Austin Godber
cdaaffd735
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403 lines
16 KiB
Python
403 lines
16 KiB
Python
"""Text input component for generating embeddings from user text."""
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import dash_bootstrap_components as dbc
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from dash import dcc, html
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from embeddingbuddy.config.settings import AppSettings
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class TextInputComponent:
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"""Component for text input and embedding generation."""
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def __init__(self):
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self.settings = AppSettings()
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def create_text_input_interface(self):
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"""Create the complete text input interface with model selection and processing options."""
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return html.Div(
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[
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# Model selection section
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self._create_model_selection(),
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html.Hr(),
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# Text input section
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self._create_text_input_area(),
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# Text action buttons
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self._create_text_action_buttons(),
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html.Hr(),
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# Processing options
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self._create_processing_options(),
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html.Hr(),
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# Generation controls
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self._create_generation_controls(),
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html.Hr(),
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# Progress indicators
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self._create_progress_indicators(),
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html.Hr(),
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# Status and results
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self._create_status_section(),
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# Hidden components for data flow
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self._create_hidden_components(),
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],
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className="p-3",
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)
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def _create_model_selection(self):
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"""Create model selection dropdown with descriptions."""
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model_options = []
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for model in self.settings.AVAILABLE_MODELS:
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label = f"{model['label']} - {model['size']}"
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if model.get("default", False):
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label += " (Recommended)"
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model_options.append({"label": label, "value": model["name"]})
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return html.Div(
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[
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html.H5("Embedding Model", className="mb-3"),
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html.Div(
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[
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dcc.Dropdown(
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id="model-selection",
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options=model_options,
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value=self.settings.DEFAULT_EMBEDDING_MODEL,
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placeholder="Select an embedding model...",
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className="mb-2",
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),
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dbc.Alert(
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[
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html.Div(
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id="model-info",
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children=self._get_model_description(
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self.settings.DEFAULT_EMBEDDING_MODEL
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),
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)
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],
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color="info",
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className="small",
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),
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]
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),
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]
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)
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def _create_text_input_area(self):
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"""Create text input textarea with character limits."""
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return html.Div(
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[
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html.H5("Text Input", className="mb-3"),
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dcc.Textarea(
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id="text-input-area",
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placeholder="Paste your text here... Each sentence, paragraph, or line will become a separate data point depending on your tokenization method below.",
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value="",
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style={
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"width": "100%",
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"height": "300px",
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"resize": "vertical",
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"font-family": "monospace",
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"font-size": "14px",
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},
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maxLength=self.settings.MAX_TEXT_LENGTH,
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className="form-control",
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),
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html.Small(
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f"Maximum {self.settings.MAX_TEXT_LENGTH:,} characters. Current: ",
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className="text-muted",
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),
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html.Small(
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id="text-length-counter",
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children="0",
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className="text-muted fw-bold",
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),
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html.Small(" characters", className="text-muted"),
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]
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)
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def _create_text_action_buttons(self):
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"""Create action buttons for text input (Load Sample, Clear)."""
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return html.Div(
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[
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dbc.Row(
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[
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dbc.Col(
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[
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dbc.Button(
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[
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html.I(className="fas fa-file-text me-2"),
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"Load Sample Text",
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],
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id="load-sample-btn",
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color="info",
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size="sm",
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className="w-100",
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)
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],
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md=6,
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),
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dbc.Col(
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[
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dbc.Button(
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[
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html.I(className="fas fa-trash me-2"),
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"Clear Text",
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],
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id="clear-text-btn",
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color="outline-secondary",
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size="sm",
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className="w-100",
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)
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],
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md=6,
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),
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],
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className="mt-2 mb-3",
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)
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]
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)
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def _create_processing_options(self):
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"""Create tokenization and metadata options."""
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return html.Div(
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[
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html.H5("Processing Options", className="mb-3"),
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dbc.Row(
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[
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dbc.Col(
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[
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html.Label(
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"Text Splitting Method:", className="form-label"
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),
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dcc.Dropdown(
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id="tokenization-method",
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options=[
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{
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"label": "Sentences (split on . ! ?)",
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"value": "sentence",
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},
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{
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"label": "Paragraphs (split on double newline)",
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"value": "paragraph",
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},
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{
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"label": "Lines (split on single newline)",
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"value": "manual",
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},
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{
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"label": "Entire text as one document",
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"value": "whole",
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},
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],
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value=self.settings.DEFAULT_TOKENIZATION_METHOD,
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className="mb-3",
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),
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],
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md=6,
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),
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dbc.Col(
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[
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html.Label("Batch Size:", className="form-label"),
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dcc.Dropdown(
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id="batch-size",
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options=[
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{
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"label": "Small batches (4) - Lower memory",
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"value": 4,
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},
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{
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"label": "Medium batches (8) - Balanced",
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"value": 8,
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},
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{
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"label": "Large batches (16) - Faster",
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"value": 16,
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},
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],
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value=self.settings.MAX_BATCH_SIZE,
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className="mb-3",
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),
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],
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md=6,
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),
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]
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),
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dbc.Row(
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[
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dbc.Col(
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[
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html.Label(
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"Category (Optional):", className="form-label"
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),
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dcc.Input(
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id="text-category",
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type="text",
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placeholder="e.g., Notes, Articles, Ideas...",
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value="Text Input",
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className="form-control mb-3",
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),
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],
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md=6,
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),
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dbc.Col(
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[
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html.Label(
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"Subcategory (Optional):", className="form-label"
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),
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dcc.Input(
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id="text-subcategory",
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type="text",
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placeholder="e.g., Meeting Notes, Research...",
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value="Generated",
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className="form-control mb-3",
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),
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],
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md=6,
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),
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]
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),
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]
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)
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def _create_generation_controls(self):
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"""Create embedding generation button and controls."""
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return html.Div(
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[
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html.H5("Generate Embeddings", className="mb-3"),
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dbc.Row(
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[
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dbc.Col(
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[
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dbc.Button(
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[
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html.I(className="fas fa-magic me-2"),
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"Generate Embeddings",
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],
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id="generate-embeddings-btn",
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color="primary",
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size="lg",
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disabled=True,
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className="w-100",
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)
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],
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md=12,
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),
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]
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),
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html.Div(
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[
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dbc.Alert(
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[
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html.I(className="fas fa-info-circle me-2"),
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"Enter some text above and select a model to enable embedding generation.",
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],
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color="light",
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className="mt-3",
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id="generation-help",
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)
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]
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),
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]
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)
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def _create_progress_indicators(self):
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"""Create progress bars for model loading and embedding generation."""
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return html.Div(
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[
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# Model loading progress
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html.Div(
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[
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html.H6("Model Loading Progress", className="mb-2"),
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dbc.Progress(
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id="model-loading-progress",
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value=0,
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striped=True,
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animated=True,
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className="mb-2",
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),
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html.Small(
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id="model-loading-status",
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children="No model loading in progress",
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className="text-muted",
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),
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],
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id="model-loading-section",
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style={"display": "none"},
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),
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html.Br(),
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# Embedding generation progress
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html.Div(
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[
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html.H6("Embedding Generation Progress", className="mb-2"),
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dbc.Progress(
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id="embedding-progress",
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value=0,
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striped=True,
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animated=True,
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className="mb-2",
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),
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html.Small(
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id="embedding-status",
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children="No embedding generation in progress",
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className="text-muted",
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),
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],
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id="embedding-progress-section",
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style={"display": "none"},
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),
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]
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)
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def _create_status_section(self):
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"""Create status alerts and results preview."""
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return html.Div(
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[
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# Immediate status (from client-side)
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dbc.Alert(
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id="text-input-status-immediate",
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children="Ready to generate embeddings",
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color="light",
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className="mb-3",
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),
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# Server-side status
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dbc.Alert(
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id="text-input-status",
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children="",
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color="light",
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className="mb-3",
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style={"display": "none"},
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),
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# Results preview
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html.Div(id="embedding-results-preview"),
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]
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)
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def _create_hidden_components(self):
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"""Create hidden components for data flow."""
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return html.Div(
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[
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# Store for embeddings data from client-side
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dcc.Store(id="embeddings-generated-trigger"),
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# Store for tokenization preview
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dcc.Store(id="tokenization-preview-data"),
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]
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)
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def _get_model_description(self, model_name):
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"""Get description for a specific model."""
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for model in self.settings.AVAILABLE_MODELS:
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if model["name"] == model_name:
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return html.Div(
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[
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html.Strong(
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f"Dimensions: {model['dimensions']} | Context Length: {model['context_length']}"
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),
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html.Br(),
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html.Span(model["description"]),
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html.Br(),
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html.Small(
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f"Multilingual: {'Yes' if model.get('multilingual', False) else 'No'} | Size: {model['size']}",
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className="text-muted",
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),
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]
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)
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return html.Span("Model information not available", className="text-muted")
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