bump version to 0.4.0

.claude/settings.local.json

@@ -4,9 +4,7 @@
       "Bash(mkdir:*)",
       "Bash(uv run:*)",
       "Bash(uv add:*)",
-      "Bash(uv sync:*)",
+      "Bash(uv sync:*)"
-      "Bash(tree:*)",
-      "WebFetch(domain:www.dash-bootstrap-components.com)"
     ],
     "deny": [],
     "ask": [],

CLAUDE.md

@@ -22,13 +22,11 @@ uv sync
 **Run the application:**
 
 Development mode (with auto-reload):
-
 ```bash
 uv run run_dev.py
 ```
 
 Production mode (with Gunicorn WSGI server):
-
 ```bash
 # First install production dependencies
 uv sync --extra prod
@@ -38,12 +36,11 @@ uv run run_prod.py
 ```
 
 Legacy mode (basic Dash server):
-
 ```bash
 uv run main.py
 ```
 
-The app will be available at <http://127.0.0.1:8050>
+The app will be available at http://127.0.0.1:8050
 
 **Run tests:**
 

Dockerfile

@@ -65,11 +65,6 @@ ENV EMBEDDINGBUDDY_ENV=production
 # Expose port
 EXPOSE 8050
 
-# Create non-root user
-RUN groupadd -r appuser && useradd -r -g appuser appuser
-RUN chown -R appuser:appuser /app
-USER appuser
-
 # Health check
 HEALTHCHECK --interval=30s --timeout=10s --start-period=30s --retries=3 \
     CMD python -c "import requests; requests.get('http://localhost:8050/', timeout=5)" || exit 1

README.md

@@ -152,38 +152,22 @@ The application follows a modular architecture for improved maintainability and
 
 ```text
 src/embeddingbuddy/
-├── app.py                     # Main application entry point and factory
+├── config/          # Configuration management
-├── config/                    # Configuration management
+│   └── settings.py  # Centralized app settings
-│   └── settings.py            # Centralized app settings
+├── data/           # Data parsing and processing
-├── data/                      # Data parsing and processing
+│   ├── parser.py   # NDJSON parsing logic
-│   ├── parser.py              # NDJSON parsing logic
+│   └── processor.py # Data transformation utilities
-│   ├── processor.py           # Data transformation utilities
+├── models/         # Data schemas and algorithms
-│   └── sources/               # Data source integrations
+│   ├── schemas.py  # Pydantic data models
-│       └── opensearch.py      # OpenSearch data source
+│   └── reducers.py # Dimensionality reduction algorithms
-├── models/                    # Data schemas and algorithms
+├── visualization/ # Plot creation and styling
-│   ├── schemas.py             # Pydantic data models
+│   ├── plots.py   # Plot factory and creation logic
-│   ├── reducers.py            # Dimensionality reduction algorithms
+│   └── colors.py  # Color mapping utilities
-│   └── field_mapper.py        # Field mapping utilities
+├── ui/            # User interface components
-├── visualization/             # Plot creation and styling
+│   ├── layout.py  # Main application layout
-│   ├── plots.py               # Plot factory and creation logic
+│   ├── components/ # Reusable UI components
-│   └── colors.py              # Color mapping utilities
+│   └── callbacks/ # Organized callback functions
-├── ui/                        # User interface components
+└── utils/         # Utility functions
-│   ├── layout.py              # Main application layout
-│   ├── components/            # Reusable UI components
-│   │   ├── sidebar.py         # Sidebar component
-│   │   ├── upload.py          # Upload components
-│   │   ├── textinput.py       # Text input components
-│   │   └── datasource.py      # Data source components
-│   └── callbacks/             # Organized callback functions
-│       ├── data_processing.py # Data upload/processing callbacks
-│       ├── visualization.py   # Plot update callbacks
-│       └── interactions.py    # User interaction callbacks
-└── utils/                     # Utility functions
-
-main.py            # Application runner (at project root)
-main.py            # Application runner (at project root)
-run_dev.py         # Development server runner
-run_prod.py        # Production server runner
 ```
 
 ### Testing

assets/custom.css

@@ -1,17 +0,0 @@
-/* CSS override for transparent hover boxes in Plotly plots */
-
-/* Make hover boxes transparent while preserving text readability */
-.hovertext {
-    fill-opacity: 0.8 !important;
-    stroke-opacity: 1 !important;
-}
-
-/* Alternative selector for different Plotly versions */
-g.hovertext > path {
-    opacity: 0.8 !important;
-}
-
-/* Ensure text remains fully visible */
-.hovertext text {
-    opacity: 1 !important;
-}

assets/embeddings.js

@@ -45,12 +45,28 @@ class TransformersEmbedder {
                 console.log('✅ Using globally loaded Transformers.js pipeline');
             }
             
-            this.extractor = await window.transformers.pipeline('feature-extraction', modelName);
+            // Show loading progress to user
-
+            if (window.updateModelLoadingProgress) {
+                window.updateModelLoadingProgress(0, `Loading ${modelName}...`);
+            }
+            
+            this.extractor = await window.transformers.pipeline('feature-extraction', modelName, {
+                progress_callback: (data) => {
+                    if (window.updateModelLoadingProgress && data.progress !== undefined) {
+                        const progress = Math.round(data.progress);
+                        window.updateModelLoadingProgress(progress, data.status || 'Loading...');
+                    }
+                }
+            });
+            
             this.modelCache.set(modelName, this.extractor);
             this.currentModel = modelName;
             this.isLoading = false;
-
+            
+            if (window.updateModelLoadingProgress) {
+                window.updateModelLoadingProgress(100, 'Model loaded successfully');
+            }
+            
             return { success: true, model: modelName };
         } catch (error) {
             this.isLoading = false;
@@ -100,8 +116,17 @@ class TransformersEmbedder {
                     }
                 });
                 
+                // Update progress
+                const progress = Math.min(100, ((i + batch.length) / texts.length) * 100);
+                if (window.updateEmbeddingProgress) {
+                    window.updateEmbeddingProgress(progress, `Processing ${i + batch.length}/${texts.length} texts`);
+                }
             }
-
+            
+            if (window.updateEmbeddingProgress) {
+                window.updateEmbeddingProgress(100, `Generated ${embeddings.length} embeddings successfully`);
+            }
+            
             return embeddings;
         } catch (error) {
             console.error('Embedding generation error:', error);
@@ -114,6 +139,30 @@ class TransformersEmbedder {
 window.transformersEmbedder = new TransformersEmbedder();
 console.log('📦 TransformersEmbedder instance created');
 
+// Global progress update functions
+window.updateModelLoadingProgress = function(progress, status) {
+    const progressBar = document.getElementById('model-loading-progress');
+    const statusText = document.getElementById('model-loading-status');
+    if (progressBar) {
+        progressBar.style.width = progress + '%';
+        progressBar.setAttribute('aria-valuenow', progress);
+    }
+    if (statusText) {
+        statusText.textContent = status;
+    }
+};
+
+window.updateEmbeddingProgress = function(progress, status) {
+    const progressBar = document.getElementById('embedding-progress');
+    const statusText = document.getElementById('embedding-status');
+    if (progressBar) {
+        progressBar.style.width = progress + '%';
+        progressBar.setAttribute('aria-valuenow', progress);
+    }
+    if (statusText) {
+        statusText.textContent = status;
+    }
+};
 
 // Dash clientside callback functions
 window.dash_clientside = window.dash_clientside || {};
@@ -121,28 +170,31 @@ console.log('🔧 Setting up window.dash_clientside.transformers');
 window.dash_clientside.transformers = {
     generateEmbeddings: async function(nClicks, textContent, modelName, tokenizationMethod, category, subcategory) {
         console.log('🚀 generateEmbeddings called with:', { nClicks, modelName, tokenizationMethod, textLength: textContent?.length });
-
+        
         if (!nClicks || !textContent || textContent.trim().length === 0) {
             console.log('⚠️ Early return - missing required parameters');
             return window.dash_clientside.no_update;
         }
-
+        
         try {
             // Initialize model if needed
             const initResult = await window.transformersEmbedder.initializeModel(modelName);
             if (!initResult.success) {
                 return [
-                    { error: `Model loading error: ${initResult.error}` },
+                    { error: initResult.error },
+                    `❌ Model loading error: ${initResult.error}`,
+                    "danger",
                     false
                 ];
             }
-
+            
             // Tokenize text based on method
             let textChunks;
             const trimmedText = textContent.trim();
-
+            
             switch (tokenizationMethod) {
                 case 'sentence':
+                    // Simple sentence splitting - can be enhanced with proper NLP
                     textChunks = trimmedText
                         .split(/[.!?]+/)
                         .map(s => s.trim())
@@ -163,24 +215,28 @@ window.dash_clientside.transformers = {
                 default:
                     textChunks = [trimmedText];
             }
-
+            
             if (textChunks.length === 0) {
                 return [
                     { error: 'No valid text chunks found after tokenization' },
+                    '❌ Error: No valid text chunks found after tokenization',
+                    "danger",
                     false
                 ];
             }
-
+            
             // Generate embeddings
             const embeddings = await window.transformersEmbedder.generateEmbeddings(textChunks);
-
+            
             if (!embeddings || embeddings.length !== textChunks.length) {
                 return [
-                    { error: 'Embedding generation failed' },
+                    { error: 'Embedding generation failed - mismatch in text chunks and embeddings' },
+                    '❌ Error: Embedding generation failed',
+                    "danger",
                     false
                 ];
             }
-
+            
             // Create documents structure
             const documents = textChunks.map((text, i) => ({
                 id: `text_input_${Date.now()}_${i}`,
@@ -190,36 +246,33 @@ window.dash_clientside.transformers = {
                 subcategory: subcategory || "Generated",
                 tags: []
             }));
-
+            
-            // Return the successful embeddings data
-            const embeddingsData = {
-                documents: documents,
-                embeddings: embeddings
-            };
-
-            console.log('✅ Embeddings generated successfully:', embeddingsData);
-
             return [
-                embeddingsData,
+                {
+                    documents: documents,
+                    embeddings: embeddings
+                },
+                `✅ Generated embeddings for ${documents.length} text chunks using ${modelName}`,
+                "success",
                 false
             ];
-
+            
         } catch (error) {
             console.error('Client-side embedding error:', error);
             return [
                 { error: error.message },
+                `❌ Error: ${error.message}`,
+                "danger", 
                 false
             ];
         }
     }
 };
 
-
 console.log('✅ Transformers.js client-side setup complete');
 console.log('Available:', {
     transformersEmbedder: !!window.transformersEmbedder,
     dashClientside: !!window.dash_clientside,
     transformersModule: !!window.dash_clientside?.transformers,
-    generateFunction: typeof window.dash_clientside?.transformers?.generateEmbeddings,
+    generateFunction: typeof window.dash_clientside?.transformers?.generateEmbeddings
-    processAsync: typeof window.processEmbeddingsAsync
 });

assets/transformers-loader.js

@@ -104,28 +104,17 @@ window.dash_clientside = window.dash_clientside || {};
 window.dash_clientside.transformers = {
     generateEmbeddings: async function(nClicks, textContent, modelName, tokenizationMethod, category, subcategory) {
         console.log('🚀 Client-side generateEmbeddings called');
-
+        
         if (!nClicks || !textContent || textContent.trim().length === 0) {
             console.log('⚠️ Missing required parameters');
             return window.dash_clientside.no_update;
         }
-
+        
         try {
-            // Ensure Transformers.js is loaded
-            if (!window.transformersLibraryLoaded) {
-                const loaded = await initializeTransformers();
-                if (!loaded) {
-                    return [
-                        { error: 'Failed to load Transformers.js' },
-                        false
-                    ];
-                }
-            }
-
             // Tokenize text
             let textChunks;
             const trimmedText = textContent.trim();
-
+            
             switch (tokenizationMethod) {
                 case 'sentence':
                     textChunks = trimmedText.split(/[.!?]+/).map(s => s.trim()).filter(s => s.length > 0);
@@ -139,50 +128,45 @@ window.dash_clientside.transformers = {
                 default:
                     textChunks = [trimmedText];
             }
-
+            
             if (textChunks.length === 0) {
-                return [
+                throw new Error('No valid text chunks after tokenization');
-                    { error: 'No valid text chunks after tokenization' },
-                    false
-                ];
             }
-
+            
             // Generate embeddings
             const embeddings = await window.simpleEmbedder.generateEmbeddings(textChunks, modelName);
-
+            
             // Create documents
             const documents = textChunks.map((text, i) => ({
                 id: `text_input_${Date.now()}_${i}`,
                 text: text,
                 embedding: embeddings[i],
                 category: category || "Text Input",
-                subcategory: subcategory || "Generated",
+                subcategory: subcategory || "Generated", 
                 tags: []
             }));
-
+            
-            // Return the successful embeddings data
-            const embeddingsData = {
-                documents: documents,
-                embeddings: embeddings
-            };
-
-            console.log('✅ Embeddings generated successfully:', embeddingsData);
-
             return [
-                embeddingsData,
+                {
+                    documents: documents,
+                    embeddings: embeddings
+                },
+                `✅ Generated embeddings for ${documents.length} text chunks using ${modelName}`,
+                "success",
                 false
             ];
-
+            
         } catch (error) {
             console.error('❌ Error generating embeddings:', error);
             return [
                 { error: error.message },
+                `❌ Error: ${error.message}`,
+                "danger",
                 false
             ];
         }
     }
 };
 
-
 console.log('✅ Simple Transformers.js setup complete');
 console.log('Available functions:', Object.keys(window.dash_clientside.transformers));

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "embeddingbuddy"
-version = "0.5.0"
+version = "0.4.0"
 description = "A Python Dash application for interactive exploration and visualization of embedding vectors through dimensionality reduction techniques."
 readme = "README.md"
 requires-python = ">=3.11"

run_prod.py

@@ -25,7 +25,7 @@ def main():
         "--workers", str(AppSettings.GUNICORN_WORKERS),
         "--bind", AppSettings.GUNICORN_BIND,
         "--timeout", str(AppSettings.GUNICORN_TIMEOUT),
-        "--keep-alive", str(AppSettings.GUNICORN_KEEPALIVE),
+        "--keepalive", str(AppSettings.GUNICORN_KEEPALIVE),
         "--access-logfile", "-",
         "--error-logfile", "-",
         "--log-level", "info",

src/embeddingbuddy/app.py

@@ -15,12 +15,7 @@ def create_app():
     assets_path = os.path.join(project_root, "assets")
 
     app = dash.Dash(
-        __name__,
+        __name__, external_stylesheets=[dbc.themes.BOOTSTRAP], assets_folder=assets_path
-        external_stylesheets=[
-            dbc.themes.BOOTSTRAP,
-            "https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css",
-        ],
-        assets_folder=assets_path,
     )
 
     # Allow callbacks to components that are dynamically created in tabs
@@ -50,22 +45,22 @@ def _register_client_side_callbacks(app):
             if (!nClicks || !textContent || !textContent.trim()) {
                 return window.dash_clientside.no_update;
             }
-
+            
             console.log('🔍 Checking for Transformers.js...');
             console.log('window.dash_clientside:', typeof window.dash_clientside);
             console.log('window.dash_clientside.transformers:', typeof window.dash_clientside?.transformers);
             console.log('generateEmbeddings function:', typeof window.dash_clientside?.transformers?.generateEmbeddings);
-
+            
-            if (typeof window.dash_clientside !== 'undefined' &&
+            if (typeof window.dash_clientside !== 'undefined' && 
                 typeof window.dash_clientside.transformers !== 'undefined' &&
                 typeof window.dash_clientside.transformers.generateEmbeddings === 'function') {
-
+                
                 console.log('✅ Calling Transformers.js generateEmbeddings...');
                 return window.dash_clientside.transformers.generateEmbeddings(
                     nClicks, textContent, modelName, tokenizationMethod, category, subcategory
                 );
             }
-
+            
             // More detailed error information
             let errorMsg = '❌ Transformers.js not available. ';
             if (typeof window.dash_clientside === 'undefined') {
@@ -75,17 +70,21 @@ def _register_client_side_callbacks(app):
             } else if (typeof window.dash_clientside.transformers.generateEmbeddings !== 'function') {
                 errorMsg += 'generateEmbeddings function not found.';
             }
-
+            
             console.error(errorMsg);
-
+            
             return [
                 { error: 'Transformers.js not loaded. Please refresh the page and try again.' },
+                errorMsg + ' Please refresh the page.',
+                'danger',
                 false
             ];
         }
         """,
         [
             Output("embeddings-generated-trigger", "data"),
+            Output("text-input-status-immediate", "children"),
+            Output("text-input-status-immediate", "color"),
             Output("generate-embeddings-btn", "disabled", allow_duplicate=True),
         ],
         [Input("generate-embeddings-btn", "n_clicks")],

src/embeddingbuddy/config/settings.py

@@ -72,12 +72,10 @@ class AppSettings:
     DEBUG = os.getenv("EMBEDDINGBUDDY_DEBUG", "True").lower() == "true"
     HOST = os.getenv("EMBEDDINGBUDDY_HOST", "127.0.0.1")
     PORT = int(os.getenv("EMBEDDINGBUDDY_PORT", "8050"))
-
+    
     # Environment Configuration
-    ENVIRONMENT = os.getenv(
+    ENVIRONMENT = os.getenv("EMBEDDINGBUDDY_ENV", "development")  # development, production
-        "EMBEDDINGBUDDY_ENV", "development"
+    
-    )  # development, production
-
     # WSGI Server Configuration (for production)
     GUNICORN_WORKERS = int(os.getenv("GUNICORN_WORKERS", "4"))
     GUNICORN_BIND = os.getenv("GUNICORN_BIND", f"{HOST}:{PORT}")

src/embeddingbuddy/ui/callbacks/data_processing.py

@@ -621,12 +621,6 @@ class DataProcessingCallbacks:
             if not embeddings_data:
                 return no_update, no_update, no_update, no_update, no_update
 
-            # Check if this is a request trigger (contains textContent) vs actual embeddings data
-            if isinstance(embeddings_data, dict) and "textContent" in embeddings_data:
-                # This is a processing request trigger, not the actual results
-                # The JavaScript will handle the async processing and update the UI directly
-                return no_update, no_update, no_update, no_update, no_update
-
             processed_data = self.processor.process_client_embeddings(embeddings_data)
 
             if processed_data.error:

src/embeddingbuddy/ui/callbacks/interactions.py

@@ -1,5 +1,6 @@
 import dash
-from dash import callback, Input, Output
+from dash import callback, Input, Output, State, html
+import dash_bootstrap_components as dbc
 
 
 class InteractionCallbacks:
@@ -7,16 +8,75 @@ class InteractionCallbacks:
         self._register_callbacks()
 
     def _register_callbacks(self):
+        @callback(
+            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"]
+            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"
+            else:
+                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,
         )
         def reset_data(n_clicks):
             if n_clicks is None or n_clicks == 0:
-                return dash.no_update, dash.no_update
+                return dash.no_update, dash.no_update, dash.no_update
 
-            return None, None
+            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"),
+                    ]
+                )
+            ]
+        )

src/embeddingbuddy/ui/components/datasource.py

@@ -1,11 +1,13 @@
 from dash import dcc, html
 import dash_bootstrap_components as dbc
 from .upload import UploadComponent
+from .textinput import TextInputComponent
 
 
 class DataSourceComponent:
     def __init__(self):
         self.upload_component = UploadComponent()
+        self.text_input_component = TextInputComponent()
 
     def create_tabbed_interface(self):
         """Create tabbed interface for different data sources."""
@@ -17,6 +19,7 @@ class DataSourceComponent:
                             [
                                 dbc.Tab(label="File Upload", tab_id="file-tab"),
                                 dbc.Tab(label="OpenSearch", tab_id="opensearch-tab"),
+                                dbc.Tab(label="Text Input", tab_id="text-input-tab"),
                             ],
                             id="data-source-tabs",
                             active_tab="file-tab",
@@ -208,6 +211,10 @@ class DataSourceComponent:
             ]
         )
 
+    def create_text_input_tab(self):
+        """Create text input tab content for browser-based embedding generation."""
+        return html.Div([self.text_input_component.create_text_input_interface()])
+
     def _create_opensearch_section(self, section_type):
         """Create a complete OpenSearch section for either 'data' or 'prompts'."""
         section_id = section_type  # 'data' or 'prompts'

src/embeddingbuddy/ui/components/sidebar.py

@@ -2,26 +2,31 @@ from dash import dcc, html
 import dash_bootstrap_components as dbc
 from .upload import UploadComponent
 from .datasource import DataSourceComponent
-from .textinput import TextInputComponent
 
 
 class SidebarComponent:
     def __init__(self):
         self.upload_component = UploadComponent()
         self.datasource_component = DataSourceComponent()
-        self.textinput_component = TextInputComponent()
 
     def create_layout(self):
         return dbc.Col(
             [
-                dbc.Accordion(
+                html.H5("Data Sources", className="mb-3"),
-                    [
+                self.datasource_component.create_error_alert(),
-                        self._create_data_sources_item(),
+                self.datasource_component.create_success_alert(),
-                        self._create_generate_embeddings_item(),
+                self.datasource_component.create_tabbed_interface(),
-                        self._create_visualization_controls_item(),
+                html.H5("Visualization Controls", className="mb-3 mt-4"),
-                    ],
+            ]
-                    always_open=True,
+            + 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"},
@@ -81,63 +86,3 @@ class SidebarComponent:
                 style={"margin-bottom": "20px"},
             ),
         ]
-
-    def _create_generate_embeddings_item(self):
-        return dbc.AccordionItem(
-            [
-                self.textinput_component.create_text_input_interface(),
-            ],
-            title=html.Span(
-                [
-                    "Generate Embeddings ",
-                    html.I(
-                        className="fas fa-info-circle text-muted",
-                        style={"cursor": "pointer"},
-                        id="generate-embeddings-info-icon",
-                        title="Create new embeddings from text input using various in-browser models",
-                    ),
-                ]
-            ),
-            item_id="generate-embeddings-accordion",
-        )
-
-    def _create_data_sources_item(self):
-        return dbc.AccordionItem(
-            [
-                self.datasource_component.create_error_alert(),
-                self.datasource_component.create_success_alert(),
-                self.datasource_component.create_tabbed_interface(),
-            ],
-            title=html.Span(
-                [
-                    "Load Embeddings ",
-                    html.I(
-                        className="fas fa-info-circle text-muted",
-                        style={"cursor": "pointer"},
-                        id="load-embeddings-info-icon",
-                        title="Load existing embeddings: upload files or read from OpenSearch",
-                    ),
-                ]
-            ),
-            item_id="data-sources-accordion",
-        )
-
-    def _create_visualization_controls_item(self):
-        return dbc.AccordionItem(
-            self._create_method_dropdown()
-            + self._create_color_dropdown()
-            + self._create_dimension_toggle()
-            + self._create_prompts_toggle(),
-            title=html.Span(
-                [
-                    "Visualization Controls ",
-                    html.I(
-                        className="fas fa-info-circle text-muted",
-                        style={"cursor": "pointer"},
-                        id="visualization-controls-info-icon",
-                        title="Configure plot settings: select dimensionality reduction method, colors, and display options",
-                    ),
-                ]
-            ),
-            item_id="visualization-controls-accordion",
-        )

src/embeddingbuddy/ui/components/textinput.py

@@ -30,6 +30,9 @@ class TextInputComponent:
                 # Generation controls
                 self._create_generation_controls(),
                 html.Hr(),
+                # Progress indicators
+                self._create_progress_indicators(),
+                html.Hr(),
                 # Status and results
                 self._create_status_section(),
                 # Hidden components for data flow
@@ -294,10 +297,65 @@ class TextInputComponent:
             ]
         )
 
+    def _create_progress_indicators(self):
+        """Create progress bars for model loading and embedding generation."""
+        return html.Div(
+            [
+                # Model loading progress
+                html.Div(
+                    [
+                        html.H6("Model Loading Progress", className="mb-2"),
+                        dbc.Progress(
+                            id="model-loading-progress",
+                            value=0,
+                            striped=True,
+                            animated=True,
+                            className="mb-2",
+                        ),
+                        html.Small(
+                            id="model-loading-status",
+                            children="No model loading in progress",
+                            className="text-muted",
+                        ),
+                    ],
+                    id="model-loading-section",
+                    style={"display": "none"},
+                ),
+                html.Br(),
+                # Embedding generation progress
+                html.Div(
+                    [
+                        html.H6("Embedding Generation Progress", className="mb-2"),
+                        dbc.Progress(
+                            id="embedding-progress",
+                            value=0,
+                            striped=True,
+                            animated=True,
+                            className="mb-2",
+                        ),
+                        html.Small(
+                            id="embedding-status",
+                            children="No embedding generation in progress",
+                            className="text-muted",
+                        ),
+                    ],
+                    id="embedding-progress-section",
+                    style={"display": "none"},
+                ),
+            ]
+        )
+
     def _create_status_section(self):
         """Create status alerts and results preview."""
         return html.Div(
             [
+                # Immediate status (from client-side)
+                dbc.Alert(
+                    id="text-input-status-immediate",
+                    children="Ready to generate embeddings",
+                    color="light",
+                    className="mb-3",
+                ),
                 # Server-side status
                 dbc.Alert(
                     id="text-input-status",

src/embeddingbuddy/ui/components/upload.py

@@ -5,75 +5,39 @@ import dash_bootstrap_components as dbc
 class UploadComponent:
     @staticmethod
     def create_data_upload():
-        return html.Div(
+        return dcc.Upload(
-            [
+            id="upload-data",
-                dcc.Upload(
+            children=html.Div(["Drag and Drop or ", html.A("Select Files")]),
-                    id="upload-data",
+            style={
-                    children=html.Div(
+                "width": "100%",
-                        [
+                "height": "60px",
-                            "Upload Data ",
+                "lineHeight": "60px",
-                            html.I(
+                "borderWidth": "1px",
-                                className="fas fa-info-circle",
+                "borderStyle": "dashed",
-                                style={"color": "#6c757d", "fontSize": "14px"},
+                "borderRadius": "5px",
-                                id="data-upload-info",
+                "textAlign": "center",
-                            ),
+                "margin-bottom": "20px",
-                        ]
+            },
-                    ),
+            multiple=False,
-                    style={
-                        "width": "100%",
-                        "height": "60px",
-                        "lineHeight": "60px",
-                        "borderWidth": "1px",
-                        "borderStyle": "dashed",
-                        "borderRadius": "5px",
-                        "textAlign": "center",
-                        "margin-bottom": "20px",
-                    },
-                    multiple=False,
-                ),
-                dbc.Tooltip(
-                    "Click here or drag and drop NDJSON files containing document embeddings",
-                    target="data-upload-info",
-                    placement="top",
-                ),
-            ]
         )
 
     @staticmethod
     def create_prompts_upload():
-        return html.Div(
+        return dcc.Upload(
-            [
+            id="upload-prompts",
-                dcc.Upload(
+            children=html.Div(["Drag and Drop Prompts or ", html.A("Select Files")]),
-                    id="upload-prompts",
+            style={
-                    children=html.Div(
+                "width": "100%",
-                        [
+                "height": "60px",
-                            "Upload Prompts ",
+                "lineHeight": "60px",
-                            html.I(
+                "borderWidth": "1px",
-                                className="fas fa-info-circle",
+                "borderStyle": "dashed",
-                                style={"color": "#6c757d", "fontSize": "14px"},
+                "borderRadius": "5px",
-                                id="prompts-upload-info",
+                "textAlign": "center",
-                            ),
+                "margin-bottom": "20px",
-                        ]
+                "borderColor": "#28a745",
-                    ),
+            },
-                    style={
+            multiple=False,
-                        "width": "100%",
-                        "height": "60px",
-                        "lineHeight": "60px",
-                        "borderWidth": "1px",
-                        "borderStyle": "dashed",
-                        "borderRadius": "5px",
-                        "textAlign": "center",
-                        "margin-bottom": "20px",
-                        "borderColor": "#28a745",
-                    },
-                    multiple=False,
-                ),
-                dbc.Tooltip(
-                    "Click here or drag and drop NDJSON files containing prompt embeddings",
-                    target="prompts-upload-info",
-                    placement="top",
-                ),
-            ]
         )
 
     @staticmethod

src/embeddingbuddy/visualization/plots.py

@@ -38,9 +38,9 @@ class PlotFactory:
         if dimensions == "3d":
             fig = px.scatter_3d(
                 df,
-                x="x",
+                x="dim_1",
-                y="y",
+                y="dim_2",
-                z="z",
+                z="dim_3",
                 color=color_values,
                 hover_data=hover_fields,
                 title=f"3D Embedding Visualization - {method} (colored by {color_by})",
@@ -49,8 +49,8 @@ class PlotFactory:
         else:
             fig = px.scatter(
                 df,
-                x="x",
+                x="dim_1",
-                y="y",
+                y="dim_2",
                 color=color_values,
                 hover_data=hover_fields,
                 title=f"2D Embedding Visualization - {method} (colored by {color_by})",
@@ -77,17 +77,17 @@ class PlotFactory:
         if dimensions == "3d":
             doc_fig = px.scatter_3d(
                 doc_df,
-                x="x",
+                x="dim_1",
-                y="y",
+                y="dim_2",
-                z="z",
+                z="dim_3",
                 color=doc_color_values,
                 hover_data=hover_fields,
             )
         else:
             doc_fig = px.scatter(
                 doc_df,
-                x="x",
+                x="dim_1",
-                y="y",
+                y="dim_2",
                 color=doc_color_values,
                 hover_data=hover_fields,
             )
@@ -114,17 +114,17 @@ class PlotFactory:
             if dimensions == "3d":
                 prompt_fig = px.scatter_3d(
                     prompt_df,
-                    x="x",
+                    x="dim_1",
-                    y="y",
+                    y="dim_2",
-                    z="z",
+                    z="dim_3",
                     color=prompt_color_values,
                     hover_data=hover_fields,
                 )
             else:
                 prompt_fig = px.scatter(
                     prompt_df,
-                    x="x",
+                    x="dim_1",
-                    y="y",
+                    y="dim_2",
                     color=prompt_color_values,
                     hover_data=hover_fields,
                 )
@@ -168,11 +168,11 @@ class PlotFactory:
                 "category": doc.category,
                 "subcategory": doc.subcategory,
                 "tags_str": ", ".join(doc.tags) if doc.tags else "None",
-                "x": coordinates[i, 0],
+                "dim_1": coordinates[i, 0],
-                "y": coordinates[i, 1],
+                "dim_2": coordinates[i, 1],
             }
             if dimensions == "3d":
-                row["z"] = coordinates[i, 2]
+                row["dim_3"] = coordinates[i, 2]
             df_data.append(row)
 
         return pd.DataFrame(df_data)

uv.lock

@@ -412,7 +412,7 @@ wheels = [
 
 [[package]]
 name = "embeddingbuddy"
-version = "0.5.0"
+version = "0.3.0"
 source = { editable = "." }
 dependencies = [
     { name = "dash" },