Update app.py

app.py

@@ -11,52 +11,48 @@ model = AutoModelForTokenClassification.from_pretrained(MODEL_ID)
 id2label = {int(k): v for k, v in model.config.id2label.items()}
 
 
-def ner_predict(text):
+def generate_ner_output(text):
     if not text.strip():
-        return "Please enter some text.", []
+        return "Please enter text."
 
-    # tokenize text
     inputs = tokenizer(text, return_tensors="pt")
     tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
 
-    # model forward
     with torch.no_grad():
         logits = model(**inputs).logits
 
     pred_ids = torch.argmax(logits, dim=-1)[0].tolist()
 
-    rows = []
-    pretty_text = ""
-
+    # Build formatted text
+    output_lines = []
     for tok, pid in zip(tokens, pred_ids):
         label = id2label[pid]
-        rows.append([tok, label])
-        pretty_text += f"{tok:15} → {label}\n"
-
-    return pretty_text, rows
-
+        output_lines.append(f"{tok:<15} → {label}")
 
-def build_ui():
-    with gr.Blocks(title="Indic NER Token Viewer") as demo:
-        gr.Markdown("## 🔥 Hindi + English Token-level NER (Fine-tuned Model)")
+    return "\n".join(output_lines)
 
-        inp = gr.Textbox(lines=3, label="Enter text to analyze")
 
-        btn = gr.Button("Run NER")
+# ----------- GRADIO UI ---------------
+with gr.Blocks() as demo:
+    gr.Markdown("## 🔥 IndicNER — Token → Label Output")
 
-        output_text = gr.Textbox(label="Formatted Output", lines=20)
-        output_table = gr.Dataframe(
-            headers=["Token", "NER Label"],
-            datatype=["str", "str"],
-            label="Detailed Table"
-        )
+    text_input = gr.Textbox(
+        label="Enter text",
+        placeholder="Type your Hindi/English sentence here...",
+        lines=4
+    )
 
-        btn.click(fn=ner_predict, inputs=inp, outputs=[output_text, output_table])
+    run_button = gr.Button("Generate NER")
 
-    return demo
+    ner_output = gr.Textbox(
+        label="NER Output (Token → Label Format)",
+        lines=30
+    )
 
+    run_button.click(
+        fn=generate_ner_output,
+        inputs=text_input,
+        outputs=ner_output
+    )
 
-# 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)
+demo.launch()