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)