Upload app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM
+
+# Define the model repository and tokenizer checkpoint
+model_checkpoint = "himanishprak23/neural_machine_translation"
+tokenizer_checkpoint = "Helsinki-NLP/opus-mt-en-hi"
+
+# Load the tokenizer from Helsinki-NLP and model from Hugging Face repository
+tokenizer = AutoTokenizer.from_pretrained(tokenizer_checkpoint)
+model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+
+def translate_text(input_text):
+    tokenized_input = tokenizer(input_text, return_tensors='tf', max_length=128, truncation=True)
+    generated_tokens = model.generate(**tokenized_input, max_length=128)
+    predicted_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
+    return predicted_text
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=translate_text,
+    inputs=gr.components.Textbox(lines=2, placeholder="Enter text to translate from English to Hindi..."),
+    outputs=gr.components.Textbox(),
+    title="English to Hindi Translator",
+    description="Enter English text and get the Hindi translation."
+)
+
+# Launch the Gradio app
+iface.launch()