Initial app

app.py

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+"""Gradio demo application for RusCxnPipe."""
+
+import gradio as gr
+import logging
+from typing import List, Dict, Any
+
+
+# Set up logging to avoid cluttering the interface
+logging.getLogger("transformers").setLevel(logging.WARNING)
+logging.getLogger("sentence_transformers").setLevel(logging.WARNING)
+logging.getLogger("torch").setLevel(logging.WARNING)
+
+try:
+    from ruscxnpipe import RusCxnPipe, SpanPredictor
+except ImportError:
+    # For development/testing when library isn't installed
+    import sys
+    import os
+    sys.path.append(
+        os.path.dirname(
+            os.path.dirname(
+                os.path.abspath(__file__))))
+    from ruscxnpipe import RusCxnPipe, SpanPredictor
+
+
+# Initialize models at startup
+print("🚀 Initializing RusCxnPipe models...")
+try:
+    PIPELINE = RusCxnPipe(
+        semantic_model="Futyn-Maker/ruscxn-embedder",
+        classification_model="Futyn-Maker/ruscxn-classifier",
+        span_model="Futyn-Maker/ruscxn-span-predictor"
+    )
+    SPAN_PREDICTOR = SpanPredictor(
+        model_name="Futyn-Maker/ruscxn-span-predictor")
+    print("✅ Models initialized successfully!")
+    MODELS_LOADED = True
+    MODEL_ERROR = None
+except Exception as e:
+    print(f"❌ Error initializing models: {str(e)}")
+    PIPELINE = None
+    SPAN_PREDICTOR = None
+    MODELS_LOADED = False
+    MODEL_ERROR = str(e)
+
+
+def highlight_span(
+        text: str,
+        span_start: int,
+        span_end: int,
+        span_string: str) -> str:
+    """Highlight a span in text using HTML."""
+    if span_start < 0 or span_end > len(text) or span_start >= span_end:
+        return text
+
+    # Ensure the span matches
+    actual_span = text[span_start:span_end]
+    if actual_span.strip() != span_string.strip():
+        # Fallback: try to find the span in the text
+        span_start = text.find(span_string)
+        if span_start >= 0:
+            span_end = span_start + len(span_string)
+        else:
+            return text
+
+    # Create highlighted version
+    before = text[:span_start]
+    highlighted = text[span_start:span_end]
+    after = text[span_end:]
+
+    return f'{before}<mark style="background-color: #ffeb3b; padding: 2px 4px; border-radius: 3px; font-weight: bold;">{highlighted}</mark>{after}'
+
+
+def create_construction_link(construction_id: str, pattern: str) -> str:
+    """Create a clickable link to the construction page."""
+    url = f"https://constructicon.ruscorpora.ru/construction/{construction_id}"
+    return f'<a href="{url}" target="_blank" style="color: #1976d2; text-decoration: none; font-weight: bold; border-bottom: 1px dotted #1976d2;">{pattern}</a>'
+
+
+def format_pipeline_results(results: Dict[str, Any]) -> str:
+    """Format the pipeline results as HTML."""
+    if not results or not results['constructions']:
+        return "<div style='padding: 20px; text-align: center; color: #666;'>No constructions found in the text.</div>"
+
+    constructions = results['constructions']
+    original_text = results['example']
+
+    html_parts = []
+    html_parts.append("<div style='font-family: Arial, sans-serif;'>")
+
+    # Header
+    html_parts.append(
+        f"<h3 style='color: #333; margin-bottom: 20px;'>Found {
+            len(constructions)} construction(s):</h3>")
+
+    # Process each construction
+    for i, construction in enumerate(constructions, 1):
+        construction_id = construction['id']
+        pattern = construction['pattern']
+        span_info = construction['span']
+
+        # Construction header with link
+        html_parts.append(
+            "<div style='margin-bottom: 25px; padding: 15px; border: 1px solid #e0e0e0; border-radius: 8px; background-color: #fafafa;'>")
+        html_parts.append(
+            f"<h4 style='margin: 0 0 10px 0; color: #333;'>{i}. {
+                create_construction_link(
+                    construction_id, pattern)}</h4>")
+
+        # Highlighted text
+        if span_info['span_string']:
+            highlighted_text = highlight_span(
+                original_text,
+                span_info['span_start'],
+                span_info['span_end'],
+                span_info['span_string']
+            )
+            html_parts.append(
+                f"<div style='font-size: 16px; line-height: 1.5; margin-top: 10px; padding: 10px; background-color: white; border-radius: 4px; border: 1px solid #ddd;'>{highlighted_text}</div>")
+
+            # Span details
+            html_parts.append(
+                "<div style='margin-top: 8px; font-size: 12px; color: #666;'>")
+            html_parts.append(
+                f"Span: \"{
+                    span_info['span_string']}\" (positions {
+                    span_info['span_start']}-{
+                    span_info['span_end']})")
+            html_parts.append("</div>")
+        else:
+            html_parts.append(
+                f"<div style='font-size: 16px; line-height: 1.5; margin-top: 10px; padding: 10px; background-color: white; border-radius: 4px; border: 1px solid #ddd;'>{original_text}</div>")
+            html_parts.append(
+                "<div style='margin-top: 8px; font-size: 12px; color: #999;'>No specific span identified</div>")
+
+        html_parts.append("</div>")
+
+    html_parts.append("</div>")
+    return "".join(html_parts)
+
+
+def format_span_results(text: str, results: List[Dict[str, Any]]) -> str:
+    """Format span prediction results as HTML."""
+    if not results or not results[0]['patterns']:
+        return "<div style='padding: 20px; text-align: center; color: #666;'>No patterns processed.</div>"
+
+    patterns = results[0]['patterns']
+
+    html_parts = []
+    html_parts.append("<div style='font-family: Arial, sans-serif;'>")
+
+    # Header
+    html_parts.append(
+        f"<h3 style='color: #333; margin-bottom: 20px;'>Span predictions for {
+            len(patterns)} pattern(s):</h3>")
+
+    # Process each pattern
+    for i, pattern_info in enumerate(patterns, 1):
+        pattern = pattern_info['pattern']
+        span_info = pattern_info['span']
+
+        html_parts.append(
+            "<div style='margin-bottom: 25px; padding: 15px; border: 1px solid #e0e0e0; border-radius: 8px; background-color: #fafafa;'>")
+        html_parts.append(
+            f"<h4 style='margin: 0 0 10px 0; color: #333;'>{i}. {pattern}</h4>")
+
+        # Highlighted text
+        if span_info['span_string']:
+            highlighted_text = highlight_span(
+                text,
+                span_info['span_start'],
+                span_info['span_end'],
+                span_info['span_string']
+            )
+            html_parts.append(
+                f"<div style='font-size: 16px; line-height: 1.5; margin-top: 10px; padding: 10px; background-color: white; border-radius: 4px; border: 1px solid #ddd;'>{highlighted_text}</div>")
+
+            # Span details
+            html_parts.append(
+                "<div style='margin-top: 8px; font-size: 12px; color: #666;'>")
+            html_parts.append(
+                f"Span: \"{
+                    span_info['span_string']}\" (positions {
+                    span_info['span_start']}-{
+                    span_info['span_end']})")
+            html_parts.append("</div>")
+        else:
+            html_parts.append(
+                f"<div style='font-size: 16px; line-height: 1.5; margin-top: 10px; padding: 10px; background-color: white; border-radius: 4px; border: 1px solid #ddd;'>{text}</div>")
+            html_parts.append(
+                "<div style='margin-top: 8px; font-size: 12px; color: #999;'>No span found for this pattern</div>")
+
+        html_parts.append("</div>")
+
+    html_parts.append("</div>")
+    return "".join(html_parts)
+
+
+def process_full_pipeline(text: str, n_candidates: int) -> str:
+    """Process text through the full pipeline."""
+    if not text.strip():
+        return "<div style='padding: 20px; text-align: center; color: #666;'>Please enter some text to analyze.</div>"
+
+    if not MODELS_LOADED:
+        return f"<div style='color: red; padding: 20px;'>Error: {MODEL_ERROR}</div>"
+
+    try:
+        results = PIPELINE.process_text(
+            text.strip(), n_candidates=n_candidates)
+        return format_pipeline_results(results)
+    except Exception as e:
+        return f"<div style='color: red; padding: 20px;'>Error processing text: {
+            str(e)}</div>"
+
+
+def process_span_prediction(text: str, patterns_text: str) -> str:
+    """Process text for span prediction only."""
+    if not text.strip():
+        return "<div style='padding: 20px; text-align: center; color: #666;'>Please enter some text to analyze.</div>"
+
+    if not patterns_text.strip():
+        return "<div style='padding: 20px; text-align: center; color: #666;'>Please enter some patterns to search for.</div>"
+
+    if not MODELS_LOADED:
+        return f"<div style='color: red; padding: 20px;'>Error: {MODEL_ERROR}</div>"
+
+    # Parse patterns
+    patterns = [p.strip()
+                for p in patterns_text.strip().split('\n') if p.strip()]
+    if not patterns:
+        return "<div style='padding: 20px; text-align: center; color: #666;'>No valid patterns found.</div>"
+
+    # Prepare input for span predictor
+    examples_with_patterns = [{'example': text.strip(),
+                               'patterns': [{'id': f'pattern_{i}',
+                                             'pattern': pattern} for i,
+                                            pattern in enumerate(patterns)]}]
+
+    try:
+        results = SPAN_PREDICTOR.predict_spans(examples_with_patterns)
+        return format_span_results(text.strip(), results)
+    except Exception as e:
+        return f"<div style='color: red; padding: 20px;'>Error processing spans: {
+            str(e)}</div>"
+
+# Create the Gradio interface
+
+
+def create_demo():
+    """Create the Gradio demo interface."""
+
+    # Custom CSS
+    css = """
+    .gradio-container {
+        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    }
+    .gr-button {
+        background: linear-gradient(90deg, #1976d2, #42a5f5);
+        border: none;
+        color: white;
+        font-weight: bold;
+    }
+    .gr-button:hover {
+        background: linear-gradient(90deg, #1565c0, #2196f3);
+    }
+    """
+
+    with gr.Blocks(css=css, title="RusCxnPipe Demo", theme=gr.themes.Soft()) as demo:
+
+        # Header
+        gr.Markdown("""
+        # 🔍 RusCxnPipe: Russian Constructicon Pattern Extractor
+
+        **Automatically identify and locate Russian constructicon patterns in text**
+
+        This tool uses advanced NLP models to find linguistic constructions from the Russian Constructicon database in your text.
+        It performs semantic search, classification, and span prediction to provide accurate results with precise text locations.
+
+        """)
+
+        with gr.Tabs():
+            # Tab 1: Full Pipeline
+            with gr.Tab("🚀 Full Pipeline", id="pipeline"):
+                gr.Markdown("""
+                ### Complete Analysis
+                Enter Russian text to automatically find all constructicon patterns present in it.
+                The system will search through the database, classify candidates, and highlight exact locations.
+                """)
+
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        text_input = gr.Textbox(
+                            label="Text",
+                            placeholder="Мои друзья разъехались и исчезли кто где.",
+                            lines=3,
+                            value="Мои друзья разъехались и исчезли кто где.")
+
+                        n_candidates = gr.Slider(
+                            minimum=5,
+                            maximum=50,
+                            value=15,
+                            step=5,
+                            label="Number of semantic search candidates",
+                            info="More candidates = more thorough search but slower processing and higher probability of false-positives"
+                        )
+
+                        analyze_btn = gr.Button(
+                            "🔍 Analyze Text", variant="primary", size="lg")
+
+                    with gr.Column(scale=3):
+                        results_html = gr.HTML(
+                            label="Results",
+                            value="<div style='padding: 40px; text-align: center; color: #666; border: 2px dashed #ccc; border-radius: 8px;'>Enter text and click 'Analyze Text' to see results</div>"
+                        )
+
+                # Examples
+                gr.Markdown("### 📝 Try these examples:")
+                example_texts = [
+                    "Мои друзья разъехались и исчезли кто где.",
+                    "Мягко говоря, это была ошибка.",
+                    "Петр так и замер на месте.",
+                    "Таня танцевала без устали, танцевала со всеми подряд."
+                ]
+
+                with gr.Row():
+                    for example in example_texts:
+                        gr.Button(f'"{example}"', size="sm").click(
+                            lambda x=example: x, outputs=text_input
+                        )
+
+                analyze_btn.click(
+                    fn=process_full_pipeline,
+                    inputs=[text_input, n_candidates],
+                    outputs=results_html
+                )
+
+            # Tab 2: Span Prediction Only
+            with gr.Tab("🎯 Span Prediction", id="spans"):
+                gr.Markdown("""
+                ### Pattern Span Detection
+                Enter text and specific patterns to find where exactly these patterns occur in the text.
+                This skips the search and classification steps, directly predicting span boundaries.
+                """)
+
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        span_text_input = gr.Textbox(
+                            label="Text",
+                            placeholder="Мои друзья разъехались и исчезли кто где.",
+                            lines=3,
+                            value="Мои друзья разъехались и исчезли кто где.")
+
+                        patterns_input = gr.Textbox(
+                            label="Patterns (one per line)",
+                            placeholder="VP кто PronInt\nVP кто где",
+                            lines=5,
+                            value="VP кто PronInt\nVP кто где"
+                        )
+
+                        predict_btn = gr.Button(
+                            "🎯 Predict Spans", variant="primary", size="lg")
+
+                    with gr.Column(scale=3):
+                        span_results_html = gr.HTML(
+                            label="Span Results",
+                            value="<div style='padding: 40px; text-align: center; color: #666; border: 2px dashed #ccc; border-radius: 8px;'>Enter text and patterns, then click 'Predict Spans' to see results</div>"
+                        )
+
+                predict_btn.click(
+                    fn=process_span_prediction,
+                    inputs=[span_text_input, patterns_input],
+                    outputs=span_results_html
+                )
+
+        # Footer
+        gr.Markdown("""
+        ---
+        **About RusCxnPipe**: This tool is based on fine-tuned transformer models trained on Russian Constructicon data.
+        The pipeline combines semantic search, classification, and span prediction to achieve high accuracy in construction detection.
+
+        **Models used**:
+        - Semantic: [ruscxn-embedder](https://huggingface.co/Futyn-Maker/ruscxn-embedder)
+        - Classification: [ruscxn-classifier](https://huggingface.co/Futyn-Maker/ruscxn-classifier)
+        - Span prediction: [ruscxn-span-predictor](https://huggingface.co/Futyn-Maker/ruscxn-span-predictor)
+
+        📚 [Russian Constructicon Database](https://constructicon.ruscorpora.ru/) | 💻 [Source Code](https://github.com/Futyn-Maker/ruscxnpipe)
+        """)
+
+    return demo
+
+
+if __name__ == "__main__":
+    demo = create_demo()
+    demo.launch(
+        server_name="0.0.0.0",  # For Hugging Face Spaces
+        server_port=7860,       # Default port for Spaces
+        show_error=True
+    )

requirements.txt

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+git+https://github.com/Futyn-Maker/RusCxnPipe.git