Update app.py

app.py

@@ -1,6 +1,18 @@
 import gradio as gr
+from transformers import pipeline
 
-# Load model dari Hugging Face Inference API
+# Load model NER
+ner_pipeline = pipeline("ner", model="d4data/biomedical-ner-all")
+
+def extract_entities(text):
+    """Fungsi untuk mengekstrak entitas medis dari teks input."""
+    entities = ner_pipeline(text)
+    results = []
+    for ent in entities:
+        results.append(f"{ent['word']} ({ent['entity']}) - Score: {ent['score']:.2f}")
+    return "\n".join(results) if results else "Tidak ada entitas medis yang terdeteksi."
+
+# Load model chatbot dari Hugging Face Inference API
 model = gr.load("models/mistralai/Mistral-7B-Instruct-v0.3", provider="hf-inference")
 
 # Bungkus dengan Gradio Interface agar bisa dikustomisasi
@@ -20,4 +32,14 @@ with gr.Blocks(css="""
         with gr.Column():
             model.render()
 
+    gr.Markdown("## 🏥 Biomedical NER (Named Entity Recognition)")
+    gr.Markdown("🔍 Ekstrak entitas medis dari teks yang dimasukkan.")
+
+    with gr.Row():
+        input_text = gr.Textbox(label="Masukkan teks medis", placeholder="Ketik teks medis di sini...")
+        ner_output = gr.Textbox(label="Hasil NER", interactive=False)
+
+    extract_button = gr.Button("Ekstrak Entitas Medis")
+    extract_button.click(extract_entities, inputs=[input_text], outputs=[ner_output])
+
 demo.launch()