Update app.py

app.py

@@ -7,49 +7,56 @@ MODEL_ID = "techysanoj/fine-tuned-IndicNER"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
 model = AutoModelForTokenClassification.from_pretrained(MODEL_ID)
 
+# Convert id2label keys to int
 id2label = {int(k): v for k, v in model.config.id2label.items()}
 
 
 def ner_predict(text):
-    # tokenize input
+    if not text.strip():
+        return "Please enter some text.", []
+
+    # tokenize text
     inputs = tokenizer(text, return_tensors="pt")
     tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
 
-    # run model
+    # model forward
     with torch.no_grad():
         logits = model(**inputs).logits
 
     pred_ids = torch.argmax(logits, dim=-1)[0].tolist()
 
-    # build output table
     rows = []
+    pretty_text = ""
+
     for tok, pid in zip(tokens, pred_ids):
-        rows.append([tok, id2label[pid]])
-
-    # pretty text version
-    pretty_output = ""
-    for tok, lab in rows:
-        pretty_output += f"{tok:15} → {lab}\n"
-
-    return pretty_output, rows
-
-
-# gradio UI
-with gr.Blocks(title="Indic NER Token-wise Output") as demo:
-    gr.Markdown("🔥 Indian Language NER — Token Level Output (Hindi + English)")
-    
-    inp = gr.Textbox(lines=3, label="Enter text")
-    
-    btn = gr.Button("Run NER")
-    
-    out_text = gr.Textbox(label="Tokenized Output")
-    out_table = gr.Dataframe(
-        headers=["Token", "Label"], 
-        datatype=["str", "str"], 
-        label="Table View",
-        wrap=True
-    )
-
-    btn.click(fn=ner_predict, inputs=inp, outputs=[out_text, out_table])
-
-demo.launch()
+        label = id2label[pid]
+        rows.append([tok, label])
+        pretty_text += f"{tok:15} → {label}\n"
+
+    return pretty_text, rows
+
+
+def build_ui():
+    with gr.Blocks(title="Indic NER Token Viewer") as demo:
+        gr.Markdown("## 🔥 Hindi + English Token-level NER (Fine-tuned Model)")
+
+        inp = gr.Textbox(lines=3, label="Enter text to analyze")
+
+        btn = gr.Button("Run NER")
+
+        output_text = gr.Textbox(label="Formatted Output", lines=20)
+        output_table = gr.Dataframe(
+            headers=["Token", "NER Label"],
+            datatype=["str", "str"],
+            label="Detailed Table"
+        )
+
+        btn.click(fn=ner_predict, inputs=inp, outputs=[output_text, output_table])
+
+    return demo
+
+
+# Prevent Gradio from using asyncio event loop that causes file descriptor crash
+if __name__ == "__main__":
+    demo = build_ui()
+    demo.launch(server_name="0.0.0.0", server_port=7860)