add ci workflows (#1)

Reviewed-on: #1

.gitea/workflows/release.yml

@@ -0,0 +1,92 @@
+name: Release
+
+on:
+  push:
+    tags:
+      - 'v*'
+  workflow_dispatch:
+    inputs:
+      version:
+        description: 'Release version (e.g., v1.0.0)'
+        required: true
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+
+      - name: Set up Python
+        run: uv python install 3.11
+
+      - name: Install dependencies
+        run: uv sync --extra test
+
+      - name: Run full test suite
+        run: uv run pytest tests/ -v --cov=src/embeddingbuddy --cov-report=term-missing
+
+  build-and-release:
+    runs-on: ubuntu-latest
+    needs: test
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+
+      - name: Set up Python
+        run: uv python install 3.11
+
+      - name: Install dependencies
+        run: uv sync
+
+      - name: Build package
+        run: uv build
+
+      - name: Create release notes
+        run: |
+          echo "# Release Notes" > release-notes.md
+          echo "" >> release-notes.md
+          echo "## What's New" >> release-notes.md
+          echo "" >> release-notes.md
+          echo "- Modular architecture with improved testability" >> release-notes.md
+          echo "- Comprehensive test suite" >> release-notes.md
+          echo "- Enhanced documentation" >> release-notes.md
+          echo "- Security scanning and dependency management" >> release-notes.md
+          echo "" >> release-notes.md
+          echo "## Installation" >> release-notes.md
+          echo "" >> release-notes.md
+          echo '```bash' >> release-notes.md
+          echo 'uv sync' >> release-notes.md
+          echo 'uv run python main.py' >> release-notes.md
+          echo '```' >> release-notes.md
+
+      - name: Create Release
+        uses: actions/create-release@v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITEA_TOKEN }}
+        with:
+          tag_name: ${{ github.ref_name || github.event.inputs.version }}
+          release_name: Release ${{ github.ref_name || github.event.inputs.version }}
+          body_path: release-notes.md
+          draft: false
+          prerelease: false
+
+      - name: Upload Release Assets
+        uses: actions/upload-release-asset@v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITEA_TOKEN }}
+        with:
+          upload_url: ${{ steps.create_release.outputs.upload_url }}
+          asset_path: dist/
+          asset_name: embeddingbuddy-dist
+          asset_content_type: application/zip

.gitea/workflows/security.yml

@@ -0,0 +1,70 @@
+name: Security Scan
+
+on:
+  push:
+    branches: ["main", "master", "develop"]
+  pull_request:
+    branches: ["main", "master"]
+  schedule:
+    # Run security scan weekly on Sundays at 2 AM UTC
+    - cron: '0 2 * * 0'
+
+jobs:
+  security:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+
+      - name: Set up Python
+        run: uv python install 3.11
+
+      - name: Install dependencies
+        run: uv sync --extra security
+
+      - name: Run bandit security linter
+        run: uv run bandit -r src/ -f json -o bandit-report.json
+        continue-on-error: true
+
+      - name: Run safety vulnerability check
+        run: uv run safety check --json --save-json safety-report.json
+        continue-on-error: true
+
+      - name: Upload security reports
+        uses: actions/upload-artifact@v3
+        with:
+          name: security-reports
+          path: |
+            bandit-report.json
+            safety-report.json
+
+  dependency-check:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+
+      - name: Set up Python
+        run: uv python install 3.11
+
+      - name: Check for dependency vulnerabilities
+        run: |
+          uv sync --extra security
+          uv run pip-audit --format=json --output=pip-audit-report.json
+        continue-on-error: true
+
+      - name: Upload dependency audit report
+        uses: actions/upload-artifact@v3
+        with:
+          name: dependency-audit
+          path: pip-audit-report.json

.gitea/workflows/test.yml

@@ -0,0 +1,104 @@
+name: Test Suite
+
+on:
+  push:
+    branches: 
+      - "main"
+      - "develop"
+  pull_request:
+    branches: 
+      - "main"
+  workflow_dispatch:
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.11"]
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+
+      - name: Set up Python ${{ matrix.python-version }}
+        run: uv python install ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: uv sync --extra test
+
+      - name: Run tests with pytest
+        run: uv run pytest tests/ -v --tb=short
+
+      - name: Run tests with coverage
+        run: uv run pytest tests/ --cov=src/embeddingbuddy --cov-report=term-missing --cov-report=xml
+
+      - name: Upload coverage reports
+        uses: codecov/codecov-action@v4
+        if: matrix.python-version == '3.11'
+        with:
+          file: ./coverage.xml
+          fail_ci_if_error: false
+
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+
+      - name: Set up Python
+        run: uv python install 3.11
+
+      - name: Install dependencies
+        run: uv sync --extra lint
+
+      - name: Run ruff linter
+        run: uv run ruff check src/ tests/
+
+      - name: Run ruff formatter check
+        run: uv run ruff format --check src/ tests/
+
+      # TODO fix this it throws errors
+      # - name: Run mypy type checker
+      #   run: uv run mypy src/embeddingbuddy/ --ignore-missing-imports
+
+  build:
+    runs-on: ubuntu-latest
+    needs: [test, lint]
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v3
+        with:
+          version: "latest"
+
+      - name: Set up Python
+        run: uv python install 3.11
+
+      - name: Install dependencies
+        run: uv sync
+
+      - name: Build package
+        run: uv build
+
+      - name: Test installation
+        run: |
+          uv run python -c "from src.embeddingbuddy.app import create_app; app = create_app(); print('✅ Package builds and imports successfully')"
+
+      - name: Upload build artifacts
+        uses: actions/upload-artifact@v3
+        with:
+          name: dist-files
+          path: dist/

.gitignore

@@ -1,12 +1,84 @@
 # Python-generated files
 __pycache__/
 *.py[oc]
+*.py[cod]
+*$py.class
+*.so
+.Python
 build/
+develop-eggs/
 dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
 wheels/
-*.egg-info
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
 
 # Virtual environments
+.env
 .venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Project specific
+*.log
+.mypy_cache/
+.dmypy.json
+dmypy.json
 temp/
-todo/
+todo/
+
+# Security reports
+bandit-report.json
+safety-report.json
+pip-audit-report.json
+
+# Temporary files
+*.tmp

CLAUDE.md

@@ -30,9 +30,28 @@ The app will be available at http://127.0.0.1:8050
 **Run tests:**
 
 ```bash
+uv sync --extra test
 uv run pytest tests/ -v
 ```
 
+**Development tools:**
+
+```bash
+# Install all dev dependencies
+uv sync --extra dev
+
+# Linting and formatting
+uv run ruff check src/ tests/
+uv run ruff format src/ tests/
+
+# Type checking
+uv run mypy src/embeddingbuddy/
+
+# Security scanning
+uv run bandit -r src/
+uv run safety check
+```
+
 **Test with sample data:**
 Use the included `sample_data.ndjson` and `sample_prompts.ndjson` files for testing the application functionality.
 
@@ -42,7 +61,7 @@ Use the included `sample_data.ndjson` and `sample_prompts.ndjson` files for test
 
 The application follows a modular architecture with clear separation of concerns:
 
-```
+```text
 src/embeddingbuddy/
 ├── app.py              # Main application entry point and factory
 ├── main.py             # Application runner
@@ -72,27 +91,32 @@ src/embeddingbuddy/
 ### Key Components
 
 **Data Layer:**
+
 - `data/parser.py` - NDJSON parsing with error handling
 - `data/processor.py` - Data transformation and combination logic
 - `models/schemas.py` - Dataclasses for type safety and validation
 
 **Algorithm Layer:**
+
 - `models/reducers.py` - Modular dimensionality reduction with factory pattern
 - Supports PCA, t-SNE (openTSNE), and UMAP algorithms
 - Abstract base class for easy extension
 
 **Visualization Layer:**
+
 - `visualization/plots.py` - Plot factory with single and dual plot support
 - `visualization/colors.py` - Color mapping and grayscale conversion utilities
 - Plotly-based 2D/3D scatter plots with interactive features
 
 **UI Layer:**
+
 - `ui/layout.py` - Main application layout composition
 - `ui/components/` - Reusable, testable UI components
 - `ui/callbacks/` - Organized callbacks grouped by functionality
 - Bootstrap-styled sidebar with controls and large visualization area
 
 **Configuration:**
+
 - `config/settings.py` - Centralized settings with environment variable support
 - Plot styling, marker configurations, and app-wide constants
 
@@ -112,16 +136,19 @@ Optional fields: `id`, `category`, `subcategory`, `tags`
 The refactored callback system is organized by functionality:
 
 **Data Processing (`ui/callbacks/data_processing.py`):**
+
 - File upload handling
 - NDJSON parsing and validation
 - Data storage in dcc.Store components
 
 **Visualization (`ui/callbacks/visualization.py`):**
+
 - Dimensionality reduction pipeline
 - Plot generation and updates
 - Method/parameter change handling
 
 **Interactions (`ui/callbacks/interactions.py`):**
+
 - Point click handling and detail display
 - Reset functionality
 - User interaction management
@@ -131,15 +158,18 @@ The refactored callback system is organized by functionality:
 The modular design enables comprehensive testing:
 
 **Unit Tests:**
+
 - `tests/test_data_processing.py` - Parser and processor logic
 - `tests/test_reducers.py` - Dimensionality reduction algorithms
 - `tests/test_visualization.py` - Plot creation and color mapping
 
 **Integration Tests:**
+
 - End-to-end data pipeline testing
 - Component integration verification
 
 **Key Testing Benefits:**
+
 - Fast test execution (milliseconds vs seconds)
 - Isolated component testing
 - Easy mocking and fixture creation
@@ -167,6 +197,7 @@ Uses modern Python stack with uv for dependency management:
 5. **Tests** - Write tests for all new functionality
 
 **Code Organization Principles:**
+
 - Single responsibility principle
 - Clear module boundaries  
 - Testable, isolated components
@@ -174,7 +205,8 @@ Uses modern Python stack with uv for dependency management:
 - Error handling at appropriate layers
 
 **Testing Requirements:**
+
 - Unit tests for all core logic
 - Integration tests for data flow
 - Component tests for UI elements
-- Maintain high test coverage
+- Maintain high test coverage

pyproject.toml

@@ -14,7 +14,28 @@ dependencies = [
     "umap-learn>=0.5.8",
     "numba>=0.56.4",
     "openTSNE>=1.0.0",
+    "mypy>=1.17.1",
+]
+
+[project.optional-dependencies]
+test = [
     "pytest>=8.4.1",
+    "pytest-cov>=4.1.0",
+]
+lint = [
+    "ruff>=0.1.0",
+    "mypy>=1.5.0",
+]
+security = [
+    "bandit[toml]>=1.7.5",
+    "safety>=2.3.0",
+    "pip-audit>=2.6.0",
+]
+dev = [
+    "embeddingbuddy[test,lint,security]",
+]
+all = [
+    "embeddingbuddy[test,lint,security]",
 ]
 
 [build-system]

src/embeddingbuddy/__init__.py

@@ -1,3 +1,3 @@
 """EmbeddingBuddy - Interactive exploration and visualization of embedding vectors."""
 
-__version__ = "0.1.0"
+__version__ = "0.1.0"

src/embeddingbuddy/app.py

@@ -8,32 +8,29 @@ from .ui.callbacks.interactions import InteractionCallbacks
 
 
 def create_app():
-    app = dash.Dash(
-        __name__, 
-        external_stylesheets=[dbc.themes.BOOTSTRAP]
-    )
-    
+    app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
+
     layout_manager = AppLayout()
     app.layout = layout_manager.create_layout()
-    
+
     DataProcessingCallbacks()
     VisualizationCallbacks()
     InteractionCallbacks()
-    
+
     return app
 
 
 def run_app(app=None, debug=None, host=None, port=None):
     if app is None:
         app = create_app()
-    
+
     app.run(
         debug=debug if debug is not None else AppSettings.DEBUG,
         host=host if host is not None else AppSettings.HOST,
-        port=port if port is not None else AppSettings.PORT
+        port=port if port is not None else AppSettings.PORT,
     )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     app = create_app()
-    run_app(app)
+    run_app(app)

src/embeddingbuddy/config/settings.py

@@ -3,105 +3,100 @@ import os
 
 
 class AppSettings:
-    
     # UI Configuration
     UPLOAD_STYLE = {
-        'width': '100%',
-        'height': '60px',
-        'lineHeight': '60px',
-        'borderWidth': '1px',
-        'borderStyle': 'dashed',
-        'borderRadius': '5px',
-        'textAlign': 'center',
-        'margin-bottom': '20px'
+        "width": "100%",
+        "height": "60px",
+        "lineHeight": "60px",
+        "borderWidth": "1px",
+        "borderStyle": "dashed",
+        "borderRadius": "5px",
+        "textAlign": "center",
+        "margin-bottom": "20px",
     }
-    
-    PROMPTS_UPLOAD_STYLE = {
-        **UPLOAD_STYLE,
-        'borderColor': '#28a745'
-    }
-    
-    PLOT_CONFIG = {
-        'responsive': True,
-        'displayModeBar': True
-    }
-    
-    PLOT_STYLE = {
-        'height': '85vh',
-        'width': '100%'
-    }
-    
+
+    PROMPTS_UPLOAD_STYLE = {**UPLOAD_STYLE, "borderColor": "#28a745"}
+
+    PLOT_CONFIG = {"responsive": True, "displayModeBar": True}
+
+    PLOT_STYLE = {"height": "85vh", "width": "100%"}
+
     PLOT_LAYOUT_CONFIG = {
-        'height': None,
-        'autosize': True,
-        'margin': dict(l=0, r=0, t=50, b=0)
+        "height": None,
+        "autosize": True,
+        "margin": dict(l=0, r=0, t=50, b=0),
     }
-    
+
     # Dimensionality Reduction Settings
     DEFAULT_N_COMPONENTS_3D = 3
     DEFAULT_N_COMPONENTS_2D = 2
     DEFAULT_RANDOM_STATE = 42
-    
+
     # Available Methods
     REDUCTION_METHODS = [
-        {'label': 'PCA', 'value': 'pca'},
-        {'label': 't-SNE', 'value': 'tsne'},
-        {'label': 'UMAP', 'value': 'umap'}
+        {"label": "PCA", "value": "pca"},
+        {"label": "t-SNE", "value": "tsne"},
+        {"label": "UMAP", "value": "umap"},
     ]
-    
+
     COLOR_OPTIONS = [
-        {'label': 'Category', 'value': 'category'},
-        {'label': 'Subcategory', 'value': 'subcategory'},
-        {'label': 'Tags', 'value': 'tags'}
+        {"label": "Category", "value": "category"},
+        {"label": "Subcategory", "value": "subcategory"},
+        {"label": "Tags", "value": "tags"},
     ]
-    
-    DIMENSION_OPTIONS = [
-        {'label': '2D', 'value': '2d'},
-        {'label': '3D', 'value': '3d'}
-    ]
-    
+
+    DIMENSION_OPTIONS = [{"label": "2D", "value": "2d"}, {"label": "3D", "value": "3d"}]
+
     # Default Values
-    DEFAULT_METHOD = 'pca'
-    DEFAULT_COLOR_BY = 'category'
-    DEFAULT_DIMENSIONS = '3d'
-    DEFAULT_SHOW_PROMPTS = ['show']
-    
+    DEFAULT_METHOD = "pca"
+    DEFAULT_COLOR_BY = "category"
+    DEFAULT_DIMENSIONS = "3d"
+    DEFAULT_SHOW_PROMPTS = ["show"]
+
     # Plot Marker Settings
     DOCUMENT_MARKER_SIZE_2D = 8
     DOCUMENT_MARKER_SIZE_3D = 5
     PROMPT_MARKER_SIZE_2D = 10
     PROMPT_MARKER_SIZE_3D = 6
-    
-    DOCUMENT_MARKER_SYMBOL = 'circle'
-    PROMPT_MARKER_SYMBOL = 'diamond'
-    
+
+    DOCUMENT_MARKER_SYMBOL = "circle"
+    PROMPT_MARKER_SYMBOL = "diamond"
+
     DOCUMENT_OPACITY = 1.0
     PROMPT_OPACITY = 0.8
-    
+
     # Text Processing
     TEXT_PREVIEW_LENGTH = 100
-    
+
     # App Configuration
-    DEBUG = os.getenv('EMBEDDINGBUDDY_DEBUG', 'True').lower() == 'true'
-    HOST = os.getenv('EMBEDDINGBUDDY_HOST', '127.0.0.1')
-    PORT = int(os.getenv('EMBEDDINGBUDDY_PORT', '8050'))
-    
+    DEBUG = os.getenv("EMBEDDINGBUDDY_DEBUG", "True").lower() == "true"
+    HOST = os.getenv("EMBEDDINGBUDDY_HOST", "127.0.0.1")
+    PORT = int(os.getenv("EMBEDDINGBUDDY_PORT", "8050"))
+
     # Bootstrap Theme
-    EXTERNAL_STYLESHEETS = ['https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css']
-    
+    EXTERNAL_STYLESHEETS = [
+        "https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css"
+    ]
+
     @classmethod
-    def get_plot_marker_config(cls, dimensions: str, is_prompt: bool = False) -> Dict[str, Any]:
+    def get_plot_marker_config(
+        cls, dimensions: str, is_prompt: bool = False
+    ) -> Dict[str, Any]:
         if is_prompt:
-            size = cls.PROMPT_MARKER_SIZE_3D if dimensions == '3d' else cls.PROMPT_MARKER_SIZE_2D
+            size = (
+                cls.PROMPT_MARKER_SIZE_3D
+                if dimensions == "3d"
+                else cls.PROMPT_MARKER_SIZE_2D
+            )
             symbol = cls.PROMPT_MARKER_SYMBOL
             opacity = cls.PROMPT_OPACITY
         else:
-            size = cls.DOCUMENT_MARKER_SIZE_3D if dimensions == '3d' else cls.DOCUMENT_MARKER_SIZE_2D
+            size = (
+                cls.DOCUMENT_MARKER_SIZE_3D
+                if dimensions == "3d"
+                else cls.DOCUMENT_MARKER_SIZE_2D
+            )
             symbol = cls.DOCUMENT_MARKER_SYMBOL
             opacity = cls.DOCUMENT_OPACITY
-        
-        return {
-            'size': size,
-            'symbol': symbol,
-            'opacity': opacity
-        }
+
+        return {"size": size, "symbol": symbol, "opacity": opacity}

src/embeddingbuddy/data/parser.py

@@ -1,39 +1,38 @@
 import json
 import uuid
 import base64
-from typing import List, Union
-from ..models.schemas import Document, ProcessedData
+from typing import List
+from ..models.schemas import Document
 
 
 class NDJSONParser:
-    
     @staticmethod
     def parse_upload_contents(contents: str) -> List[Document]:
-        content_type, content_string = contents.split(',')
+        content_type, content_string = contents.split(",")
         decoded = base64.b64decode(content_string)
-        text_content = decoded.decode('utf-8')
+        text_content = decoded.decode("utf-8")
         return NDJSONParser.parse_text(text_content)
-    
+
     @staticmethod
     def parse_text(text_content: str) -> List[Document]:
         documents = []
-        for line in text_content.strip().split('\n'):
+        for line in text_content.strip().split("\n"):
             if line.strip():
                 doc_dict = json.loads(line)
                 doc = NDJSONParser._dict_to_document(doc_dict)
                 documents.append(doc)
         return documents
-    
+
     @staticmethod
     def _dict_to_document(doc_dict: dict) -> Document:
-        if 'id' not in doc_dict:
-            doc_dict['id'] = str(uuid.uuid4())
-        
+        if "id" not in doc_dict:
+            doc_dict["id"] = str(uuid.uuid4())
+
         return Document(
-            id=doc_dict['id'],
-            text=doc_dict['text'],
-            embedding=doc_dict['embedding'],
-            category=doc_dict.get('category'),
-            subcategory=doc_dict.get('subcategory'),
-            tags=doc_dict.get('tags')
-        )
+            id=doc_dict["id"],
+            text=doc_dict["text"],
+            embedding=doc_dict["embedding"],
+            category=doc_dict.get("category"),
+            subcategory=doc_dict.get("subcategory"),
+            tags=doc_dict.get("tags"),
+        )

src/embeddingbuddy/data/processor.py

@@ -5,18 +5,19 @@ from .parser import NDJSONParser
 
 
 class DataProcessor:
-    
     def __init__(self):
         self.parser = NDJSONParser()
-    
-    def process_upload(self, contents: str, filename: Optional[str] = None) -> ProcessedData:
+
+    def process_upload(
+        self, contents: str, filename: Optional[str] = None
+    ) -> ProcessedData:
         try:
             documents = self.parser.parse_upload_contents(contents)
             embeddings = self._extract_embeddings(documents)
             return ProcessedData(documents=documents, embeddings=embeddings)
         except Exception as e:
             return ProcessedData(documents=[], embeddings=np.array([]), error=str(e))
-    
+
     def process_text(self, text_content: str) -> ProcessedData:
         try:
             documents = self.parser.parse_text(text_content)
@@ -24,31 +25,35 @@ class DataProcessor:
             return ProcessedData(documents=documents, embeddings=embeddings)
         except Exception as e:
             return ProcessedData(documents=[], embeddings=np.array([]), error=str(e))
-    
+
     def _extract_embeddings(self, documents: List[Document]) -> np.ndarray:
         if not documents:
             return np.array([])
         return np.array([doc.embedding for doc in documents])
-    
-    def combine_data(self, doc_data: ProcessedData, prompt_data: Optional[ProcessedData] = None) -> Tuple[np.ndarray, List[Document], Optional[List[Document]]]:
+
+    def combine_data(
+        self, doc_data: ProcessedData, prompt_data: Optional[ProcessedData] = None
+    ) -> Tuple[np.ndarray, List[Document], Optional[List[Document]]]:
         if not doc_data or doc_data.error:
             raise ValueError("Invalid document data")
-        
+
         all_embeddings = doc_data.embeddings
         documents = doc_data.documents
         prompts = None
-        
+
         if prompt_data and not prompt_data.error and prompt_data.documents:
             all_embeddings = np.vstack([doc_data.embeddings, prompt_data.embeddings])
             prompts = prompt_data.documents
-        
+
         return all_embeddings, documents, prompts
-    
-    def split_reduced_data(self, reduced_embeddings: np.ndarray, n_documents: int, n_prompts: int = 0) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+
+    def split_reduced_data(
+        self, reduced_embeddings: np.ndarray, n_documents: int, n_prompts: int = 0
+    ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
         doc_reduced = reduced_embeddings[:n_documents]
         prompt_reduced = None
-        
+
         if n_prompts > 0:
-            prompt_reduced = reduced_embeddings[n_documents:n_documents + n_prompts]
-        
-        return doc_reduced, prompt_reduced
+            prompt_reduced = reduced_embeddings[n_documents : n_documents + n_prompts]
+
+        return doc_reduced, prompt_reduced

src/embeddingbuddy/models/reducers.py

@@ -1,6 +1,5 @@
 from abc import ABC, abstractmethod
 import numpy as np
-from typing import Optional, Tuple
 from sklearn.decomposition import PCA
 import umap
 from openTSNE import TSNE
@@ -8,88 +7,89 @@ from .schemas import ReducedData
 
 
 class DimensionalityReducer(ABC):
-    
     def __init__(self, n_components: int = 3, random_state: int = 42):
         self.n_components = n_components
         self.random_state = random_state
         self._reducer = None
-    
+
     @abstractmethod
     def fit_transform(self, embeddings: np.ndarray) -> ReducedData:
         pass
-    
+
     @abstractmethod
     def get_method_name(self) -> str:
         pass
 
 
 class PCAReducer(DimensionalityReducer):
-    
     def fit_transform(self, embeddings: np.ndarray) -> ReducedData:
         self._reducer = PCA(n_components=self.n_components)
         reduced = self._reducer.fit_transform(embeddings)
         variance_explained = self._reducer.explained_variance_ratio_
-        
+
         return ReducedData(
             reduced_embeddings=reduced,
             variance_explained=variance_explained,
             method=self.get_method_name(),
-            n_components=self.n_components
+            n_components=self.n_components,
         )
-    
+
     def get_method_name(self) -> str:
         return "PCA"
 
 
 class TSNEReducer(DimensionalityReducer):
-    
     def fit_transform(self, embeddings: np.ndarray) -> ReducedData:
-        self._reducer = TSNE(n_components=self.n_components, random_state=self.random_state)
+        self._reducer = TSNE(
+            n_components=self.n_components, random_state=self.random_state
+        )
         reduced = self._reducer.fit(embeddings)
-        
+
         return ReducedData(
             reduced_embeddings=reduced,
             variance_explained=None,
             method=self.get_method_name(),
-            n_components=self.n_components
+            n_components=self.n_components,
         )
-    
+
     def get_method_name(self) -> str:
         return "t-SNE"
 
 
 class UMAPReducer(DimensionalityReducer):
-    
     def fit_transform(self, embeddings: np.ndarray) -> ReducedData:
-        self._reducer = umap.UMAP(n_components=self.n_components, random_state=self.random_state)
+        self._reducer = umap.UMAP(
+            n_components=self.n_components, random_state=self.random_state
+        )
         reduced = self._reducer.fit_transform(embeddings)
-        
+
         return ReducedData(
             reduced_embeddings=reduced,
             variance_explained=None,
             method=self.get_method_name(),
-            n_components=self.n_components
+            n_components=self.n_components,
         )
-    
+
     def get_method_name(self) -> str:
         return "UMAP"
 
 
 class ReducerFactory:
-    
     @staticmethod
-    def create_reducer(method: str, n_components: int = 3, random_state: int = 42) -> DimensionalityReducer:
+    def create_reducer(
+        method: str, n_components: int = 3, random_state: int = 42
+    ) -> DimensionalityReducer:
         method_lower = method.lower()
-        
-        if method_lower == 'pca':
+
+        if method_lower == "pca":
             return PCAReducer(n_components=n_components, random_state=random_state)
-        elif method_lower == 'tsne':
+        elif method_lower == "tsne":
             return TSNEReducer(n_components=n_components, random_state=random_state)
-        elif method_lower == 'umap':
+        elif method_lower == "umap":
             return UMAPReducer(n_components=n_components, random_state=random_state)
         else:
             raise ValueError(f"Unknown reduction method: {method}")
-    
+
     @staticmethod
     def get_available_methods() -> list:
-        return ['pca', 'tsne', 'umap']
+        return ["pca", "tsne", "umap"]

src/embeddingbuddy/models/schemas.py

@@ -1,4 +1,4 @@
-from typing import List, Optional, Any, Dict
+from typing import List, Optional
 from dataclasses import dataclass
 import numpy as np
 
@@ -50,9 +50,11 @@ class PlotData:
     coordinates: np.ndarray
     prompts: Optional[List[Document]] = None
     prompt_coordinates: Optional[np.ndarray] = None
-    
+
     def __post_init__(self):
         if not isinstance(self.coordinates, np.ndarray):
             self.coordinates = np.array(self.coordinates)
-        if self.prompt_coordinates is not None and not isinstance(self.prompt_coordinates, np.ndarray):
-            self.prompt_coordinates = np.array(self.prompt_coordinates)
+        if self.prompt_coordinates is not None and not isinstance(
+            self.prompt_coordinates, np.ndarray
+        ):
+            self.prompt_coordinates = np.array(self.prompt_coordinates)

src/embeddingbuddy/ui/callbacks/data_processing.py

@@ -1,61 +1,62 @@
-import numpy as np
 from dash import callback, Input, Output, State
 from ...data.processor import DataProcessor
 
 
 class DataProcessingCallbacks:
-    
     def __init__(self):
         self.processor = DataProcessor()
         self._register_callbacks()
-    
+
     def _register_callbacks(self):
-        
         @callback(
-            Output('processed-data', 'data'),
-            Input('upload-data', 'contents'),
-            State('upload-data', 'filename')
+            Output("processed-data", "data"),
+            Input("upload-data", "contents"),
+            State("upload-data", "filename"),
         )
         def process_uploaded_file(contents, filename):
             if contents is None:
                 return None
-            
+
             processed_data = self.processor.process_upload(contents, filename)
-            
+
             if processed_data.error:
-                return {'error': processed_data.error}
-            
+                return {"error": processed_data.error}
+
             return {
-                'documents': [self._document_to_dict(doc) for doc in processed_data.documents],
-                'embeddings': processed_data.embeddings.tolist()
+                "documents": [
+                    self._document_to_dict(doc) for doc in processed_data.documents
+                ],
+                "embeddings": processed_data.embeddings.tolist(),
             }
-        
+
         @callback(
-            Output('processed-prompts', 'data'),
-            Input('upload-prompts', 'contents'),
-            State('upload-prompts', 'filename')
+            Output("processed-prompts", "data"),
+            Input("upload-prompts", "contents"),
+            State("upload-prompts", "filename"),
         )
         def process_uploaded_prompts(contents, filename):
             if contents is None:
                 return None
-            
+
             processed_data = self.processor.process_upload(contents, filename)
-            
+
             if processed_data.error:
-                return {'error': processed_data.error}
-            
+                return {"error": processed_data.error}
+
             return {
-                'prompts': [self._document_to_dict(doc) for doc in processed_data.documents],
-                'embeddings': processed_data.embeddings.tolist()
+                "prompts": [
+                    self._document_to_dict(doc) for doc in processed_data.documents
+                ],
+                "embeddings": processed_data.embeddings.tolist(),
             }
-    
+
     @staticmethod
     def _document_to_dict(doc):
         return {
-            'id': doc.id,
-            'text': doc.text,
-            'embedding': doc.embedding,
-            'category': doc.category,
-            'subcategory': doc.subcategory,
-            'tags': doc.tags
-        }
+            "id": doc.id,
+            "text": doc.text,
+            "embedding": doc.embedding,
+            "category": doc.category,
+            "subcategory": doc.subcategory,
+            "tags": doc.tags,
+        }

src/embeddingbuddy/ui/callbacks/interactions.py

@@ -4,63 +4,79 @@ import dash_bootstrap_components as dbc
 
 
 class InteractionCallbacks:
-    
     def __init__(self):
         self._register_callbacks()
-    
+
     def _register_callbacks(self):
-        
         @callback(
-            Output('point-details', 'children'),
-            Input('embedding-plot', 'clickData'),
-            [State('processed-data', 'data'),
-             State('processed-prompts', 'data')]
+            Output("point-details", "children"),
+            Input("embedding-plot", "clickData"),
+            [State("processed-data", "data"), State("processed-prompts", "data")],
         )
         def display_click_data(clickData, data, prompts_data):
             if not clickData or not data:
                 return "Click on a point to see details"
-            
-            point_data = clickData['points'][0]
-            trace_name = point_data.get('fullData', {}).get('name', 'Documents')
-            
-            if 'pointIndex' in point_data:
-                point_index = point_data['pointIndex']
-            elif 'pointNumber' in point_data:
-                point_index = point_data['pointNumber']
+
+            point_data = clickData["points"][0]
+            trace_name = point_data.get("fullData", {}).get("name", "Documents")
+
+            if "pointIndex" in point_data:
+                point_index = point_data["pointIndex"]
+            elif "pointNumber" in point_data:
+                point_index = point_data["pointNumber"]
             else:
                 return "Could not identify clicked point"
-            
-            if trace_name.startswith('Prompts') and prompts_data and 'prompts' in prompts_data:
-                item = prompts_data['prompts'][point_index]
-                item_type = 'Prompt'
+
+            if (
+                trace_name.startswith("Prompts")
+                and prompts_data
+                and "prompts" in prompts_data
+            ):
+                item = prompts_data["prompts"][point_index]
+                item_type = "Prompt"
             else:
-                item = data['documents'][point_index]
-                item_type = 'Document'
-            
+                item = data["documents"][point_index]
+                item_type = "Document"
+
             return self._create_detail_card(item, item_type)
-        
+
         @callback(
-            [Output('processed-data', 'data', allow_duplicate=True),
-             Output('processed-prompts', 'data', allow_duplicate=True),
-             Output('point-details', 'children', allow_duplicate=True)],
-            Input('reset-button', 'n_clicks'),
-            prevent_initial_call=True
+            [
+                Output("processed-data", "data", allow_duplicate=True),
+                Output("processed-prompts", "data", allow_duplicate=True),
+                Output("point-details", "children", allow_duplicate=True),
+            ],
+            Input("reset-button", "n_clicks"),
+            prevent_initial_call=True,
         )
         def reset_data(n_clicks):
             if n_clicks is None or n_clicks == 0:
                 return dash.no_update, dash.no_update, dash.no_update
-            
+
             return None, None, "Click on a point to see details"
-    
+
     @staticmethod
     def _create_detail_card(item, item_type):
-        return dbc.Card([
-            dbc.CardBody([
-                html.H5(f"{item_type}: {item['id']}", className="card-title"),
-                html.P(f"Text: {item['text']}", className="card-text"),
-                html.P(f"Category: {item.get('category', 'Unknown')}", className="card-text"),
-                html.P(f"Subcategory: {item.get('subcategory', 'Unknown')}", className="card-text"),
-                html.P(f"Tags: {', '.join(item.get('tags', [])) if item.get('tags') else 'None'}", className="card-text"),
-                html.P(f"Type: {item_type}", className="card-text text-muted")
-            ])
-        ])
+        return dbc.Card(
+            [
+                dbc.CardBody(
+                    [
+                        html.H5(f"{item_type}: {item['id']}", className="card-title"),
+                        html.P(f"Text: {item['text']}", className="card-text"),
+                        html.P(
+                            f"Category: {item.get('category', 'Unknown')}",
+                            className="card-text",
+                        ),
+                        html.P(
+                            f"Subcategory: {item.get('subcategory', 'Unknown')}",
+                            className="card-text",
+                        ),
+                        html.P(
+                            f"Tags: {', '.join(item.get('tags', [])) if item.get('tags') else 'None'}",
+                            className="card-text",
+                        ),
+                        html.P(f"Type: {item_type}", className="card-text text-muted"),
+                    ]
+                )
+            ]
+        )

src/embeddingbuddy/ui/callbacks/visualization.py

@@ -7,81 +7,102 @@ from ...visualization.plots import PlotFactory
 
 
 class VisualizationCallbacks:
-    
     def __init__(self):
         self.plot_factory = PlotFactory()
         self._register_callbacks()
-    
+
     def _register_callbacks(self):
-        
         @callback(
-            Output('embedding-plot', 'figure'),
-            [Input('processed-data', 'data'),
-             Input('processed-prompts', 'data'),
-             Input('method-dropdown', 'value'),
-             Input('color-dropdown', 'value'),
-             Input('dimension-toggle', 'value'),
-             Input('show-prompts-toggle', 'value')]
+            Output("embedding-plot", "figure"),
+            [
+                Input("processed-data", "data"),
+                Input("processed-prompts", "data"),
+                Input("method-dropdown", "value"),
+                Input("color-dropdown", "value"),
+                Input("dimension-toggle", "value"),
+                Input("show-prompts-toggle", "value"),
+            ],
         )
         def update_plot(data, prompts_data, method, color_by, dimensions, show_prompts):
-            if not data or 'error' in data:
+            if not data or "error" in data:
                 return go.Figure().add_annotation(
                     text="Upload a valid NDJSON file to see visualization",
-                    xref="paper", yref="paper",
-                    x=0.5, y=0.5, xanchor='center', yanchor='middle',
-                    showarrow=False, font=dict(size=16)
+                    xref="paper",
+                    yref="paper",
+                    x=0.5,
+                    y=0.5,
+                    xanchor="center",
+                    yanchor="middle",
+                    showarrow=False,
+                    font=dict(size=16),
                 )
-            
+
             try:
-                doc_embeddings = np.array(data['embeddings'])
+                doc_embeddings = np.array(data["embeddings"])
                 all_embeddings = doc_embeddings
-                has_prompts = prompts_data and 'error' not in prompts_data and prompts_data.get('prompts')
-                
+                has_prompts = (
+                    prompts_data
+                    and "error" not in prompts_data
+                    and prompts_data.get("prompts")
+                )
+
                 if has_prompts:
-                    prompt_embeddings = np.array(prompts_data['embeddings'])
+                    prompt_embeddings = np.array(prompts_data["embeddings"])
                     all_embeddings = np.vstack([doc_embeddings, prompt_embeddings])
-                
-                n_components = 3 if dimensions == '3d' else 2
-                
-                reducer = ReducerFactory.create_reducer(method, n_components=n_components)
+
+                n_components = 3 if dimensions == "3d" else 2
+
+                reducer = ReducerFactory.create_reducer(
+                    method, n_components=n_components
+                )
                 reduced_data = reducer.fit_transform(all_embeddings)
-                
-                doc_reduced = reduced_data.reduced_embeddings[:len(doc_embeddings)]
+
+                doc_reduced = reduced_data.reduced_embeddings[: len(doc_embeddings)]
                 prompt_reduced = None
                 if has_prompts:
-                    prompt_reduced = reduced_data.reduced_embeddings[len(doc_embeddings):]
-                
-                documents = [self._dict_to_document(doc) for doc in data['documents']]
+                    prompt_reduced = reduced_data.reduced_embeddings[
+                        len(doc_embeddings) :
+                    ]
+
+                documents = [self._dict_to_document(doc) for doc in data["documents"]]
                 prompts = None
                 if has_prompts:
-                    prompts = [self._dict_to_document(prompt) for prompt in prompts_data['prompts']]
-                
+                    prompts = [
+                        self._dict_to_document(prompt)
+                        for prompt in prompts_data["prompts"]
+                    ]
+
                 plot_data = PlotData(
                     documents=documents,
                     coordinates=doc_reduced,
                     prompts=prompts,
-                    prompt_coordinates=prompt_reduced
+                    prompt_coordinates=prompt_reduced,
                 )
-                
+
                 return self.plot_factory.create_plot(
                     plot_data, dimensions, color_by, reduced_data.method, show_prompts
                 )
-                
+
             except Exception as e:
                 return go.Figure().add_annotation(
                     text=f"Error creating visualization: {str(e)}",
-                    xref="paper", yref="paper",
-                    x=0.5, y=0.5, xanchor='center', yanchor='middle',
-                    showarrow=False, font=dict(size=16)
+                    xref="paper",
+                    yref="paper",
+                    x=0.5,
+                    y=0.5,
+                    xanchor="center",
+                    yanchor="middle",
+                    showarrow=False,
+                    font=dict(size=16),
                 )
-    
+
     @staticmethod
     def _dict_to_document(doc_dict):
         return Document(
-            id=doc_dict['id'],
-            text=doc_dict['text'],
-            embedding=doc_dict['embedding'],
-            category=doc_dict.get('category'),
-            subcategory=doc_dict.get('subcategory'),
-            tags=doc_dict.get('tags', [])
-        )
+            id=doc_dict["id"],
+            text=doc_dict["text"],
+            embedding=doc_dict["embedding"],
+            category=doc_dict.get("category"),
+            subcategory=doc_dict.get("subcategory"),
+            tags=doc_dict.get("tags", []),
+        )

src/embeddingbuddy/ui/components/sidebar.py

@@ -4,79 +4,81 @@ from .upload import UploadComponent
 
 
 class SidebarComponent:
-    
     def __init__(self):
         self.upload_component = UploadComponent()
-    
+
     def create_layout(self):
-        return dbc.Col([
-            html.H5("Upload Data", className="mb-3"),
-            self.upload_component.create_data_upload(),
-            self.upload_component.create_prompts_upload(),
-            self.upload_component.create_reset_button(),
-            
-            html.H5("Visualization Controls", className="mb-3"),
-            self._create_method_dropdown(),
-            self._create_color_dropdown(),
-            self._create_dimension_toggle(),
-            self._create_prompts_toggle(),
-            
-            html.H5("Point Details", className="mb-3"),
-            html.Div(id='point-details', children="Click on a point to see details")
-            
-        ], width=3, style={'padding-right': '20px'})
-    
+        return dbc.Col(
+            [
+                html.H5("Upload Data", className="mb-3"),
+                self.upload_component.create_data_upload(),
+                self.upload_component.create_prompts_upload(),
+                self.upload_component.create_reset_button(),
+                html.H5("Visualization Controls", className="mb-3"),
+                self._create_method_dropdown(),
+                self._create_color_dropdown(),
+                self._create_dimension_toggle(),
+                self._create_prompts_toggle(),
+                html.H5("Point Details", className="mb-3"),
+                html.Div(
+                    id="point-details", children="Click on a point to see details"
+                ),
+            ],
+            width=3,
+            style={"padding-right": "20px"},
+        )
+
     def _create_method_dropdown(self):
         return [
             dbc.Label("Method:"),
             dcc.Dropdown(
-                id='method-dropdown',
+                id="method-dropdown",
                 options=[
-                    {'label': 'PCA', 'value': 'pca'},
-                    {'label': 't-SNE', 'value': 'tsne'},
-                    {'label': 'UMAP', 'value': 'umap'}
+                    {"label": "PCA", "value": "pca"},
+                    {"label": "t-SNE", "value": "tsne"},
+                    {"label": "UMAP", "value": "umap"},
                 ],
-                value='pca',
-                style={'margin-bottom': '15px'}
-            )
+                value="pca",
+                style={"margin-bottom": "15px"},
+            ),
         ]
-    
+
     def _create_color_dropdown(self):
         return [
             dbc.Label("Color by:"),
             dcc.Dropdown(
-                id='color-dropdown',
+                id="color-dropdown",
                 options=[
-                    {'label': 'Category', 'value': 'category'},
-                    {'label': 'Subcategory', 'value': 'subcategory'},
-                    {'label': 'Tags', 'value': 'tags'}
+                    {"label": "Category", "value": "category"},
+                    {"label": "Subcategory", "value": "subcategory"},
+                    {"label": "Tags", "value": "tags"},
                 ],
-                value='category',
-                style={'margin-bottom': '15px'}
-            )
+                value="category",
+                style={"margin-bottom": "15px"},
+            ),
         ]
-    
+
     def _create_dimension_toggle(self):
         return [
             dbc.Label("Dimensions:"),
             dcc.RadioItems(
-                id='dimension-toggle',
+                id="dimension-toggle",
                 options=[
-                    {'label': '2D', 'value': '2d'},
-                    {'label': '3D', 'value': '3d'}
+                    {"label": "2D", "value": "2d"},
+                    {"label": "3D", "value": "3d"},
                 ],
-                value='3d',
-                style={'margin-bottom': '20px'}
-            )
+                value="3d",
+                style={"margin-bottom": "20px"},
+            ),
         ]
-    
+
     def _create_prompts_toggle(self):
         return [
             dbc.Label("Show Prompts:"),
             dcc.Checklist(
-                id='show-prompts-toggle',
-                options=[{'label': 'Show prompts on plot', 'value': 'show'}],
-                value=['show'],
-                style={'margin-bottom': '20px'}
-            )
-        ]
+                id="show-prompts-toggle",
+                options=[{"label": "Show prompts on plot", "value": "show"}],
+                value=["show"],
+                style={"margin-bottom": "20px"},
+            ),
+        ]

src/embeddingbuddy/ui/components/upload.py

@@ -3,58 +3,51 @@ import dash_bootstrap_components as dbc
 
 
 class UploadComponent:
-    
     @staticmethod
     def create_data_upload():
         return dcc.Upload(
-            id='upload-data',
-            children=html.Div([
-                'Drag and Drop or ',
-                html.A('Select Files')
-            ]),
+            id="upload-data",
+            children=html.Div(["Drag and Drop or ", html.A("Select Files")]),
             style={
-                'width': '100%',
-                'height': '60px',
-                'lineHeight': '60px',
-                'borderWidth': '1px',
-                'borderStyle': 'dashed',
-                'borderRadius': '5px',
-                'textAlign': 'center',
-                'margin-bottom': '20px'
+                "width": "100%",
+                "height": "60px",
+                "lineHeight": "60px",
+                "borderWidth": "1px",
+                "borderStyle": "dashed",
+                "borderRadius": "5px",
+                "textAlign": "center",
+                "margin-bottom": "20px",
             },
-            multiple=False
+            multiple=False,
         )
-    
+
     @staticmethod
     def create_prompts_upload():
         return dcc.Upload(
-            id='upload-prompts',
-            children=html.Div([
-                'Drag and Drop Prompts or ',
-                html.A('Select Files')
-            ]),
+            id="upload-prompts",
+            children=html.Div(["Drag and Drop Prompts or ", html.A("Select Files")]),
             style={
-                'width': '100%',
-                'height': '60px',
-                'lineHeight': '60px',
-                'borderWidth': '1px',
-                'borderStyle': 'dashed',
-                'borderRadius': '5px',
-                'textAlign': 'center',
-                'margin-bottom': '20px',
-                'borderColor': '#28a745'
+                "width": "100%",
+                "height": "60px",
+                "lineHeight": "60px",
+                "borderWidth": "1px",
+                "borderStyle": "dashed",
+                "borderRadius": "5px",
+                "textAlign": "center",
+                "margin-bottom": "20px",
+                "borderColor": "#28a745",
             },
-            multiple=False
+            multiple=False,
         )
-    
+
     @staticmethod
     def create_reset_button():
         return dbc.Button(
             "Reset All Data",
-            id='reset-button',
-            color='danger',
+            id="reset-button",
+            color="danger",
             outline=True,
-            size='sm',
-            className='mb-3',
-            style={'width': '100%'}
-        )
+            size="sm",
+            className="mb-3",
+            style={"width": "100%"},
+        )

src/embeddingbuddy/ui/layout.py

@@ -4,41 +4,43 @@ from .components.sidebar import SidebarComponent
 
 
 class AppLayout:
-    
     def __init__(self):
         self.sidebar = SidebarComponent()
-    
+
     def create_layout(self):
-        return dbc.Container([
-            self._create_header(),
-            self._create_main_content(),
-            self._create_stores()
-        ], fluid=True)
-    
+        return dbc.Container(
+            [self._create_header(), self._create_main_content(), self._create_stores()],
+            fluid=True,
+        )
+
     def _create_header(self):
-        return dbc.Row([
-            dbc.Col([
-                html.H1("EmbeddingBuddy", className="text-center mb-4"),
-            ], width=12)
-        ])
-    
+        return dbc.Row(
+            [
+                dbc.Col(
+                    [
+                        html.H1("EmbeddingBuddy", className="text-center mb-4"),
+                    ],
+                    width=12,
+                )
+            ]
+        )
+
     def _create_main_content(self):
-        return dbc.Row([
-            self.sidebar.create_layout(),
-            self._create_visualization_area()
-        ])
-    
+        return dbc.Row(
+            [self.sidebar.create_layout(), self._create_visualization_area()]
+        )
+
     def _create_visualization_area(self):
-        return dbc.Col([
-            dcc.Graph(
-                id='embedding-plot',
-                style={'height': '85vh', 'width': '100%'},
-                config={'responsive': True, 'displayModeBar': True}
-            )
-        ], width=9)
-    
+        return dbc.Col(
+            [
+                dcc.Graph(
+                    id="embedding-plot",
+                    style={"height": "85vh", "width": "100%"},
+                    config={"responsive": True, "displayModeBar": True},
+                )
+            ],
+            width=9,
+        )
+
     def _create_stores(self):
-        return [
-            dcc.Store(id='processed-data'),
-            dcc.Store(id='processed-prompts')
-        ]
+        return [dcc.Store(id="processed-data"), dcc.Store(id="processed-prompts")]

src/embeddingbuddy/visualization/colors.py

@@ -1,33 +1,36 @@
-from typing import List, Dict, Any
+from typing import List
 import plotly.colors as pc
 from ..models.schemas import Document
 
 
 class ColorMapper:
-    
     @staticmethod
     def create_color_mapping(documents: List[Document], color_by: str) -> List[str]:
-        if color_by == 'category':
+        if color_by == "category":
             return [doc.category for doc in documents]
-        elif color_by == 'subcategory':
+        elif color_by == "subcategory":
             return [doc.subcategory for doc in documents]
-        elif color_by == 'tags':
-            return [', '.join(doc.tags) if doc.tags else 'No tags' for doc in documents]
+        elif color_by == "tags":
+            return [", ".join(doc.tags) if doc.tags else "No tags" for doc in documents]
         else:
-            return ['All'] * len(documents)
-    
+            return ["All"] * len(documents)
+
     @staticmethod
     def to_grayscale_hex(color_str: str) -> str:
         try:
-            if color_str.startswith('#'):
-                rgb = tuple(int(color_str[i:i+2], 16) for i in (1, 3, 5))
+            if color_str.startswith("#"):
+                rgb = tuple(int(color_str[i : i + 2], 16) for i in (1, 3, 5))
             else:
-                rgb = pc.hex_to_rgb(pc.convert_colors_to_same_type([color_str], colortype='hex')[0][0])
-            
+                rgb = pc.hex_to_rgb(
+                    pc.convert_colors_to_same_type([color_str], colortype="hex")[0][0]
+                )
+
             gray_value = int(0.299 * rgb[0] + 0.587 * rgb[1] + 0.114 * rgb[2])
-            gray_rgb = (gray_value * 0.7 + rgb[0] * 0.3, 
-                       gray_value * 0.7 + rgb[1] * 0.3, 
-                       gray_value * 0.7 + rgb[2] * 0.3)
-            return f'rgb({int(gray_rgb[0])},{int(gray_rgb[1])},{int(gray_rgb[2])})'
-        except:
-            return 'rgb(128,128,128)'
+            gray_rgb = (
+                gray_value * 0.7 + rgb[0] * 0.3,
+                gray_value * 0.7 + rgb[1] * 0.3,
+                gray_value * 0.7 + rgb[2] * 0.3,
+            )
+            return f"rgb({int(gray_rgb[0])},{int(gray_rgb[1])},{int(gray_rgb[2])})"
+        except:  # noqa: E722
+            return "rgb(128,128,128)"

src/embeddingbuddy/visualization/plots.py

@@ -7,139 +7,172 @@ from .colors import ColorMapper
 
 
 class PlotFactory:
-    
     def __init__(self):
         self.color_mapper = ColorMapper()
-    
-    def create_plot(self, plot_data: PlotData, dimensions: str = '3d', 
-                   color_by: str = 'category', method: str = 'PCA',
-                   show_prompts: Optional[List[str]] = None) -> go.Figure:
-        
-        if plot_data.prompts and show_prompts and 'show' in show_prompts:
+
+    def create_plot(
+        self,
+        plot_data: PlotData,
+        dimensions: str = "3d",
+        color_by: str = "category",
+        method: str = "PCA",
+        show_prompts: Optional[List[str]] = None,
+    ) -> go.Figure:
+        if plot_data.prompts and show_prompts and "show" in show_prompts:
             return self._create_dual_plot(plot_data, dimensions, color_by, method)
         else:
             return self._create_single_plot(plot_data, dimensions, color_by, method)
-    
-    def _create_single_plot(self, plot_data: PlotData, dimensions: str, 
-                           color_by: str, method: str) -> go.Figure:
-        df = self._prepare_dataframe(plot_data.documents, plot_data.coordinates, dimensions)
-        color_values = self.color_mapper.create_color_mapping(plot_data.documents, color_by)
-        
-        hover_fields = ['id', 'text_preview', 'category', 'subcategory', 'tags_str']
-        
-        if dimensions == '3d':
+
+    def _create_single_plot(
+        self, plot_data: PlotData, dimensions: str, color_by: str, method: str
+    ) -> go.Figure:
+        df = self._prepare_dataframe(
+            plot_data.documents, plot_data.coordinates, dimensions
+        )
+        color_values = self.color_mapper.create_color_mapping(
+            plot_data.documents, color_by
+        )
+
+        hover_fields = ["id", "text_preview", "category", "subcategory", "tags_str"]
+
+        if dimensions == "3d":
             fig = px.scatter_3d(
-                df, x='dim_1', y='dim_2', z='dim_3',
+                df,
+                x="dim_1",
+                y="dim_2",
+                z="dim_3",
                 color=color_values,
                 hover_data=hover_fields,
-                title=f'3D Embedding Visualization - {method} (colored by {color_by})'
+                title=f"3D Embedding Visualization - {method} (colored by {color_by})",
             )
             fig.update_traces(marker=dict(size=5))
         else:
             fig = px.scatter(
-                df, x='dim_1', y='dim_2',
+                df,
+                x="dim_1",
+                y="dim_2",
                 color=color_values,
                 hover_data=hover_fields,
-                title=f'2D Embedding Visualization - {method} (colored by {color_by})'
+                title=f"2D Embedding Visualization - {method} (colored by {color_by})",
             )
             fig.update_traces(marker=dict(size=8))
-        
-        fig.update_layout(
-            height=None,
-            autosize=True,
-            margin=dict(l=0, r=0, t=50, b=0)
-        )
+
+        fig.update_layout(height=None, autosize=True, margin=dict(l=0, r=0, t=50, b=0))
         return fig
-    
-    def _create_dual_plot(self, plot_data: PlotData, dimensions: str, 
-                         color_by: str, method: str) -> go.Figure:
+
+    def _create_dual_plot(
+        self, plot_data: PlotData, dimensions: str, color_by: str, method: str
+    ) -> go.Figure:
         fig = go.Figure()
-        
-        doc_df = self._prepare_dataframe(plot_data.documents, plot_data.coordinates, dimensions)
-        doc_color_values = self.color_mapper.create_color_mapping(plot_data.documents, color_by)
-        
-        hover_fields = ['id', 'text_preview', 'category', 'subcategory', 'tags_str']
-        
-        if dimensions == '3d':
+
+        doc_df = self._prepare_dataframe(
+            plot_data.documents, plot_data.coordinates, dimensions
+        )
+        doc_color_values = self.color_mapper.create_color_mapping(
+            plot_data.documents, color_by
+        )
+
+        hover_fields = ["id", "text_preview", "category", "subcategory", "tags_str"]
+
+        if dimensions == "3d":
             doc_fig = px.scatter_3d(
-                doc_df, x='dim_1', y='dim_2', z='dim_3',
+                doc_df,
+                x="dim_1",
+                y="dim_2",
+                z="dim_3",
                 color=doc_color_values,
-                hover_data=hover_fields
+                hover_data=hover_fields,
             )
         else:
             doc_fig = px.scatter(
-                doc_df, x='dim_1', y='dim_2',
+                doc_df,
+                x="dim_1",
+                y="dim_2",
                 color=doc_color_values,
-                hover_data=hover_fields
+                hover_data=hover_fields,
             )
-        
+
         for trace in doc_fig.data:
-            trace.name = f'Documents - {trace.name}'
-            if dimensions == '3d':
+            trace.name = f"Documents - {trace.name}"
+            if dimensions == "3d":
                 trace.marker.size = 5
-                trace.marker.symbol = 'circle'
+                trace.marker.symbol = "circle"
             else:
                 trace.marker.size = 8
-                trace.marker.symbol = 'circle'
+                trace.marker.symbol = "circle"
             trace.marker.opacity = 1.0
             fig.add_trace(trace)
-        
+
         if plot_data.prompts and plot_data.prompt_coordinates is not None:
-            prompt_df = self._prepare_dataframe(plot_data.prompts, plot_data.prompt_coordinates, dimensions)
-            prompt_color_values = self.color_mapper.create_color_mapping(plot_data.prompts, color_by)
-            
-            if dimensions == '3d':
+            prompt_df = self._prepare_dataframe(
+                plot_data.prompts, plot_data.prompt_coordinates, dimensions
+            )
+            prompt_color_values = self.color_mapper.create_color_mapping(
+                plot_data.prompts, color_by
+            )
+
+            if dimensions == "3d":
                 prompt_fig = px.scatter_3d(
-                    prompt_df, x='dim_1', y='dim_2', z='dim_3',
+                    prompt_df,
+                    x="dim_1",
+                    y="dim_2",
+                    z="dim_3",
                     color=prompt_color_values,
-                    hover_data=hover_fields
+                    hover_data=hover_fields,
                 )
             else:
                 prompt_fig = px.scatter(
-                    prompt_df, x='dim_1', y='dim_2',
+                    prompt_df,
+                    x="dim_1",
+                    y="dim_2",
                     color=prompt_color_values,
-                    hover_data=hover_fields
+                    hover_data=hover_fields,
                 )
-            
+
             for trace in prompt_fig.data:
-                if hasattr(trace.marker, 'color') and isinstance(trace.marker.color, str):
-                    trace.marker.color = self.color_mapper.to_grayscale_hex(trace.marker.color)
-                
-                trace.name = f'Prompts - {trace.name}'
-                if dimensions == '3d':
+                if hasattr(trace.marker, "color") and isinstance(
+                    trace.marker.color, str
+                ):
+                    trace.marker.color = self.color_mapper.to_grayscale_hex(
+                        trace.marker.color
+                    )
+
+                trace.name = f"Prompts - {trace.name}"
+                if dimensions == "3d":
                     trace.marker.size = 6
-                    trace.marker.symbol = 'diamond'
+                    trace.marker.symbol = "diamond"
                 else:
                     trace.marker.size = 10
-                    trace.marker.symbol = 'diamond'
+                    trace.marker.symbol = "diamond"
                 trace.marker.opacity = 0.8
                 fig.add_trace(trace)
-        
-        title = f'{dimensions.upper()} Embedding Visualization - {method} (colored by {color_by})'
+
+        title = f"{dimensions.upper()} Embedding Visualization - {method} (colored by {color_by})"
         fig.update_layout(
-            title=title,
-            height=None,
-            autosize=True,
-            margin=dict(l=0, r=0, t=50, b=0)
+            title=title, height=None, autosize=True, margin=dict(l=0, r=0, t=50, b=0)
         )
-        
+
         return fig
-    
-    def _prepare_dataframe(self, documents: List[Document], coordinates, dimensions: str) -> pd.DataFrame:
+
+    def _prepare_dataframe(
+        self, documents: List[Document], coordinates, dimensions: str
+    ) -> pd.DataFrame:
         df_data = []
         for i, doc in enumerate(documents):
             row = {
-                'id': doc.id,
-                'text': doc.text,
-                'text_preview': doc.text[:100] + "..." if len(doc.text) > 100 else doc.text,
-                'category': doc.category,
-                'subcategory': doc.subcategory,
-                'tags_str': ', '.join(doc.tags) if doc.tags else 'None',
-                'dim_1': coordinates[i, 0],
-                'dim_2': coordinates[i, 1],
+                "id": doc.id,
+                "text": doc.text,
+                "text_preview": doc.text[:100] + "..."
+                if len(doc.text) > 100
+                else doc.text,
+                "category": doc.category,
+                "subcategory": doc.subcategory,
+                "tags_str": ", ".join(doc.tags) if doc.tags else "None",
+                "dim_1": coordinates[i, 0],
+                "dim_2": coordinates[i, 1],
             }
-            if dimensions == '3d':
-                row['dim_3'] = coordinates[i, 2]
+            if dimensions == "3d":
+                row["dim_3"] = coordinates[i, 2]
             df_data.append(row)
-        
-        return pd.DataFrame(df_data)
+
+        return pd.DataFrame(df_data)

tests/test_data_processing.py

@@ -6,62 +6,64 @@ from src.embeddingbuddy.models.schemas import Document
 
 
 class TestNDJSONParser:
-    
     def test_parse_text_basic(self):
-        text_content = '{"id": "test1", "text": "Hello world", "embedding": [0.1, 0.2, 0.3]}'
+        text_content = (
+            '{"id": "test1", "text": "Hello world", "embedding": [0.1, 0.2, 0.3]}'
+        )
         documents = NDJSONParser.parse_text(text_content)
-        
+
         assert len(documents) == 1
         assert documents[0].id == "test1"
         assert documents[0].text == "Hello world"
         assert documents[0].embedding == [0.1, 0.2, 0.3]
-    
+
     def test_parse_text_with_metadata(self):
         text_content = '{"id": "test1", "text": "Hello", "embedding": [0.1, 0.2], "category": "greeting", "tags": ["test"]}'
         documents = NDJSONParser.parse_text(text_content)
-        
+
         assert documents[0].category == "greeting"
         assert documents[0].tags == ["test"]
-    
+
     def test_parse_text_missing_id(self):
         text_content = '{"text": "Hello", "embedding": [0.1, 0.2]}'
         documents = NDJSONParser.parse_text(text_content)
-        
+
         assert len(documents) == 1
         assert documents[0].id is not None  # Should be auto-generated
 
 
 class TestDataProcessor:
-    
     def test_extract_embeddings(self):
         documents = [
             Document(id="1", text="test1", embedding=[0.1, 0.2]),
-            Document(id="2", text="test2", embedding=[0.3, 0.4])
+            Document(id="2", text="test2", embedding=[0.3, 0.4]),
         ]
-        
+
         processor = DataProcessor()
         embeddings = processor._extract_embeddings(documents)
-        
+
         assert embeddings.shape == (2, 2)
         assert np.allclose(embeddings[0], [0.1, 0.2])
         assert np.allclose(embeddings[1], [0.3, 0.4])
-    
+
     def test_combine_data(self):
         from src.embeddingbuddy.models.schemas import ProcessedData
-        
+
         doc_data = ProcessedData(
             documents=[Document(id="1", text="doc", embedding=[0.1, 0.2])],
-            embeddings=np.array([[0.1, 0.2]])
+            embeddings=np.array([[0.1, 0.2]]),
         )
-        
+
         prompt_data = ProcessedData(
             documents=[Document(id="p1", text="prompt", embedding=[0.3, 0.4])],
-            embeddings=np.array([[0.3, 0.4]])
+            embeddings=np.array([[0.3, 0.4]]),
         )
-        
+
         processor = DataProcessor()
-        all_embeddings, documents, prompts = processor.combine_data(doc_data, prompt_data)
-        
+        all_embeddings, documents, prompts = processor.combine_data(
+            doc_data, prompt_data
+        )
+
         assert all_embeddings.shape == (2, 2)
         assert len(documents) == 1
         assert len(prompts) == 1
@@ -70,4 +72,4 @@ class TestDataProcessor:
 
 
 if __name__ == "__main__":
-    pytest.main([__file__])
+    pytest.main([__file__])

tests/test_reducers.py

@@ -1,89 +1,90 @@
 import pytest
 import numpy as np
-from src.embeddingbuddy.models.reducers import ReducerFactory, PCAReducer, TSNEReducer, UMAPReducer
+from src.embeddingbuddy.models.reducers import (
+    ReducerFactory,
+    PCAReducer,
+    TSNEReducer,
+    UMAPReducer,
+)
 
 
 class TestReducerFactory:
-    
     def test_create_pca_reducer(self):
-        reducer = ReducerFactory.create_reducer('pca', n_components=2)
+        reducer = ReducerFactory.create_reducer("pca", n_components=2)
         assert isinstance(reducer, PCAReducer)
         assert reducer.n_components == 2
-    
+
     def test_create_tsne_reducer(self):
-        reducer = ReducerFactory.create_reducer('tsne', n_components=3)
+        reducer = ReducerFactory.create_reducer("tsne", n_components=3)
         assert isinstance(reducer, TSNEReducer)
         assert reducer.n_components == 3
-    
+
     def test_create_umap_reducer(self):
-        reducer = ReducerFactory.create_reducer('umap', n_components=2)
+        reducer = ReducerFactory.create_reducer("umap", n_components=2)
         assert isinstance(reducer, UMAPReducer)
         assert reducer.n_components == 2
-    
+
     def test_invalid_method(self):
         with pytest.raises(ValueError, match="Unknown reduction method"):
-            ReducerFactory.create_reducer('invalid_method')
-    
+            ReducerFactory.create_reducer("invalid_method")
+
     def test_available_methods(self):
         methods = ReducerFactory.get_available_methods()
-        assert 'pca' in methods
-        assert 'tsne' in methods
-        assert 'umap' in methods
+        assert "pca" in methods
+        assert "tsne" in methods
+        assert "umap" in methods
 
 
 class TestPCAReducer:
-    
     def test_fit_transform(self):
         embeddings = np.random.rand(100, 512)
         reducer = PCAReducer(n_components=2)
-        
+
         result = reducer.fit_transform(embeddings)
-        
+
         assert result.reduced_embeddings.shape == (100, 2)
         assert result.variance_explained is not None
         assert result.method == "PCA"
         assert result.n_components == 2
-    
+
     def test_method_name(self):
         reducer = PCAReducer()
         assert reducer.get_method_name() == "PCA"
 
 
 class TestTSNEReducer:
-    
     def test_fit_transform_small_dataset(self):
         embeddings = np.random.rand(30, 10)  # Small dataset for faster testing
         reducer = TSNEReducer(n_components=2)
-        
+
         result = reducer.fit_transform(embeddings)
-        
+
         assert result.reduced_embeddings.shape == (30, 2)
         assert result.variance_explained is None  # t-SNE doesn't provide this
         assert result.method == "t-SNE"
         assert result.n_components == 2
-    
+
     def test_method_name(self):
         reducer = TSNEReducer()
         assert reducer.get_method_name() == "t-SNE"
 
 
 class TestUMAPReducer:
-    
     def test_fit_transform(self):
         embeddings = np.random.rand(50, 10)
         reducer = UMAPReducer(n_components=2)
-        
+
         result = reducer.fit_transform(embeddings)
-        
+
         assert result.reduced_embeddings.shape == (50, 2)
         assert result.variance_explained is None  # UMAP doesn't provide this
         assert result.method == "UMAP"
         assert result.n_components == 2
-    
+
     def test_method_name(self):
         reducer = UMAPReducer()
         assert reducer.get_method_name() == "UMAP"
 
 
 if __name__ == "__main__":
-    pytest.main([__file__])
+    pytest.main([__file__])