Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, MT5ForConditionalGeneration
+
+# Load tokenizer and model
+checkpoint = "syubraj/RomanEng2Nep-v2"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = MT5ForConditionalGeneration.from_pretrained(checkpoint)
+
+# Set max sequence length
+max_seq_len = 20
+
+# Define the translation function
+def translate(text):
+    # Tokenize the input text with a max length of 20
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=max_seq_len)
+
+    # Generate translation
+    translated = model.generate(**inputs)
+
+    # Decode the translated tokens back to text
+    translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+    return translated_text
+
+# Gradio interface
+iface = gr.Interface(
+    fn=translate,  # function to use for inference
+    inputs="text",  # input type
+    outputs="text",  # output type
+    title="Romanized English to Nepali Transliterator",
+    description="Translate Romanized English text into Nepali.",
+    examples=[["ahile"],["prakriti"], ["mahasagar"], ["pradarshan"],["khutkela"], ["nandan"], ["khola"]]
+)
+
+# Launch the Gradio app
+iface.launch()