Create app.py

app.py

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+import gradio as gr
+
+from transformers import pipeline
+from typing import Dict, Union
+from gliner import GLiNER
+
+model = GLiNER.from_pretrained("numind/NuNER_Zero")
+
+classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-base-zeroshot-v1")
+
+css = """
+h1 {
+    text-align: center;
+    display:block;
+}
+"""
+
+#define a function to process your input and output
+def zero_shot(doc, candidates):
+    given_labels = candidates.split(", ")
+    dictionary = classifier(doc, given_labels)
+    labels = dictionary['labels']
+    scores = dictionary['scores']
+    return dict(zip(labels, scores))
+
+examples_text = [
+    [
+        "Pasar saham ngalaman panurunan nu signifikan akibat kateupastian global.",
+        "ékonomi, pulitik, bisnis, kauangan, téknologi"
+    ],
+    [
+        "I am very happy today but suddenly sad because of the recent news.",
+        "positive, negative, neutral"
+    ],
+    [
+        "I just received the best news ever! I got the job I always wanted!",
+        "joy, sadness, anger, surprise, fear, disgust"
+    ],
+]
+examples_ner = [
+    [
+        "Pada tahun 1945, Indonesia memproklamasikan kemerdekaannya dari penjajahan Belanda. Proklamasi tersebut dibacakan oleh Soekarno dan Mohammad Hatta di Jakarta.",
+        "tahun, negara, tokoh, lokasi",
+        0.3
+    ],
+    [
+        "Mount Everest is the highest mountain above sea level, located in the Himalayas. It stands at 8,848 meters (29,029 ft) and attracts many climbers.",
+        "location, measurement, person",
+        0.3
+    ],
+     [
+        "Perusahaan teknologi raksasa, Google, mbukak kantor cabang anyar ing Jakarta ing wulan Januari 2020 kanggo nggedhekake operasine ing Asia Tenggara",
+        "perusahaan, lokasi, wulan, taun",
+        0.3
+    ],
+]
+
+def merge_entities(entities):
+    if not entities:
+        return []
+    merged = []
+    current = entities[0]
+    for next_entity in entities[1:]:
+        if next_entity['entity'] == current['entity'] and (next_entity['start'] == current['end'] + 1 or next_entity['start'] == current['end']):
+            current['word'] += ' ' + next_entity['word']
+            current['end'] = next_entity['end']
+        else:
+            merged.append(current)
+            current = next_entity
+    merged.append(current)
+    return merged
+
+def ner(
+    text, labels: str, threshold: float, nested_ner: bool
+) -> Dict[str, Union[str, int, float]]:
+    labels = labels.split(",")
+    r = {
+        "text": text,
+        "entities": [
+            {
+                "entity": entity["label"],
+                "word": entity["text"],
+                "start": entity["start"],
+                "end": entity["end"],
+                "score": 0,
+            }
+            for entity in model.predict_entities(
+                text, labels, flat_ner=not nested_ner, threshold=threshold
+            )
+        ],
+    }
+    r["entities"] =  merge_entities(r["entities"])
+    return r
+
+
+with gr.Blocks(title="Zero-Shot Demo", css=css) as demo: #, theme=gr.themes.Soft()
+
+    gr.Markdown(
+            """
+            # Zero-Shot Model Demo
+            """
+        )
+
+    #create input and output objects
+    with gr.Tab("Zero-Shot Text Classification"):
+
+        gr.Markdown(
+            """
+            ## Zero-Shot Text Classification
+            """
+        )
+
+        input1 = gr.Textbox(label="Text", value=examples_text[0][0])
+        input2 = gr.Textbox(label="Labels",value=examples_text[0][1])
+        output = gr.Label(label="Output")
+
+        gui = gr.Interface(
+            # title="Zero-Shot Text Classification",
+            fn=zero_shot,
+            inputs=[input1, input2],
+            outputs=[output]
+        )
+
+        examples = gr.Examples(
+            examples_text,
+            fn=zero_shot,
+            inputs=[input1, input2],
+            outputs=output,
+            cache_examples=True,
+        )
+
+
+    with gr.Tab("Zero-Shot NER"):
+        gr.Markdown(
+            """
+            ## Zero-Shot Named Entity Recognition (NER)
+            """
+        )
+
+        input_text = gr.Textbox(
+            value=examples_ner[0][0], label="Text input", placeholder="Enter your text here", lines=3
+        )
+        with gr.Row() as row:
+            labels = gr.Textbox(
+                value=examples_ner[0][1],
+                label="Labels",
+                placeholder="Enter your labels here (comma separated)",
+                scale=2,
+            )
+            threshold = gr.Slider(
+                0,
+                1,
+                value=examples_ner[0][2],
+                step=0.01,
+                label="Threshold",
+                info="Lower the threshold to increase how many entities get predicted.",
+                scale=1,
+            )
+        
+        output = gr.HighlightedText(label="Predicted Entities")
+
+        submit_btn = gr.Button("Submit")
+
+        examples = gr.Examples(
+            examples_ner,
+            fn=ner,
+            inputs=[input_text, labels, threshold],
+            outputs=output,
+            cache_examples=True,
+        )
+
+        submit_btn.click(
+            fn=ner, inputs=[input_text, labels, threshold], outputs=output
+        )
+
+demo.queue()
+demo.launch(debug=True)