import time
import torch
from transformers import MarianMTModel, MarianTokenizer

MODEL_NAME = "Helsinki-NLP/opus-mt-en-fr"
tokenizer = MarianTokenizer.from_pretrained(MODEL_NAME)
model = MarianMTModel.from_pretrained(MODEL_NAME)

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(DEVICE)
model.eval()  # disable dropout, slightly faster inference

FILE_PATH = "input.txt"
OUTPUT_PATH = "output_fr.txt"

def translate(text: str) -> str:
    inputs = tokenizer(
        text,
        return_tensors="pt",
        padding=True,
        truncation=True,
        max_length=512
    ).to(DEVICE)

    with torch.no_grad():  # saves memory, faster on GPU
        translated = model.generate(**inputs)

    return tokenizer.decode(translated[0], skip_special_tokens=True)

def tail_and_translate(filepath: str):
    with open(filepath, "r", encoding="utf-8") as f:
        f.seek(0, 2)  # jump to end of file
        print(f"Watching {filepath}...")

        with open(OUTPUT_PATH, "a", encoding="utf-8") as out:
            while True:
                line = f.readline()
                if line:
                    line = line.strip()
                    if line:
                        translated = translate(line)
                        print(f"EN: {line}")
                        print(f"FR: {translated}")
                        out.write(translated + "\n")
                        out.flush()
                else:
                    time.sleep(0.2)

tail_and_translate(FILE_PATH)