Create app.py

app.py

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+import gradio as gr
+import spacy
+from spacy import displacy
+
+# Load transformer-based spaCy NER pipeline
+nlp = spacy.load("en_core_web_trf")  # Transformer-based model
+
+def ner_extraction(text):
+    if not text.strip():
+        return "Please enter some text."
+    doc = nlp(text)
+    ents = [{"text": ent.text, "label": ent.label_} for ent in doc.ents]
+    if not ents:
+        return "No named entities found."
+    return ents
+
+# Optional: visual output
+def ner_visualizer(text):
+    doc = nlp(text)
+    html = displacy.render(doc, style="ent", minify=True)
+    return html
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Named Entity Recognition using spaCy + Transformers (en_core_web_trf)")
+    with gr.Tab("Extract Entities"):
+        inp = gr.Textbox(label="Enter Text", lines=3, placeholder="Type a sentence...")
+        out = gr.JSON(label="Named Entities")
+        btn = gr.Button("Run NER")
+        btn.click(ner_extraction, inputs=inp, outputs=out)
+
+    with gr.Tab("Visualize Entities"):
+        vis_inp = gr.Textbox(label="Enter Text", lines=3, placeholder="Type a sentence...")
+        vis_out = gr.HTML(label="Visualization")
+        vis_btn = gr.Button("Visualize")
+        vis_btn.click(ner_visualizer, inputs=vis_inp, outputs=vis_out)
+
+if __name__ == "__main__":
+    demo.launch()