initial version

app.py

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+import json
+import os
+import random
+
+import gradio as gr
+import spacy
+from huggingface_hub import snapshot_download
+from spacy import displacy
+from spacy.tokens import Span
+
+# download spacy model ---
+os.system('python -m spacy download en_core_web_sm')
+
+# # set up colors for PII types ---
+options = {
+    "colors": {
+        "NAME_STUDENT": "#7FDBFF",  # Soft blue
+        "EMAIL": "#008080",         # Dark cyan
+        "USERNAME": "#C3B1E1",      # Pastel violet
+        "ID_NUM": "#2ECC40",        # Medium green
+        "PHONE_NUM": "#FF851B",     # Deep orange
+        "URL_PERSONAL": "#4682B4",  # Steel blue
+        "STREET_ADDRESS": "#808000",  # Muted olive
+    }
+}
+
+
+# download datamix ---
+
+
+def download_data():
+
+    snapshot_download(
+        repo_id="rbiswasfc/pii_datamix",
+        repo_type="dataset",
+        local_dir="./data",
+    )
+    print("Data downloaded!")
+
+
+download_data()
+
+# load data ---
+with open("./data/datamix.json") as f:
+    data = json.load(f)
+
+subsets = list(data.keys())
+pii_types = list(options["colors"].keys())
+pii_types.append("Random")
+
+nlp = spacy.load("en_core_web_sm")
+# render sample ---
+
+
+def render_sample(subset, pii_type):
+    candidates = data[subset]
+    while True:
+        sample = random.choice(candidates)
+        if pii_type == "Random":
+            break
+        elif pii_type in sample['piis']:
+            break
+
+    # render
+    doc = spacy.tokens.Doc(nlp.vocab, words=sample['tokens'], spaces=sample['trailing_whitespace'])
+
+    #
+    ents = []
+    in_entity = False
+    start, end = 0, 0
+
+    for index, label in enumerate(sample['labels']):
+        if label.startswith('B-'):
+            if in_entity:  # End the previous entity
+                ents.append(Span(doc, start, end, label[2:]))
+            start, end = index, index + 1  # Start a new entity
+            in_entity = True
+        elif label.startswith('I-') and in_entity:
+            end = index + 1  # Continue the entity
+        elif in_entity:
+            # End the current entity and reset
+            ents.append(Span(doc, start, end, sample['labels'][start][2:]))
+            in_entity = False
+
+    doc.ents = ents
+    output = displacy.render(doc, style="ent", jupyter=False, options=options)
+    return output
+
+
+# app layout & callback ---
+with gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.red, secondary_hue=gr.themes.colors.pink)) as demo:
+    with gr.Row():
+        subset_dropdown = gr.Dropdown(
+            subsets,
+            value=subsets[0],
+            label="Subset",
+            info="Select data subset..."
+        )
+
+        focus_pii = gr.Dropdown(
+            pii_types,
+            value="Random",
+            label="PII Focus",
+            info="Select a PII type to focus on..."
+        )
+
+    sample_btn = gr.Button("Sample")
+
+    sample_display = gr.HTML(label="Example")
+
+    # callback ---
+    sample_btn.click(
+        fn=render_sample,
+        inputs=[subset_dropdown, focus_pii],
+        outputs=sample_display,
+    )
+
+# launch app ---
+demo.launch()

requirements.txt

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+spacy