Initial Gradio Space implementation for WimBERT Synth v0

.gitignore

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+venv/
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.Python
+*.so
+*.egg
+*.egg-info/
+dist/
+build/
+.DS_Store
+.vscode/
+.idea/
+*.log
+.env
+.venv
+gradio_cached_examples/
+flagged/

LICENSE

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+

README.md

@@ -1,14 +1,101 @@
 ---
-title: WimBERT Synth
-emoji: 🌖
-colorFrom: red
-colorTo: pink
+title: WimBERT Synth v0
+emoji: 🏛️
+colorFrom: blue
+colorTo: indigo
 sdk: gradio
 sdk_version: 5.49.1
 app_file: app.py
 pinned: false
 license: apache-2.0
-short_description: Demonstratie van signaalberichtlabeling door WimBERT synt
+short_description: Dutch multi-label classifier for signal messages
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# WimBERT Synth v0: Dutch Multi-Label Signal Classifier
+
+Demo of a dual-head BERT classifier trained on synthetic Dutch government signals.
+Predicts relevant topics (**onderwerp**, 64 labels) and sentiment/experience 
+(**beleving**, 33 labels) for each input message.
+
+## 🚀 Usage
+
+1. Enter Dutch text (e.g., a citizen feedback message about government services)
+2. Click **Voorspel** to classify
+3. Adjust **Drempel** (threshold) to change prediction sensitivity
+4. View results in three tabs:
+   - **Samenvatting**: Top-K predictions per head with color-coded probabilities
+   - **Alle labels**: Complete list of all labels sorted by probability
+   - **JSON**: Raw predictions in machine-readable format
+
+## 🎯 Features
+
+- **Dual-head classification**: Simultaneously predicts topic (onderwerp) and experience (beleving)
+- **Interactive threshold**: Adjust which labels are considered "predicted"
+- **Color-coded visualization**: Probability intensity shown via color (darker = higher probability)
+- **Accessible**: All probabilities shown numerically, colors are enhancements
+- **Fast**: Optimized for CPU inference (~2-5s) with optional GPU acceleration
+
+## 🤖 Model
+
+- **Base model**: `bert-base-multilingual-cased`
+- **Architecture**: Dual classification heads with 64 onderwerp + 33 beleving labels
+- **Training**: Synthetic data via Argilla + distillation pipeline
+- **License**: Apache-2.0
+- **Full model card**: [UWV/wimbert-synth-v0](https://huggingface.co/UWV/wimbert-synth-v0)
+
+### Labels
+
+**Onderwerp (64 topics)**:
+Advies, Algemene veiligheid, Begeleiding, Bijstand, Bouwoverlast, COVID-19, Criminaliteit, 
+Documentaanvraag, Energiekosten, Evenementen, Financiële regelingen, Geluidsoverlast, 
+Gemeentelijke heffingen, Hangjongeren, Huisdierenoverlast, Hulp aan dak- en thuislozen, 
+Infrastructuur, Kwijtschelding, Migratie, Onderhoud omgeving, Parkeren, Schade en claims, 
+Verkeersmaatregelen, Verkeersveiligheid, Wijkteam, and more...
+
+**Beleving (33 experiences)**:
+Afspraakmogelijkheden, Algemene ervaring, Behulpzaamheid, Bereikbaarheid, Bezwaar & bewijs, 
+Communicatie, Deskundigheid, Duidelijkheid, Efficiëntie, Faciliteiten, Gebruiksgemak, 
+Informatievoorziening, Integriteit, Kwaliteit klantenservice, Snelheid van afhandeling, 
+Vriendelijkheid, Wachttijd, and more...
+
+## 🔒 Privacy
+
+- Input text is processed **in-memory only**
+- No data is logged or stored beyond standard Gradio telemetry
+- Model runs entirely within this Space (no external API calls)
+
+## ⚙️ Hardware
+
+- **CPU**: Works on free tier (~3-5s inference)
+- **GPU (T4)**: Recommended for production (<1s inference)
+
+Current Space is running on: **CPU** with FP32
+
+## 🛠️ Local Development
+
+```bash
+# Clone and setup
+git clone https://huggingface.co/spaces/UWV/wimbert-synth-v0
+cd wimbert-synth-v0
+python3 -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+
+# Run
+python app.py
+```
+
+## 📊 Example Use Cases
+
+- **Citizen feedback routing**: Automatically categorize incoming messages
+- **Sentiment analysis**: Understand citizen experience with government services
+- **Analytics**: Aggregate trends across topics and experiences
+- **Triage**: Prioritize urgent or negative feedback
+
+⚠️ **Note**: This is a research/demo tool. Not intended for automated decision-making.
+
+---
+
+**Built with**: Gradio • Transformers • PyTorch  
+**Developed by**: UWV  
+**License**: Apache-2.0

app.py

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+#!/usr/bin/env python3
+"""
+WimBERT Synth v0 Gradio Space
+Dual-head multi-label classifier for Dutch signal messages
+"""
+
+import json
+import importlib.util
+import torch
+import gradio as gr
+from huggingface_hub import snapshot_download
+
+# Constants
+MODEL_REPO = "UWV/wimbert-synth-v0"
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+DTYPE = torch.float16 if DEVICE.type == "cuda" else torch.float32
+MAX_LENGTH = 512  # Default to 512 for better CPU performance
+
+print(f"🔧 Loading model from {MODEL_REPO}...")
+print(f"🖥️  Device: {DEVICE} ({DTYPE})")
+
+# Download model files (uses HF cache)
+model_dir = snapshot_download(MODEL_REPO, cache_dir=None)
+
+# Dynamic import of model.py from downloaded dir
+spec = importlib.util.spec_from_file_location("model", f"{model_dir}/model.py")
+model_module = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(model_module)
+DualHeadModel = model_module.DualHeadModel
+
+# Load model + tokenizer + config
+model, tokenizer, config = DualHeadModel.from_pretrained(model_dir, device=DEVICE)
+
+# Cast to target dtype
+if DTYPE == torch.float16:
+    model = model.half()
+
+# Warm-up inference
+with torch.no_grad():
+    dummy_input = tokenizer("Warm-up", return_tensors="pt", padding="max_length", 
+                           max_length=MAX_LENGTH, truncation=True)
+    _ = model.predict(
+        dummy_input["input_ids"].to(DEVICE), 
+        dummy_input["attention_mask"].to(DEVICE)
+    )
+
+print(f"✅ Model loaded and warmed up")
+
+# Extract label names
+LABELS_ONDERWERP = config["labels"]["onderwerp"]
+LABELS_BELEVING = config["labels"]["beleving"]
+
+
+def prob_to_color(prob: float, threshold: float) -> str:
+    """Generate CSS style for probability visualization"""
+    lightness = 95 - int(prob * 65)
+    border = "2px solid #1e3a8a" if prob >= threshold else "1px solid #e5e7eb"
+    return f"background: hsl(210, 80%, {lightness}%); border: {border}; padding: 6px 12px; border-radius: 4px; margin: 2px 0;"
+
+
+def format_topk(labels: list, probs: list, threshold: float, topk: int) -> str:
+    """Generate HTML for top-K labels"""
+    sorted_indices = sorted(range(len(probs)), key=lambda i: probs[i], reverse=True)
+    html = "<div style='display: flex; flex-direction: column; gap: 6px;'>"
+    for idx in sorted_indices[:topk]:
+        label = labels[idx]
+        prob = probs[idx]
+        style = prob_to_color(prob, threshold)
+        predicted = " ✓" if prob >= threshold else ""
+        html += f"<div style='{style}'><b>{label}</b>: {prob:.3f}{predicted}</div>"
+    html += "</div>"
+    return html
+
+
+def format_all_labels(head_name: str, labels: list, probs: list, threshold: float) -> str:
+    """Generate scrollable table for all labels"""
+    sorted_indices = sorted(range(len(probs)), key=lambda i: probs[i], reverse=True)
+    html = f"<h3>{head_name}</h3><div style='max-height: 500px; overflow-y: auto; border: 1px solid #e5e7eb; border-radius: 4px;'>"
+    html += "<table style='width: 100%; border-collapse: collapse;'>"
+    html += "<thead style='position: sticky; top: 0; background: white; border-bottom: 2px solid #e5e7eb;'>"
+    html += "<tr><th style='text-align: left; padding: 8px;'>Label</th><th style='text-align: right; padding: 8px;'>Probability</th><th style='padding: 8px;'>Predicted</th></tr>"
+    html += "</thead><tbody>"
+    for idx in sorted_indices:
+        label = labels[idx]
+        prob = probs[idx]
+        style = prob_to_color(prob, threshold)
+        predicted = "✓" if prob >= threshold else ""
+        html += f"<tr><td style='{style}'><b>{label}</b></td><td style='text-align: right; padding: 8px;'>{prob:.4f}</td><td style='text-align: center; padding: 8px;'>{predicted}</td></tr>"
+    html += "</tbody></table></div>"
+    return html
+
+
+@torch.inference_mode()
+def predict(text: str, threshold: float, topk: int):
+    """Run inference and return visualizations"""
+    if not text or not text.strip():
+        empty_msg = "<p style='color: #666; font-style: italic;'>Voer een bericht in om te classificeren...</p>"
+        return empty_msg, empty_msg, {}
+    
+    # Tokenize
+    inputs = tokenizer(
+        text, 
+        return_tensors="pt", 
+        padding="max_length", 
+        max_length=MAX_LENGTH,
+        truncation=True
+    )
+    
+    # Move to device
+    input_ids = inputs["input_ids"].to(DEVICE)
+    attention_mask = inputs["attention_mask"].to(DEVICE)
+    
+    # Predict
+    onderwerp_probs, beleving_probs = model.predict(input_ids, attention_mask)
+    
+    # Convert to lists
+    onderwerp_probs = onderwerp_probs[0].cpu().numpy().tolist()
+    beleving_probs = beleving_probs[0].cpu().numpy().tolist()
+    
+    # Generate summary view (top-K for each head side by side)
+    summary_html = "<div style='display: grid; grid-template-columns: 1fr 1fr; gap: 20px;'>"
+    summary_html += f"<div><h3>Onderwerp (Top-{topk})</h3>{format_topk(LABELS_ONDERWERP, onderwerp_probs, threshold, topk)}</div>"
+    summary_html += f"<div><h3>Beleving (Top-{topk})</h3>{format_topk(LABELS_BELEVING, beleving_probs, threshold, topk)}</div>"
+    summary_html += "</div>"
+    
+    # Generate all labels view
+    all_labels_html = "<div style='display: grid; grid-template-columns: 1fr 1fr; gap: 20px;'>"
+    all_labels_html += f"<div>{format_all_labels('Onderwerp', LABELS_ONDERWERP, onderwerp_probs, threshold)}</div>"
+    all_labels_html += f"<div>{format_all_labels('Beleving', LABELS_BELEVING, beleving_probs, threshold)}</div>"
+    all_labels_html += "</div>"
+    
+    # Generate JSON output
+    json_output = {
+        "text": text,
+        "threshold": threshold,
+        "onderwerp": {
+            "probabilities": {label: float(prob) for label, prob in zip(LABELS_ONDERWERP, onderwerp_probs)},
+            "predicted": [label for label, prob in zip(LABELS_ONDERWERP, onderwerp_probs) if prob >= threshold]
+        },
+        "beleving": {
+            "probabilities": {label: float(prob) for label, prob in zip(LABELS_BELEVING, beleving_probs)},
+            "predicted": [label for label, prob in zip(LABELS_BELEVING, beleving_probs) if prob >= threshold]
+        }
+    }
+    
+    return summary_html, all_labels_html, json_output
+
+
+def load_examples():
+    """Load example texts"""
+    try:
+        with open("examples.json") as f:
+            return json.load(f)
+    except:
+        return []
+
+
+# Build Gradio interface
+with gr.Blocks(title="WimBERT Synth v0", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🏛️ WimBERT Synth v0: Multi-label Signaal Classifier
+    
+    Classificeert Nederlandse signaalberichten op **Onderwerp** (64 categorieën) en **Beleving** (33 categorieën).
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            input_text = gr.Textbox(
+                label="Signaalbericht (Nederlands)",
+                lines=8,
+                placeholder="Bijv: Ik kan niet parkeren bij mijn huis en de website voor vergunningen werkt niet...",
+                info="Voer een bericht in en klik op 'Voorspel'"
+            )
+            with gr.Row():
+                predict_btn = gr.Button("🔮 Voorspel", variant="primary", scale=2)
+                clear_btn = gr.ClearButton([input_text], value="🗑️ Wissen", scale=1)
+        
+        with gr.Column(scale=1):
+            threshold_slider = gr.Slider(
+                minimum=0, 
+                maximum=1, 
+                value=0.5, 
+                step=0.05, 
+                label="🎯 Drempel",
+                info="Labels boven deze waarde worden als 'voorspeld' gemarkeerd"
+            )
+            topk_slider = gr.Slider(
+                minimum=1, 
+                maximum=15, 
+                value=5, 
+                step=1, 
+                label="📊 Top-K",
+                info="Aantal top labels om te tonen in samenvatting"
+            )
+            gr.Markdown(f"""
+            **Hardware:** {DEVICE.type.upper()}  
+            **Dtype:** {DTYPE}  
+            **Max length:** {MAX_LENGTH}
+            """)
+    
+    with gr.Tabs():
+        with gr.Tab("📋 Samenvatting"):
+            summary_output = gr.HTML(label="Top voorspellingen per categorie")
+        
+        with gr.Tab("📊 Alle labels"):
+            all_labels_output = gr.HTML(label="Volledige classificatie")
+        
+        with gr.Tab("💾 JSON"):
+            json_output = gr.JSON(label="Ruwe output")
+    
+    gr.Examples(
+        examples=load_examples(),
+        inputs=input_text,
+        label="📝 Voorbeelden"
+    )
+    
+    gr.Markdown("""
+    ---
+    ### ℹ️ Over dit model
+    - **Model:** `UWV/wimbert-synth-v0` (dual-head BERT)
+    - **Licentie:** Apache-2.0
+    - **Privacy:** Input wordt alleen in-memory verwerkt, niet opgeslagen
+    
+    [Model Card](https://huggingface.co/UWV/wimbert-synth-v0) • Gebouwd met Gradio
+    """)
+    
+    # Event handlers
+    predict_btn.click(
+        fn=predict,
+        inputs=[input_text, threshold_slider, topk_slider],
+        outputs=[summary_output, all_labels_output, json_output]
+    )
+    
+    # Update predictions when threshold/topk changes (if there's existing output)
+    threshold_slider.change(
+        fn=predict,
+        inputs=[input_text, threshold_slider, topk_slider],
+        outputs=[summary_output, all_labels_output, json_output]
+    )
+    
+    topk_slider.change(
+        fn=predict,
+        inputs=[input_text, threshold_slider, topk_slider],
+        outputs=[summary_output, all_labels_output, json_output]
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()

examples.json

@@ -0,0 +1,7 @@
+[
+  ["Ik heb geprobeerd te bellen maar krijg niemand aan de lijn. Kan iemand mij helpen met mijn vraag over mijn uitkering?"],
+  ["De vuilcontainers zijn weer kapot en het stinkt hier verschrikkelijk. Wanneer wordt dit opgelost?"],
+  ["Ik wil graag een parkeervergunning aanvragen maar de website werkt niet goed. Kan dit eenvoudiger?"],
+  ["Bedankt voor de snelle en vriendelijke hulp bij mijn aanvraag! De medewerker was zeer behulpzaam."],
+  ["Ik ben erg ontevreden over de lange wachttijden en onduidelijke informatie die ik kreeg."]
+]

requirements.txt

@@ -0,0 +1,5 @@
+gradio>=4.0
+transformers>=4.40
+torch>=2.0
+safetensors>=0.4
+huggingface-hub>=0.20