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import transformers
import gradio as gr
from transformers import pipeline
import os
TOKEN = os.getenv('HUGGING_FACE_HUB_TOKEN')
models = [
"barghavani/English_to_French",
"barghavani/English_to_German",
"barghavani/English_to_Hindi",
#"Helsinki-NLP/opus-mt-ber-fr",
#"Helsinki-NLP/opus-mt-es-ber",
#"Helsinki-NLP/opus-mt-ber-es",
#"Helsinki-NLP/opus-mt-kab-en",
#
]
pipes = {}
def predict(text, model):
if model not in pipes:
pipes[model] = pipeline("translation", model=model)
pipe = pipes[model]
return pipe(text)[0]['translation_text']
demo = gr.Interface(
fn=predict,
inputs=[
gr.Textbox(lines=5, label="Input Text"),
gr.Dropdown(models, label="Model")
],
outputs='text',
)
demo.launch(share=True,debug=True) |