Update app.py

app.py

@@ -1,31 +1,31 @@
 import gradio as gr
 from gliner import GLiNER
 
-# Load the PII model
+# Load model
 model = GLiNER.from_pretrained("DeepMount00/GLiNER_PII_ITA")
 
-# Define labels for entity extraction
+# Labels to extract
 labels = ["PERSON", "LOCATION", "ORGANIZATION", "EMAIL", "PHONE", "DATE", "ADDRESS", "TAX_ID"]
 
-# Define the prediction function
+# Inference function
 def predict(text):
     if not text or not isinstance(text, str) or len(text.strip()) < 5:
         return []
     try:
-        results = model.predict_entities(text, labels)
-        return results
+        return model.predict_entities(text, labels)
     except Exception as e:
         return [{"error": str(e)}]
 
-# Build Gradio Interface (no enable_queue here!)
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Textbox(label="Testo da analizzare", placeholder="Inserisci qui il testo..."),
-    outputs=gr.Json(label="output"),
-    title="GLiNER PII Extractor 🇮🇹",
-    description="Named Entity Recognition for PII in Italian legal texts using GLiNER.",
-    allow_flagging="never"
-)
+# Use Blocks style (recommended for latest gradio)
+with gr.Blocks() as demo:
+    gr.Markdown("# GLiNER PII Extractor 🇮🇹")
+    gr.Markdown("Named Entity Recognition for PII in Italian legal texts using GLiNER.")
+    
+    inp = gr.Textbox(label="Testo da analizzare", placeholder="Inserisci qui il testo...")
+    out = gr.Json(label="Output")
 
-# Launch Space with queuing enabled
-demo.launch(queue=True)
+    btn = gr.Button("Analizza")
+    btn.click(fn=predict, inputs=inp, outputs=out)
+
+# Launch properly
+demo.queue().launch()