Update app.py

app.py

@@ -1,26 +1,26 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
-
-# Load the model
-model_name = "tahirmuhammadcs/multi-ner-final"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForTokenClassification.from_pretrained(model_name)
-
-ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
-
-# Define prediction function
-def predict(text):
-    result = ner(text)
-    return result
-
-# Interface (you can hide this by setting `live=False`)
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Textbox(lines=4, placeholder="Enter text here..."),
-    outputs="json",
-    title="Multi-language NER",
-    description="Named Entity Recognition using Hugging Face Transformers."
-)
-
-# Launch the app
-demo.launch()
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
+
+# Load the model
+model_name = "tahirmuhammadcs/multi-ner-final"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+
+ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
+
+# Define prediction function
+def predict(text):
+    result = ner(text)
+    return result
+
+# Interface (you can hide this by setting `live=False`)
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=4, placeholder="Enter text here..."),
+    outputs="json",
+    title="Multi-language NER",
+    description="Named Entity Recognition using Hugging Face Transformers."
+)
+
+# Launch the app
+demo.launch(share=True)