app.py

import gradio as gr

# Import the pipeline
from transformers import pipeline

# Define the pipeline
# Note: This pipeline is hosted on the Hugging Face model hub
# https://huggingface.co/Helsinki-NLP/opus-mt-en-he
# You can replace this with any other translation pipeline
# https://huggingface.co/models?filter=translation
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-he")

# Define a pipeline for reverse translation
# Note: This pipeline is hosted on the Hugging Face model hub
# https://huggingface.co/Helsinki-NLP/opus-mt-he-en
# You can replace this with any other translation pipeline
# https://huggingface.co/models?filter=translation
pipe_reverse = pipeline("translation", model="Helsinki-NLP/opus-mt-he-en")


# Define the function
def predict(text):
  # Return the translation
  return pipe(text)[0]["translation_text"]

def predict_reverse(text):
    # Return the translation
    return pipe_reverse(text)[0]["translation_text"]

# Define the interface
iface = gr.Interface(
  fn=predict, 
  fn_reverse=predict_reverse,
  inputs='text',
  outputs='text',
    title="English to Hebrew Translator",
  description="Translate English to Hebrew",
  examples=[["Hello! My name is Bob."], ["I like to eat apples and banana"]]
)



# Launch the interface
iface.launch()