app.py

import gradio as gr
from transformers import pipeline

# Load BERT NER pipeline
ner_pipeline = pipeline("ner", model="dslim/bert-base-NER", aggregation_strategy="simple")

def extract_entities(text):
    entities = ner_pipeline(text)
    results = []
    for ent in entities:
        results.append({
            "Entity": ent["word"],
            "Label": ent["entity_group"],
            "Score": round(ent["score"] * 100, 2)
        })
    return results

iface = gr.Interface(
    fn=extract_entities,
    inputs=gr.Textbox(lines=10, placeholder="Paste your content here"),
    outputs=gr.Dataframe(headers=["Entity", "Label", "Score"]),
    title="Semantic Keyword + Entity Extractor",
    description="Extracts BERT-based Named Entities for semantic coverage and AI optimization."
)

iface.launch()