Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load pre-trained model & tokenizer (Example: XLM-R for multilingual text classification)
+model_name = "xlm-roberta-base"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
+
+# Define prediction function
+def classify_text(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    with torch.no_grad():
+        output = model(**inputs)
+    label = torch.argmax(output.logits, dim=1).item()
+    return "Correct" if label == 1 else "Incorrect"
+
+# Gradio UI
+gradio_app = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(label="Enter Text"),
+    outputs="text",
+    title="Multi-Language RL Model"
+)
+
+gradio_app.launch()