Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load Pretrained Model & Tokenizer (XLM-Roberta for multilingual text classification)
+MODEL_NAME = "xlm-roberta-base"
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels=5)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+# Classification Function
+def classify_text(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    label = torch.argmax(outputs.logits, dim=1).item()
+    return f"Predicted Category: {label}"
+
+# Gradio UI
+demo = gr.Interface(fn=classify_text, inputs=gr.Textbox(lines=2, placeholder="Enter business document text..."), 
+                    outputs="text", title="Multilingual Business Document Classifier")
+
+demo.launch()