Commit
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e1c577b
1
Parent(s):
5436b2b
update app
Browse files
app.py
CHANGED
@@ -16,23 +16,25 @@ ner_pipeline = pipeline(
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# Function to process the sentence and extract entities
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def extract_entities(sentence):
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results = ner_pipeline(sentence)
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# Extract and format the entities
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for
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(
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entity["word"],
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entity["start"],
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entity["end"],
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f"{entity['entity']} ({entity['score']:.2f}%)",
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)
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)
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return
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# Create Gradio interface
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@@ -40,7 +42,7 @@ def ner_app_interface():
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input_sentence = gr.Textbox(
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lines=5, label="Input Sentence", placeholder="Enter a sentence for NER..."
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)
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output_entities = gr.
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# Interface definition
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interface = gr.Interface(
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@@ -49,11 +51,6 @@ def ner_app_interface():
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outputs=output_entities,
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title="Named Entity Recognition",
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description="Enter a sentence to extract named entities using the NER model from the Impresso project.",
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examples=[
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[
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"In the year 1789, King Louis XVI, ruler of France, convened the Estates-General at the Palace of Versailles."
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]
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],
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interface.launch()
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)
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# Helper function to print entities nicely
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def print_nicely(entities):
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entity_details = []
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for entity in entities:
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entity_info = f"Entity: {entity['entity']} | Confidence: {entity['score']:.2f}% | Text: {entity['word'].strip()} | Start: {entity['start']} | End: {entity['end']}"
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entity_details.append(entity_info)
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return "\n".join(entity_details)
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# Function to process the sentence and extract entities
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def extract_entities(sentence):
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results = ner_pipeline(sentence)
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entity_results = []
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# Extract and format the entities
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for key in results.keys():
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entity_results.append(print_nicely(results[key]))
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return "\n".join(entity_results)
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# Create Gradio interface
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input_sentence = gr.Textbox(
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lines=5, label="Input Sentence", placeholder="Enter a sentence for NER..."
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)
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output_entities = gr.Textbox(label="Extracted Entities")
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# Interface definition
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interface = gr.Interface(
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outputs=output_entities,
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title="Named Entity Recognition",
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description="Enter a sentence to extract named entities using the NER model from the Impresso project.",
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)
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interface.launch()
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