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app.py

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+import gradio as gr
+from transformers import pipeline
+
+models = {
+    "MoritzLaurer/deberta-v3-large-zeroshot-v2.0 (best, English)": "MoritzLaurer/deberta-v3-large-zeroshot-v2.0",
+    "MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 (multilingual incl. Dutch)": "MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7",
+    "facebook/bart-large-mnli (classic)": "facebook/bart-large-mnli",
+}
+
+pipes = {}
+
+
+def get_pipe(model_name):
+    if model_name not in pipes:
+        pipes[model_name] = pipeline(
+            "zero-shot-classification",
+            model=models[model_name],
+        )
+    return pipes[model_name]
+
+
+PRESETS = {
+    "Custom (type your own)": "",
+    "News categories": "politics, economy, sports, culture, technology, health, crime, environment",
+    "Sentiment": "positive, negative, neutral",
+    "Urgency": "urgent, important, routine, not relevant",
+    "Story type": "breaking news, investigation, feature, opinion, analysis",
+    "Tips inbox triage": "actionable tip, complaint, spam, press release, personal story",
+}
+
+
+def classify(text, model_choice, labels_text, preset, multi_label):
+    if not text.strip():
+        return "Enter some text to classify."
+
+    if preset != "Custom (type your own)" and not labels_text.strip():
+        labels_text = PRESETS[preset]
+
+    if not labels_text.strip():
+        return "Enter at least two labels separated by commas."
+
+    labels = [l.strip() for l in labels_text.split(",") if l.strip()]
+    if len(labels) < 2:
+        return "Need at least two labels."
+
+    pipe = get_pipe(model_choice)
+    result = pipe(text, candidate_labels=labels, multi_label=multi_label)
+
+    output = ""
+    for label, score in zip(result["labels"], result["scores"]):
+        bar = "█" * int(score * 30)
+        output += f"{label:.<30s} {score:.1%} {bar}\n"
+
+    return output
+
+
+def update_labels(preset):
+    if preset == "Custom (type your own)":
+        return ""
+    return PRESETS.get(preset, "")
+
+
+with gr.Blocks(title="Zero-Shot Classification — KRO-NCRV Workshop") as demo:
+    gr.Markdown("# Zero-Shot Classification")
+    gr.Markdown(
+        "Classify text into **any categories you define** — no training needed. "
+        "Works in Dutch and English."
+    )
+
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(
+                label="Text to classify",
+                lines=6,
+                placeholder="Paste an article, tip, tweet, or paragraph...",
+            )
+            model_choice = gr.Dropdown(
+                choices=list(models.keys()),
+                value=list(models.keys())[1],
+                label="Model",
+            )
+            preset = gr.Dropdown(
+                choices=list(PRESETS.keys()),
+                value="Custom (type your own)",
+                label="Label preset",
+            )
+            labels_input = gr.Textbox(
+                label="Labels (comma-separated)",
+                placeholder="politics, economy, sports, culture",
+            )
+            multi_label = gr.Checkbox(
+                label="Multi-label (text can belong to multiple categories)",
+                value=False,
+            )
+            btn = gr.Button("Classify", variant="primary")
+
+        with gr.Column():
+            output = gr.Textbox(label="Results", lines=15, show_copy_button=True)
+
+    preset.change(fn=update_labels, inputs=[preset], outputs=[labels_input])
+    btn.click(
+        fn=classify,
+        inputs=[text_input, model_choice, labels_input, preset, multi_label],
+        outputs=[output],
+    )
+
+demo.launch()

requirements.txt

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+transformers
+torch
+gradio