Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load NER model. Device -1 means CPU.
+# If you have a GPU, you can set device=0 or the appropriate GPU index.
+ner_model = pipeline("token-classification", model="loolootech/no-name-ner-th", device=-1)
+
+ENTITY_TO_ANONYMIZED_TOKEN_MAP = {
+    "PERSON": "[PERSON]",
+    "PHONE": "[PHONE]",
+    "EMAIL": "[EMAIL]",
+    "ADDRESS": "[LOCATION]",
+    "DATE": "[DATE]",
+    "NATIONAL_ID": "[NATIONAL_ID]",
+    "HOSPITAL_IDS": "[HOSPITAL_IDS]",
+}
+
+def anonymize_text(original_text):
+    """
+    Anonymizes sensitive entities in the input text using a named entity recognition (NER) model.
+
+    Parameters
+    ----------
+    original_text : str
+        The text that may contain sensitive information such as names, phone numbers, emails, etc.
+
+    Returns
+    -------
+    list
+        A list containing:
+        - original_text: the original input string
+        - anonymized_text: the text with specified entities replaced by tokens
+        - anonymized_entities: a list of dictionaries for each anonymized entity, containing
+        the original word and its entity label
+
+    Notes
+    -----
+    - Requires `ner_model` to be defined and initialized (e.g., a HuggingFace NER pipeline).
+    - The mapping `ENTITY_TO_ANONYMIZED_TOKEN_MAP` defines which entity types will be replaced
+    and what token will be used.
+    """
+
+    # Step 1: Perform NER on the input text
+    ner_results = ner_model(original_text)
+
+    # Step 2: Combine overlapping or adjacent entities of the same type
+    combined_entities = []
+    for entity in ner_results:
+        # Normalize entity label (e.g., "B-PERSON" -> "PERSON")
+        entity_name = entity['entity'].split('-')[-1]
+        entity['entity'] = entity_name
+
+        # Add as new entity if list is empty, different type, or non-overlapping
+        if not combined_entities or combined_entities[-1]['entity'] != entity_name or \
+            combined_entities[-1]['start'] + len(combined_entities[-1]['word']) < entity['start']:
+            combined_entities.append(entity)
+        else:
+            # Merge adjacent/overlapping entities of the same type
+            combined_entities[-1]['word'] += ' ' + entity['word']
+            combined_entities[-1]['end'] = entity['end']
+
+    # Step 3: Filter entities that should be anonymized
+    entities_to_anonymize = [
+        e for e in combined_entities if e['entity'] in ENTITY_TO_ANONYMIZED_TOKEN_MAP.keys()
+    ]
+
+    # Step 4: Sort entities in reverse order of start index to safely replace them
+    entities_to_anonymize.sort(key=lambda x: x['start'], reverse=True)
+
+    # Step 5: Replace each entity in the text with the corresponding anonymized token
+    anonymized_text = original_text
+    for entity in entities_to_anonymize:
+        start, end = entity['start'], entity['end']
+        token = ENTITY_TO_ANONYMIZED_TOKEN_MAP.get(entity['entity'])
+        anonymized_text = anonymized_text[:start] + token + anonymized_text[end:]
+
+    # Step 6: Return original text, anonymized text, and information about redacted entities
+    return [
+        original_text,
+        anonymized_text,
+        [{"word": e["word"], "label": e["entity"]} for e in entities_to_anonymize]
+    ]
+
+
+with gr.Blocks(title="Thai Clinical Conversation De-identification") as demo:
+    gr.HTML(
+        """
+        <div style="text-align: center;">
+            <h1 style="font-size: 3em;">Thai Clinical Conversation De-identification</h1>
+            <p style="font-size: 1.2em;">Paste Thai clinical or personal text below to redact sensitive info.</p>
+        </div>
+        """
+    )
+# Use a gr.Row with gr.Column spacers to center the image
+    with gr.Row():
+        gr.Column(scale=1) # Left spacer
+        gr.Image(
+            value="assets/mascot-image-landscape.png", # Replace with your image URL/path
+            width=200, # Set a smaller width for the image
+            show_label=False,
+            container=False # Prevent the image from being wrapped in a default Gradio container
+        )
+        gr.Column(scale=1) # Right spacer
+
+    # Add the main interface components
+    gr.Interface(
+        fn=anonymize_text,
+        inputs=gr.Textbox(lines=10, label="Input Text"),
+        outputs=[
+            gr.Textbox(label="Original Text"),
+            gr.Textbox(label="Anonymized Text"),
+            gr.JSON(label="Entities")
+        ],
+        live=False,  # Set live=False since we are using Blocks now
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=False)