| # Use a pipeline as a high-level helper | |
| import gradio as gr | |
| from transformers import pipeline | |
| pipe = pipeline("translation", model="ayoubkirouane/Med_English2Spanish") | |
| def predict(text): | |
| return pipe(text)[0]["translation_text"] | |
| with gr.Blocks() as demo: | |
| english_inp = gr.Textbox(label="English Sentence") | |
| spanish_op = gr.Textbox(label="Spanish Translation") | |
| translate_btn = gr.Button("Translate") | |
| translate_btn.click(fn=predict, inputs=english_inp, outputs=spanish_op) | |
| demo.launch() |