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import gradio as gr
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_name = "Helsinki-NLP/opus-mt-en-fr"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
def translate(text):
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
english = gr.Textbox(label="English text")
with gr.Column():
german = gr.Textbox(label="French text")
translate_btn = gr.Button("Translate")
translate_btn.click(fn=translate, inputs=english, outputs=german)
# Adding examples at the bottom
gr.Examples(["Hello, how are you?", "I am learning Gradio."], inputs=english)
# 4. Launch on the specific port Hugging Face requires
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860) |