves local & global structure",
+        "Spectral Embedding": "Uses graph theory, good for non-convex clusters",
+        "Force-Directed": "Physics-based layout, creates natural spacing"
+    },
+    "clustering": {
+        "HDBSCAN": "Density-based, finds variable density clusters, handles noise",
+        "Spectral Clustering": "Uses eigenvalues, good for non-convex shapes",
+        "Gaussian Mixture": "Probabilistic, assumes gaussian distributions",
+        "Agglomerative (Ward)": "Hierarchical, minimizes within-cluster variance",
+        "Agglomerative (Complete)": "Hierarchical, minimizes maximum distance",
+        "OPTICS": "Density-based, finds clusters of varying densities"
+    },
+    "separation": {
+        "Spread Factor": "Applies repulsive forces between nearby points",
+        "Smart Jittering": "Adds intelligent noise to separate overlapping points",
+        "Density-Based Jittering": "Stronger separation in crowded areas",
+        "Perplexity Factor": "Controls t-SNE's focus on local vs global structure",
+        "Min Distance Factor": "Controls UMAP's point packing tightness"
+    },
+    "metrics": {
+        "Silhouette Score": "Higher is better (range: -1 to 1)",
+        "Calinski-Harabasz": "Higher is better, measures cluster separation"
+    }
+}

apps/cluster_map/data_loader.py

@@ -0,0 +1,86 @@
+"""
+Data loading and parsing utilities for Discord chat logs.
+"""
+
+import pandas as pd
+import numpy as np
+import streamlit as st
+import ast
+from pathlib import Path
+from config import CHAT_LOGS_PATH
+
+
+@st.cache_data
+def load_all_chat_data():
+    """Load all CSV files from the discord_chat_logs folder"""
+    chat_logs_path = Path(CHAT_LOGS_PATH)
+    
+    with st.expander("📁 Loading Details", expanded=False):
+        # Display the path for debugging
+        st.write(f"Looking for CSV files in: {chat_logs_path}")
+        st.write(f"Path exists: {chat_logs_path.exists()}")
+    
+        all_data = []
+        
+        for csv_file in chat_logs_path.glob("*.csv"):
+            try:
+                df = pd.read_csv(csv_file)
+                df['source_file'] = csv_file.stem  # Add source file name
+                all_data.append(df)
+                st.write(f"✅ Loaded {len(df)} messages from {csv_file.name}")
+            except Exception as e:
+                st.error(f"❌ Error loading {csv_file.name}: {e}")
+        
+        if all_data:
+            combined_df = pd.concat(all_data, ignore_index=True)
+            st.success(f"🎉 Successfully loaded {len(combined_df)} total messages from {len(all_data)} files")
+        else:
+            st.error("No data loaded!")
+            combined_df = pd.DataFrame()
+    
+    return combined_df if all_data else pd.DataFrame()
+
+
+@st.cache_data
+def parse_embeddings(df):
+    """Parse the content_embedding column from string to numpy array"""
+    embeddings = []
+    valid_indices = []
+    
+    for idx, embedding_str in enumerate(df['content_embedding']):
+        try:
+            # Parse the string representation of the list
+            embedding = ast.literal_eval(embedding_str)
+            if isinstance(embedding, list) and len(embedding) > 0:
+                embeddings.append(embedding)
+                valid_indices.append(idx)
+        except Exception as e:
+            continue
+    
+    embeddings_array = np.array(embeddings)
+    valid_df = df.iloc[valid_indices].copy()
+    
+    st.info(f"📊 Parsed {len(embeddings)} valid embeddings from {len(df)} messages")
+    st.info(f"🔢 Embedding dimension: {embeddings_array.shape[1] if len(embeddings) > 0 else 0}")
+    
+    return embeddings_array, valid_df
+
+
+def filter_data(df, selected_sources, selected_authors):
+    """Filter dataframe by selected sources and authors"""
+    if not selected_sources:
+        selected_sources = df['source_file'].unique()
+    
+    filtered_df = df[
+        (df['source_file'].isin(selected_sources)) &
+        (df['author_name'].isin(selected_authors))
+    ]
+    
+    return filtered_df
+
+
+def get_filtered_embeddings(embeddings, valid_df, filtered_df):
+    """Get embeddings corresponding to filtered dataframe"""
+    filtered_indices = filtered_df.index.tolist()
+    filtered_embeddings = embeddings[[i for i, idx in enumerate(valid_df.index) if idx in filtered_indices]]
+    return filtered_embeddings

apps/cluster_map/dimensionality_reduction.py

@@ -0,0 +1,211 @@
+"""
+Dimensionality reduction algorithms and point separation techniques.
+"""
+
+import numpy as np
+import streamlit as st
+from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE, SpectralEmbedding
+from sklearn.preprocessing import StandardScaler
+from sklearn.neighbors import NearestNeighbors
+from scipy.spatial.distance import pdist, squareform
+from scipy.optimize import minimize
+import umap
+from config import DEFAULT_RANDOM_STATE
+
+
+def apply_adaptive_spreading(embeddings, spread_factor=1.0):
+    """
+    Apply adaptive spreading to push apart nearby points while preserving global structure.
+    Uses a force-based approach where closer points repel more strongly.
+    """
+    if spread_factor <= 0:
+        return embeddings
+    
+    embeddings = embeddings.copy()
+    n_points = len(embeddings)
+    
+    print(f"DEBUG: Applying adaptive spreading to {n_points} points with factor {spread_factor}")
+    
+    if n_points < 2:
+        return embeddings
+    
+    # For very large datasets, skip spreading to avoid hanging
+    if n_points > 1000:
+        print(f"DEBUG: Large dataset ({n_points} points), skipping adaptive spreading...")
+        return embeddings
+    
+    # Calculate pairwise distances
+    distances = squareform(pdist(embeddings))
+    
+    # Apply force-based spreading with fewer iterations for large datasets
+    max_iterations = 3 if n_points > 500 else 5
+    
+    for iteration in range(max_iterations):
+        if iteration % 2 == 0:  # Progress indicator
+            print(f"DEBUG: Spreading iteration {iteration + 1}/{max_iterations}")
+            
+        forces = np.zeros_like(embeddings)
+        
+        for i in range(n_points):
+            for j in range(i + 1, n_points):
+                diff = embeddings[i] - embeddings[j]
+                dist = np.linalg.norm(diff)
+                
+                if dist > 0:
+                    # Repulsive force inversely proportional to distance
+                    force_magnitude = spread_factor / (dist ** 2 + 0.01)
+                    force_direction = diff / dist
+                    force = force_magnitude * force_direction
+                    
+                    forces[i] += force
+                    forces[j] -= force
+        
+        # Apply forces with damping
+        embeddings += forces * 0.1
+    
+    print(f"DEBUG: Adaptive spreading complete")
+    return embeddings
+
+
+def force_directed_layout(high_dim_embeddings, n_components=2, spread_factor=1.0):
+    """
+    Create a force-directed layout from high-dimensional embeddings.
+    This creates more natural spacing between similar points.
+    """
+    print(f"DEBUG: Starting force-directed layout with {len(high_dim_embeddings)} points...")
+    
+    # For large datasets, fall back to PCA + spreading to avoid hanging
+    if len(high_dim_embeddings) > 500:
+        print(f"DEBUG: Large dataset ({len(high_dim_embeddings)} points), using PCA + spreading instead...")
+        pca = PCA(n_components=n_components, random_state=DEFAULT_RANDOM_STATE)
+        result = pca.fit_transform(high_dim_embeddings)
+        return apply_adaptive_spreading(result, spread_factor)
+    
+    # Start with PCA as initial layout
+    pca = PCA(n_components=n_components, random_state=DEFAULT_RANDOM_STATE)
+    initial_layout = pca.fit_transform(high_dim_embeddings)
+    print(f"DEBUG: Initial PCA layout computed...")
+    
+    # For simplicity, just apply spreading to the PCA result
+    # The original optimization was too computationally intensive
+    result = apply_adaptive_spreading(initial_layout, spread_factor)
+    print(f"DEBUG: Force-directed layout complete...")
+    return result
+
+
+def calculate_local_density_scaling(embeddings, k=5):
+    """
+    Calculate local density scaling factors to emphasize differences in dense regions.
+    """
+    if len(embeddings) < k:
+        return np.ones(len(embeddings))
+    
+    # Find k nearest neighbors for each point
+    nn = NearestNeighbors(n_neighbors=k+1)  # +1 because first neighbor is the point itself
+    nn.fit(embeddings)
+    distances, indices = nn.kneighbors(embeddings)
+    
+    # Calculate local density (inverse of average distance to k nearest neighbors)
+    local_densities = 1.0 / (np.mean(distances[:, 1:], axis=1) + 1e-6)
+    
+    # Normalize densities
+    local_densities = (local_densities - np.min(local_densities)) / (np.max(local_densities) - np.min(local_densities) + 1e-6)
+    
+    return local_densities
+
+
+def apply_density_based_jittering(embeddings, density_scaling=True, jitter_strength=0.1):
+    """
+    Apply smart jittering that's stronger in dense regions to separate overlapping points.
+    """
+    if not density_scaling:
+        # Simple random jittering
+        noise = np.random.normal(0, jitter_strength, embeddings.shape)
+        return embeddings + noise
+    
+    # Calculate local densities
+    densities = calculate_local_density_scaling(embeddings)
+    
+    # Apply density-proportional jittering
+    jittered = embeddings.copy()
+    for i in range(len(embeddings)):
+        # More jitter in denser regions
+        jitter_amount = jitter_strength * (1 + densities[i])
+        noise = np.random.normal(0, jitter_amount, embeddings.shape[1])
+        jittered[i] += noise
+    
+    return jittered
+
+
+def reduce_dimensions(embeddings, method="PCA", n_components=2, spread_factor=1.0, 
+                     perplexity_factor=1.0, min_dist_factor=1.0):
+    """Apply dimensionality reduction with enhanced separation"""
+    
+    # Convert to numpy array if it's not already
+    embeddings = np.array(embeddings)
+    
+    print(f"DEBUG: Starting {method} with {len(embeddings)} embeddings, shape: {embeddings.shape}")
+    
+    # Standardize embeddings for better processing
+    scaler = StandardScaler()
+    scaled_embeddings = scaler.fit_transform(embeddings)
+    print(f"DEBUG: Embeddings standardized")
+    
+    # Apply the selected dimensionality reduction method
+    if method == "PCA":
+        print(f"DEBUG: Applying PCA...")
+        reducer = PCA(n_components=n_components, random_state=DEFAULT_RANDOM_STATE)
+        reduced_embeddings = reducer.fit_transform(scaled_embeddings)
+        # Apply spreading to PCA results
+        print(f"DEBUG: Applying spreading...")
+        reduced_embeddings = apply_adaptive_spreading(reduced_embeddings, spread_factor)
+        
+    elif method == "t-SNE":
+        # Adjust perplexity based on user preference and data size
+        base_perplexity = min(30, len(embeddings)-1)
+        adjusted_perplexity = max(5, min(50, int(base_perplexity * perplexity_factor)))
+        print(f"DEBUG: Applying t-SNE with perplexity {adjusted_perplexity}...")
+        
+        reducer = TSNE(n_components=n_components, random_state=DEFAULT_RANDOM_STATE, 
+                      perplexity=adjusted_perplexity, n_iter=1000,
+                      early_exaggeration=12.0 * spread_factor,  # Increase early exaggeration for more separation
+                      learning_rate='auto')
+        reduced_embeddings = reducer.fit_transform(scaled_embeddings)
+        
+    elif method == "UMAP":
+        # Adjust UMAP parameters for better local separation
+        n_neighbors = min(15, len(embeddings)-1)
+        min_dist = 0.1 * min_dist_factor
+        spread = 1.0 * spread_factor
+        print(f"DEBUG: Applying UMAP with n_neighbors={n_neighbors}, min_dist={min_dist}...")
+        
+        reducer = umap.UMAP(n_components=n_components, random_state=DEFAULT_RANDOM_STATE, 
+                           n_neighbors=n_neighbors, min_dist=min_dist,
+                           spread=spread, local_connectivity=2.0)
+        reduced_embeddings = reducer.fit_transform(scaled_embeddings)
+        
+    elif method == "Spectral Embedding":
+        n_neighbors = min(10, len(embeddings)-1)
+        print(f"DEBUG: Applying Spectral Embedding with n_neighbors={n_neighbors}...")
+        reducer = SpectralEmbedding(n_components=n_components, random_state=DEFAULT_RANDOM_STATE,
+                                   n_neighbors=n_neighbors)
+        reduced_embeddings = reducer.fit_transform(scaled_embeddings)
+        # Apply spreading to spectral results
+        print(f"DEBUG: Applying spreading...")
+        reduced_embeddings = apply_adaptive_spreading(reduced_embeddings, spread_factor)
+        
+    elif method == "Force-Directed":
+        # New method: Use force-directed layout for natural spreading
+        print(f"DEBUG: Applying Force-Directed layout...")
+        reduced_embeddings = force_directed_layout(scaled_embeddings, n_components, spread_factor)
+        
+    else:
+        # Fallback to PCA
+        print(f"DEBUG: Unknown method {method}, falling back to PCA...")
+        reducer = PCA(n_components=n_components, random_state=DEFAULT_RANDOM_STATE)
+        reduced_embeddings = reducer.fit_transform(scaled_embeddings)
+        reduced_embeddings = apply_adaptive_spreading(reduced_embeddings, spread_factor)
+    
+    print(f"DEBUG: Dimensionality reduction complete. Output shape: {reduced_embeddings.shape}")
+    return reduced_embeddings

apps/cluster_map/main.py

@@ -0,0 +1,169 @@
+"""
+Main application logic for the Discord Chat Embeddings Visualizer.
+"""
+
+import streamlit as st
+import warnings
+warnings.filterwarnings('ignore')
+
+# Import custom modules
+from ui_components import (
+    setup_page_config, display_title_and_description, get_all_ui_parameters,
+    display_performance_warnings
+)
+from data_loader import (
+    load_all_chat_data, parse_embeddings, filter_data, get_filtered_embeddings
+)
+from dimensionality_reduction import (
+    reduce_dimensions, apply_density_based_jittering
+)
+from clustering import apply_clustering, generate_cluster_names
+from visualization import (
+    create_visualization_plot, display_clustering_metrics, display_summary_stats,
+    display_clustering_results, display_data_table, display_cluster_summary
+)
+
+
+def main():
+    """Main application function"""
+    # Set up page configuration
+    setup_page_config()
+    
+    # Display title and description
+    display_title_and_description()
+    
+    # Load data
+    with st.spinner("Loading chat data..."):
+        df = load_all_chat_data()
+    
+    if df.empty:
+        st.error("No data could be loaded. Please check the data directory.")
+        st.stop()
+    
+    # Parse embeddings
+    with st.spinner("Parsing embeddings..."):
+        embeddings, valid_df = parse_embeddings(df)
+    
+    if len(embeddings) == 0:
+        st.error("No valid embeddings found!")
+        st.stop()
+    
+    # Get UI parameters
+    params = get_all_ui_parameters(valid_df)
+    
+    # Check if any sources are selected before proceeding
+    if not params['selected_sources']:
+        st.info("📂 **Select source files from the sidebar to begin visualization**")
+        st.markdown("### Available Data Sources:")
+        
+        # Show available sources as an informational table
+        source_info = []
+        for source in valid_df['source_file'].unique():
+            source_data = valid_df[valid_df['source_file'] == source]
+            source_info.append({
+                'Source File': source,
+                'Messages': len(source_data),
+                'Unique Authors': source_data['author_name'].nunique(),
+                'Date Range': f"{source_data['timestamp_utc'].min()} to {source_data['timestamp_utc'].max()}"
+            })
+        
+        import pandas as pd
+        source_df = pd.DataFrame(source_info)
+        st.dataframe(source_df, use_container_width=True, hide_index=True)
+        
+        st.markdown("👈 **Use the sidebar to select which sources to visualize**")
+        st.stop()
+    
+    # Filter data
+    filtered_df = filter_data(valid_df, params['selected_sources'], params['selected_authors'])
+    
+    if filtered_df.empty:
+        st.warning("No data matches the current filters! Try selecting different sources or authors.")
+        st.stop()
+    
+    # Display performance warnings
+    display_performance_warnings(filtered_df, params['method'], params['clustering_method'])
+    
+    # Get corresponding embeddings
+    filtered_embeddings = get_filtered_embeddings(embeddings, valid_df, filtered_df)
+    
+    st.info(f"📈 Visualizing {len(filtered_df)} messages")
+    
+    # Reduce dimensions
+    n_components = 3 if params['enable_3d'] else 2
+    with st.spinner(f"Reducing dimensions using {params['method']}..."):
+        reduced_embeddings = reduce_dimensions(
+            filtered_embeddings, 
+            method=params['method'],
+            n_components=n_components,
+            spread_factor=params['spread_factor'],
+            perplexity_factor=params['perplexity_factor'],
+            min_dist_factor=params['min_dist_factor']
+        )
+    
+    # Apply clustering
+    with st.spinner(f"Applying {params['clustering_method']}..."):
+        cluster_labels, silhouette_avg, calinski_harabasz = apply_clustering(
+            filtered_embeddings,
+            clustering_method=params['clustering_method'],
+            n_clusters=params['n_clusters']
+        )
+    
+    # Apply jittering if requested
+    if params['apply_jittering']:
+        with st.spinner("Applying smart jittering to separate overlapping points..."):
+            reduced_embeddings = apply_density_based_jittering(
+                reduced_embeddings, 
+                density_scaling=params['density_based_jitter'], 
+                jitter_strength=params['jitter_strength']
+            )
+    
+    # Generate cluster names if clustering was applied
+    cluster_names = None
+    if cluster_labels is not None:
+        with st.spinner("Generating cluster names..."):
+            cluster_names = generate_cluster_names(filtered_df, cluster_labels)
+    
+    # Display clustering metrics
+    display_clustering_metrics(
+        cluster_labels, silhouette_avg, calinski_harabasz, 
+        params['show_cluster_metrics']
+    )
+    
+    # Display cluster summary with names
+    if cluster_names:
+        display_cluster_summary(cluster_names, cluster_labels)
+    
+    # Create and display the main plot
+    fig = create_visualization_plot(
+        reduced_embeddings=reduced_embeddings,
+        filtered_df=filtered_df,
+        cluster_labels=cluster_labels,
+        selected_sources=params['selected_sources'] if params['selected_sources'] else None,
+        method=params['method'],
+        clustering_method=params['clustering_method'],
+        point_size=params['point_size'],
+        point_opacity=params['point_opacity'],
+        density_based_sizing=params['density_based_sizing'],
+        size_variation=params['size_variation'],
+        enable_3d=params['enable_3d'],
+        cluster_names=cluster_names
+    )
+    
+    st.plotly_chart(fig, use_container_width=True)
+    
+    # Display summary statistics
+    display_summary_stats(filtered_df, params['selected_sources'] or filtered_df['source_file'].unique())
+    
+    # Display clustering results and export options
+    display_clustering_results(
+        filtered_df, cluster_labels, reduced_embeddings, 
+        params['method'], params['clustering_method'], params['enable_3d']
+    )
+    
+    # Display data table
+    display_data_table(filtered_df, cluster_labels)
+
+
+if __name__ == "__main__":
+    main()

apps/cluster_map/requirements.txt

@@ -0,0 +1,8 @@
+streamlit>=1.28.0
+pandas>=1.5.0
+numpy>=1.24.0
+plotly>=5.15.0
+scikit-learn>=1.3.0
+umap-learn>=0.5.3
+hdbscan>=0.8.29
+scipy>=1.10.0

apps/cluster_map/test_debug.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+"""
+Test script to debug the hanging issue in the modular app
+"""
+
+import numpy as np
+import sys
+import os
+
+# Add the current directory to Python path
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+def test_dimensionality_reduction():
+    """Test dimensionality reduction functions"""
+    print("Testing dimensionality reduction functions...")
+    
+    from dimensionality_reduction import reduce_dimensions
+    
+    # Create test data similar to what we'd expect
+    n_samples = 796  # Same as the user's dataset
+    n_features = 384  # Common embedding dimension
+    
+    print(f"Creating test embeddings: {n_samples} x {n_features}")
+    test_embeddings = np.random.randn(n_samples, n_features)
+    
+    # Test PCA (should be fast)
+    print("Testing PCA...")
+    try:
+        result = reduce_dimensions(test_embeddings, method="PCA")
+        print(f"✓ PCA successful, output shape: {result.shape}")
+    except Exception as e:
+        print(f"✗ PCA failed: {e}")
+    
+    # Test UMAP (might be slower)
+    print("Testing UMAP...")
+    try:
+        result = reduce_dimensions(test_embeddings, method="UMAP")
+        print(f"✓ UMAP successful, output shape: {result.shape}")
+    except Exception as e:
+        print(f"✗ UMAP failed: {e}")
+
+if __name__ == "__main__":
+    test_dimensionality_reduction()

apps/cluster_map/ui_components.py

@@ -0,0 +1,267 @@
+"""
+Streamlit UI components and controls for the Discord Chat Embeddings Visualizer.
+"""
+
+import streamlit as st
+import numpy as np
+from config import (
+    APP_TITLE, APP_ICON, APP_LAYOUT, METHOD_EXPLANATIONS,
+    CLUSTERING_METHODS_REQUIRING_N_CLUSTERS, COMPUTATIONALLY_INTENSIVE_METHODS,
+    LARGE_DATASET_WARNING_THRESHOLD, MAX_DISPLAYED_AUTHORS,
+    DEFAULT_DIMENSION_REDUCTION_METHOD, DEFAULT_CLUSTERING_METHOD
+)
+
+
+def setup_page_config():
+    """Set up the Streamlit page configuration"""
+    st.set_page_config(
+        page_title=APP_TITLE,
+        page_icon=APP_ICON,
+        layout=APP_LAYOUT
+    )
+
+
+def display_title_and_description():
+    """Display the main title and description"""
+    st.title(f"{APP_ICON} {APP_TITLE}")
+    st.markdown("Explore Discord chat messages through their vector embeddings in 2D space")
+
+
+def create_method_controls():
+    """Create controls for dimension reduction and clustering methods"""
+    st.sidebar.header("🎛️ Visualization Controls")
+    
+    # 3D visualization toggle
+    enable_3d = st.sidebar.checkbox(
+        "Enable 3D Visualization", 
+        value=False,
+        help="Switch between 2D and 3D visualization. 3D uses 3 components instead of 2."
+    )
+    
+    # Dimension reduction method
+    method_options = ["PCA", "t-SNE", "UMAP", "Spectral Embedding", "Force-Directed"]
+    default_index = method_options.index(DEFAULT_DIMENSION_REDUCTION_METHOD) if DEFAULT_DIMENSION_REDUCTION_METHOD in method_options else 0
+    method = st.sidebar.selectbox(
+        "Dimension Reduction Method",
+        method_options,
+        index=default_index,
+        help="PCA is fastest, UMAP balances speed and quality, t-SNE and Spectral are slower but may reveal better structures. Force-Directed creates natural spacing."
+    )
+    
+    # Clustering method
+    clustering_options = ["None", "HDBSCAN", "Spectral Clustering", "Gaussian Mixture", 
+                         "Agglomerative (Ward)", "Agglomerative (Complete)", "OPTICS"]
+    clustering_default_index = clustering_options.index(DEFAULT_CLUSTERING_METHOD) if DEFAULT_CLUSTERING_METHOD in clustering_options else 0
+    clustering_method = st.sidebar.selectbox(
+        "Clustering Method",
+        clustering_options,
+        index=clustering_default_index,
+        help="Apply clustering to identify groups. HDBSCAN and OPTICS can find variable density clusters."
+    )
+    
+    return method, clustering_method, enable_3d
+
+
+def create_clustering_controls(clustering_method):
+    """Create controls for clustering parameters"""
+    # Always show the clusters slider, but indicate when it's used
+    if clustering_method in CLUSTERING_METHODS_REQUIRING_N_CLUSTERS:
+        help_text = "Number of clusters to create. This setting affects the clustering algorithm."
+        disabled = False
+    elif clustering_method == "None":
+        help_text = "Clustering is disabled. This setting has no effect."
+        disabled = True
+    else:
+        help_text = f"{clustering_method} automatically determines the number of clusters. This setting has no effect."
+        disabled = True
+    
+    n_clusters = st.sidebar.slider(
+        "Number of Clusters", 
+        min_value=2, 
+        max_value=20, 
+        value=5,
+        disabled=disabled,
+        help=help_text
+    )
+    
+    return n_clusters
+
+
+def create_separation_controls(method):
+    """Create controls for point separation and method-specific parameters"""
+    st.sidebar.subheader("🎯 Point Separation Controls")
+    
+    spread_factor = st.sidebar.slider(
+        "Spread Factor", 
+        0.5, 3.0, 1.0, 0.1,
+        help="Increase to spread apart nearby points. Higher values create more separation."
+    )
+    
+    # Method-specific parameters
+    perplexity_factor = 1.0
+    min_dist_factor = 1.0
+    
+    if method == "t-SNE":
+        perplexity_factor = st.sidebar.slider(
+            "Perplexity Factor", 
+            0.1, 2.0, 1.0, 0.1,
+            help="Affects local vs global structure balance. Lower values focus on local details."
+        )
+        
+    if method == "UMAP":
+        min_dist_factor = st.sidebar.slider(
+            "Min Distance Factor", 
+            0.1, 2.0, 1.0, 0.1,
+            help="Controls how tightly points are packed. Lower values create tighter clusters."
+        )
+    
+    return spread_factor, perplexity_factor, min_dist_factor
+
+
+def create_jittering_controls():
+    """Create controls for jittering options"""
+    apply_jittering = st.sidebar.checkbox(
+        "Apply Smart Jittering", 
+        value=False,
+        help="Add intelligent noise to separate overlapping points"
+    )
+    
+    jitter_strength = 0.1
+    density_based_jitter = True
+    
+    if apply_jittering:
+        jitter_strength = st.sidebar.slider(
+            "Jitter Strength", 
+            0.01, 0.5, 0.1, 0.01,
+            help="Strength of jittering. Higher values spread points more."
+        )
+        density_based_jitter = st.sidebar.checkbox(
+            "Density-Based Jittering", 
+            value=True,
+            help="Apply stronger jittering in dense regions"
+        )
+    
+    return apply_jittering, jitter_strength, density_based_jitter
+
+
+def create_advanced_options():
+    """Create advanced visualization options"""
+    with st.sidebar.expander("⚙️ Advanced Options"):
+        show_cluster_metrics = st.checkbox("Show Clustering Metrics", value=True)
+        point_size = st.slider("Point Size", 4, 15, 8)
+        point_opacity = st.slider("Point Opacity", 0.3, 1.0, 0.7)
+        
+        # Density-based visualization
+        density_based_sizing = st.checkbox(
+            "Density-Based Point Sizing", 
+            value=False,
+            help="Make points larger in sparse regions, smaller in dense regions"
+        )
+        
+        size_variation = 2.0
+        if density_based_sizing:
+            size_variation = st.slider(
+                "Size Variation Factor", 
+                1.5, 4.0, 2.0, 0.1,
+                help="How much point sizes vary based on local density"
+            )
+    
+    return show_cluster_metrics, point_size, point_opacity, density_based_sizing, size_variation
+
+
+def create_filter_controls(valid_df):
+    """Create controls for filtering data by source and author"""
+    # Source file filter
+    source_files = valid_df['source_file'].unique()
+    selected_sources = st.sidebar.multiselect(
+        "Filter by Source Files",
+        source_files,
+        default=[],
+        help="Select which chat log files to include"
+    )
+    
+    # Author filter
+    authors = valid_df['author_name'].unique()
+    default_authors = authors[:MAX_DISPLAYED_AUTHORS] if len(authors) > MAX_DISPLAYED_AUTHORS else authors
+    selected_authors = st.sidebar.multiselect(
+        "Filter by Authors",
+        authors,
+        default=default_authors,
+        help="Select which authors to include"
+    )
+    
+    return selected_sources, selected_authors
+
+
+def display_method_explanations():
+    """Display explanations for different methods"""
+    st.sidebar.markdown("---")
+    with st.sidebar.expander("📚 Method Explanations"):
+        st.markdown("**Dimensionality Reduction:**")
+        for method, explanation in METHOD_EXPLANATIONS["dimension_reduction"].items():
+            st.markdown(f"- **{method}**: {explanation}")
+        
+        st.markdown("\n**Clustering Methods:**")
+        for method, explanation in METHOD_EXPLANATIONS["clustering"].items():
+            st.markdown(f"- **{method}**: {explanation}")
+        
+        st.markdown("\n**Separation Techniques:**")
+        for technique, explanation in METHOD_EXPLANATIONS["separation"].items():
+            st.markdown(f"- **{technique}**: {explanation}")
+        
+        st.markdown("\n**Metrics:**")
+        for metric, explanation in METHOD_EXPLANATIONS["metrics"].items():
+            st.markdown(f"- **{metric}**: {explanation}")
+
+
+def display_performance_warnings(filtered_df, method, clustering_method):
+    """Display performance warnings for computationally intensive operations"""
+    if len(filtered_df) > LARGE_DATASET_WARNING_THRESHOLD:
+        if method in COMPUTATIONALLY_INTENSIVE_METHODS["dimension_reduction"]:
+            st.warning(f"⚠️ {method} with {len(filtered_df)} points may take several minutes to compute.")
+        if clustering_method in COMPUTATIONALLY_INTENSIVE_METHODS["clustering"]:
+            st.warning(f"⚠️ {clustering_method} with {len(filtered_df)} points may be computationally intensive.")
+
+
+def get_all_ui_parameters(valid_df):
+    """Get all UI parameters in a single function call"""
+    # Method selection
+    method, clustering_method, enable_3d = create_method_controls()
+    
+    # Clustering parameters
+    n_clusters = create_clustering_controls(clustering_method)
+    
+    # Separation controls
+    spread_factor, perplexity_factor, min_dist_factor = create_separation_controls(method)
+    
+    # Jittering controls
+    apply_jittering, jitter_strength, density_based_jitter = create_jittering_controls()
+    
+    # Advanced options
+    show_cluster_metrics, point_size, point_opacity, density_based_sizing, size_variation = create_advanced_options()
+    
+    # Filters
+    selected_sources, selected_authors = create_filter_controls(valid_df)
+    
+    # Method explanations
+    display_method_explanations()
+    
+    return {
+        'method': method,
+        'clustering_method': clustering_method,
+        'enable_3d': enable_3d,
+        'n_clusters': n_clusters,
+        'spread_factor': spread_factor,
+        'perplexity_factor': perplexity_factor,
+        'min_dist_factor': min_dist_factor,
+        'apply_jittering': apply_jittering,
+        'jitter_strength': jitter_strength,
+        'density_based_jitter': density_based_jitter,
+        'show_cluster_metrics': show_cluster_metrics,
+        'point_size': point_size,
+        'point_opacity': point_opacity,
+        'density_based_sizing': density_based_sizing,
+        'size_variation': size_variation,
+        'selected_sources': selected_sources,
+        'selected_authors': selected_authors
+    }

apps/cluster_map/visualization.py

@@ -0,0 +1,311 @@
+"""
+Visualization functions for creating interactive plots and displays.
+"""
+
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+import streamlit as st
+from dimensionality_reduction import calculate_local_density_scaling
+from config import MESSAGE_CONTENT_PREVIEW_LENGTH, DEFAULT_POINT_SIZE, DEFAULT_POINT_OPACITY
+
+
+def create_hover_text(df):
+    """Create hover text for plotly"""
+    hover_text = []
+    for _, row in df.iterrows():
+        text = f"<b>Author:</b> {row['author_name']}<br>"
+        text += f"<b>Timestamp:</b> {row['timestamp_utc']}<br>"
+        text += f"<b>Source:</b> {row['source_file']}<br>"
+        
+        # Handle potential NaN or non-string content
+        content = row['content']
+        if pd.isna(content) or content is None:
+            content_text = "[No content]"
+        else:
+            content_str = str(content)
+            content_text = content_str[:MESSAGE_CONTENT_PREVIEW_LENGTH] + ('...' if len(content_str) > MESSAGE_CONTENT_PREVIEW_LENGTH else '')
+        
+        text += f"<b>Content:</b> {content_text}"
+        hover_text.append(text)
+    return hover_text
+
+
+def calculate_point_sizes(reduced_embeddings, density_based_sizing=False, 
+                         point_size=DEFAULT_POINT_SIZE, size_variation=2.0):
+    """Calculate point sizes based on density if enabled"""
+    if not density_based_sizing:
+        return [point_size] * len(reduced_embeddings)
+    
+    local_densities = calculate_local_density_scaling(reduced_embeddings)
+    # Invert densities so sparse areas get larger points
+    inverted_densities = 1.0 - local_densities
+    # Scale point sizes
+    point_sizes = point_size * (1.0 + inverted_densities * (size_variation - 1.0))
+    return point_sizes
+
+
+def create_clustered_plot(reduced_embeddings, filtered_df, cluster_labels, hover_text, 
+                         point_sizes, point_opacity=DEFAULT_POINT_OPACITY, method="PCA", enable_3d=False,
+                         cluster_names=None):
+    """Create a plot colored by clusters"""
+    fig = go.Figure()
+    
+    unique_clusters = np.unique(cluster_labels)
+    colors = px.colors.qualitative.Set3 + px.colors.qualitative.Pastel
+    
+    for i, cluster_id in enumerate(unique_clusters):
+        cluster_mask = cluster_labels == cluster_id
+        if cluster_mask.any():
+            cluster_embeddings = reduced_embeddings[cluster_mask]
+            cluster_hover = [hover_text[j] for j, mask in enumerate(cluster_mask) if mask]
+            cluster_sizes = [point_sizes[j] for j, mask in enumerate(cluster_mask) if mask]
+            
+            # Use generated name if available, otherwise fall back to default
+            if cluster_names and cluster_id in cluster_names:
+                cluster_name = cluster_names[cluster_id]
+            else:
+                cluster_name = f"Cluster {cluster_id}" if cluster_id != -1 else "Noise"
+            
+            if enable_3d:
+                fig.add_trace(go.Scatter3d(
+                    x=cluster_embeddings[:, 0],
+                    y=cluster_embeddings[:, 1],
+                    z=cluster_embeddings[:, 2],
+                    mode='markers',
+                    name=cluster_name,
+                    marker=dict(
+                        size=cluster_sizes,
+                        color=colors[i % len(colors)],
+                        opacity=point_opacity,
+                        line=dict(width=1, color='white')
+                    ),
+                    hovertemplate='%{hovertext}<extra></extra>',
+                    hovertext=cluster_hover
+                ))
+            else:
+                fig.add_trace(go.Scatter(
+                    x=cluster_embeddings[:, 0],
+                    y=cluster_embeddings[:, 1],
+                    mode='markers',
+                    name=cluster_name,
+                    marker=dict(
+                        size=cluster_sizes,
+                        color=colors[i % len(colors)],
+                        opacity=point_opacity,
+                        line=dict(width=1, color='white')
+                    ),
+                    hovertemplate='%{hovertext}<extra></extra>',
+                    hovertext=cluster_hover
+                ))
+    
+    return fig
+
+
+def create_source_colored_plot(reduced_embeddings, filtered_df, selected_sources, hover_text, 
+                              point_sizes, point_opacity=DEFAULT_POINT_OPACITY, enable_3d=False):
+    """Create a plot colored by source files"""
+    fig = go.Figure()
+    colors = px.colors.qualitative.Set1
+    
+    for i, source in enumerate(selected_sources):
+        source_mask = filtered_df['source_file'] == source
+        if source_mask.any():
+            source_embeddings = reduced_embeddings[source_mask]
+            source_hover = [hover_text[j] for j, mask in enumerate(source_mask) if mask]
+            source_sizes = [point_sizes[j] for j, mask in enumerate(source_mask) if mask]
+            
+            if enable_3d:
+                fig.add_trace(go.Scatter3d(
+                    x=source_embeddings[:, 0],
+                    y=source_embeddings[:, 1],
+                    z=source_embeddings[:, 2],
+                    mode='markers',
+                    name=source,
+                    marker=dict(
+                        size=source_sizes,
+                        color=colors[i % len(colors)],
+                        opacity=point_opacity,
+                        line=dict(width=1, color='white')
+                    ),
+                    hovertemplate='%{hovertext}<extra></extra>',
+                    hovertext=source_hover
+                ))
+            else:
+                fig.add_trace(go.Scatter(
+                    x=source_embeddings[:, 0],
+                    y=source_embeddings[:, 1],
+                    mode='markers',
+                    name=source,
+                    marker=dict(
+                        size=source_sizes,
+                        color=colors[i % len(colors)],
+                        opacity=point_opacity,
+                        line=dict(width=1, color='white')
+                    ),
+                    hovertemplate='%{hovertext}<extra></extra>',
+                    hovertext=source_hover
+                ))
+    
+    return fig
+
+
+def create_visualization_plot(reduced_embeddings, filtered_df, cluster_labels=None, 
+                             selected_sources=None, method="PCA", clustering_method="None",
+                             point_size=DEFAULT_POINT_SIZE, point_opacity=DEFAULT_POINT_OPACITY,
+                             density_based_sizing=False, size_variation=2.0, enable_3d=False,
+                             cluster_names=None):
+    """Create the main visualization plot"""
+    
+    # Create hover text
+    hover_text = create_hover_text(filtered_df)
+    
+    # Calculate point sizes
+    point_sizes = calculate_point_sizes(reduced_embeddings, density_based_sizing, 
+                                       point_size, size_variation)
+    
+    # Create plot based on coloring strategy
+    if cluster_labels is not None:
+        fig = create_clustered_plot(reduced_embeddings, filtered_df, cluster_labels, 
+                                   hover_text, point_sizes, point_opacity, method, enable_3d,
+                                   cluster_names)
+    else:
+        if selected_sources is None:
+            selected_sources = filtered_df['source_file'].unique()
+        fig = create_source_colored_plot(reduced_embeddings, filtered_df, selected_sources, 
+                                        hover_text, point_sizes, point_opacity, enable_3d)
+    
+    # Update layout
+    title_suffix = f" with {clustering_method}" if clustering_method != "None" else ""
+    dimension_text = "3D" if enable_3d else "2D"
+    
+    if enable_3d:
+        fig.update_layout(
+            title=f"Discord Chat Messages - {method} {dimension_text} Visualization{title_suffix}",
+            scene=dict(
+                xaxis_title=f"{method} Component 1",
+                yaxis_title=f"{method} Component 2",
+                zaxis_title=f"{method} Component 3"
+            ),
+            width=1000,
+            height=700
+        )
+    else:
+        fig.update_layout(
+            title=f"Discord Chat Messages - {method} {dimension_text} Visualization{title_suffix}",
+            xaxis_title=f"{method} Component 1",
+            yaxis_title=f"{method} Component 2",
+            hovermode='closest',
+            width=1000,
+            height=700
+        )
+    
+    return fig
+
+
+def display_clustering_metrics(cluster_labels, silhouette_avg, calinski_harabasz, show_metrics=True):
+    """Display clustering quality metrics"""
+    if cluster_labels is not None and show_metrics:
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            n_clusters_found = len(np.unique(cluster_labels[cluster_labels != -1]))
+            st.metric("Clusters Found", n_clusters_found)
+        with col2:
+            if silhouette_avg is not None:
+                st.metric("Silhouette Score", f"{silhouette_avg:.3f}")
+            else:
+                st.metric("Silhouette Score", "N/A")
+        with col3:
+            if calinski_harabasz is not None:
+                st.metric("Calinski-Harabasz Index", f"{calinski_harabasz:.1f}")
+            else:
+                st.metric("Calinski-Harabasz Index", "N/A")
+
+
+def display_summary_stats(filtered_df, selected_sources):
+    """Display summary statistics"""
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        st.metric("Total Messages", len(filtered_df))
+    
+    with col2:
+        st.metric("Unique Authors", filtered_df['author_name'].nunique())
+    
+    with col3:
+        st.metric("Source Files", len(selected_sources))
+
+
+def display_clustering_results(filtered_df, cluster_labels, reduced_embeddings, method, clustering_method, enable_3d=False):
+    """Display clustering results and export options"""
+    if cluster_labels is None:
+        return
+        
+    st.subheader("📊 Clustering Results")
+    
+    # Add cluster information to dataframe for export
+    export_df = filtered_df.copy()
+    export_df['cluster_id'] = cluster_labels
+    export_df['x_coordinate'] = reduced_embeddings[:, 0]
+    export_df['y_coordinate'] = reduced_embeddings[:, 1]
+    
+    # Add z coordinate if 3D
+    if enable_3d and reduced_embeddings.shape[1] >= 3:
+        export_df['z_coordinate'] = reduced_embeddings[:, 2]
+    
+    # Show cluster distribution
+    cluster_dist = pd.Series(cluster_labels).value_counts().sort_index()
+    st.bar_chart(cluster_dist)
+    
+    # Download option
+    csv_data = export_df.to_csv(index=False)
+    dimension_text = "3D" if enable_3d else "2D"
+    st.download_button(
+        label="📥 Download Clustering Results (CSV)",
+        data=csv_data,
+        file_name=f"chat_clusters_{method}_{clustering_method}_{dimension_text}.csv",
+        mime="text/csv"
+    )
+
+
+def display_data_table(filtered_df, cluster_labels=None):
+    """Display the data table with optional clustering information"""
+    if not st.checkbox("Show Data Table"):
+        return
+        
+    st.subheader("📋 Message Data")
+    display_df = filtered_df[['timestamp_utc', 'author_name', 'source_file', 'content']].copy()
+    
+    # Add clustering info if available
+    if cluster_labels is not None:
+        display_df['cluster'] = cluster_labels
+        
+    display_df['content'] = display_df['content'].str[:100] + '...'  # Truncate for display
+    st.dataframe(display_df, use_container_width=True)
+
+
+def display_cluster_summary(cluster_names, cluster_labels):
+    """Display a summary of cluster names and their sizes"""
+    if not cluster_names or cluster_labels is None:
+        return
+        
+    st.subheader("🏷️ Cluster Summary")
+    
+    # Create summary data
+    cluster_summary = []
+    for cluster_id, name in cluster_names.items():
+        count = np.sum(cluster_labels == cluster_id)
+        cluster_summary.append({
+            'Cluster ID': cluster_id,
+            'Cluster Name': name,
+            'Message Count': count,
+            'Percentage': f"{100 * count / len(cluster_labels):.1f}%"
+        })
+    
+    # Sort by message count
+    cluster_summary.sort(key=lambda x: x['Message Count'], reverse=True)
+    
+    # Display as table
+    summary_df = pd.DataFrame(cluster_summary)
+    st.dataframe(summary_df, use_container_width=True, hide_index=True)

discord_chat_logs/newbotcommandscausefarmboisanoucnce.csv

@@ -1 +1 @@
-message_id,timestamp_utc,author_id,author_name,author_nickname,content,attachment_urls,embeds
+message_id,timestamp_utc,author_id,author_name,author_nickname,content,attachment_urls,embeds

discord_chat_logs/oop.csv

@@ -1 +1 @@
-message_id,timestamp_utc,author_id,author_name,author_nickname,content,attachment_urls,embeds
+message_id,timestamp_utc,author_id,author_name,author_nickname,content,attachment_urls,embeds

scripts/embedder.py

@@ -100,11 +100,11 @@ def process_csvs_in_directory(directory_path: str, model_name: str = 'all-MiniLM
 if __name__ == '__main__':
     # Define the directory where your CSV files are located.
     # The script will look for a folder named 'csv_data' in the current directory.
-    CSV_DIRECTORY = 'csv_data'
+    CSV_DIRECTORY = '../discord_chat_logs'
     
     # This function will create the 'csv_data' directory and some sample
     # files if they don't exist. You can comment this out if you have your own files.
-    create_sample_files(CSV_DIRECTORY)
+    # create_sample_files(CSV_DIRECTORY)
 
     # Run the main processing function on the directory
     process_csvs_in_directory(CSV_DIRECTORY)