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app.py

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+from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+import gradio as gr
+
+# Load the tokenizer and model
+model_checkpoint = "aryaumesh/english-to-telugu"
+tokenizer = MBart50TokenizerFast.from_pretrained(model_checkpoint)
+model = MBartForConditionalGeneration.from_pretrained(model_checkpoint)
+
+# Function to translate text
+def translate_to_telugu(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model.generate(**inputs)
+    translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return translation
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=translate_to_telugu,
+    inputs=gr.Textbox(lines=2, placeholder="Enter text in English..."),  # Text input
+    outputs=gr.Textbox(label="Translation to Telugu"),  # Text output
+    title="English to Telugu Translator",
+    description="Translate text from English to Telugu using an MBart model."
+)
+
+# Launch the Gradio interface
+iface.launch()
+

requirements.txt

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+transformers
+gradio