Update app.py

app.py

@@ -30,7 +30,7 @@ from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file, save_file
 from diffusers import FluxPipeline
 from PIL import Image, ImageDraw, ImageFont
-from transformers import pipeline, MT5ForConditionalGeneration, T5Tokenizer
+from transformers import pipeline, T5ForConditionalGeneration, T5Tokenizer
 from refiners.fluxion.utils import manual_seed
 from refiners.foundationals.latent_diffusion import Solver, solvers
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.multi_upscaler import (
@@ -38,9 +38,11 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1.multi_upscaler i
     UpscalerCheckpoints,
 )
 from datetime import datetime
+from translate import Translator
+
+model = T5ForConditionalGeneration.from_pretrained("t5-large")
+tokenizer = T5Tokenizer.from_pretrained("t5-large")
 
-model = MT5ForConditionalGeneration.from_pretrained("google/mt5-xl")
-tokenizer = T5Tokenizer.from_pretrained("google/mt5-xl")
 
 def log(msg):
     print(f'{datetime.now().time()} {msg}')
@@ -716,22 +718,14 @@ def translate(txt,to_lang="en",from_lang=False):
     log(f'CALL translate')
     if not from_lang:
         from_lang = get_language(txt)
-    print(f"translating from {from_lang} to {to_lang}")
     if(from_lang == to_lang):
         log(f'RET translate with txt as {txt}')
         return txt
-    prefix = f"translate {language_codes[from_lang]} to {language_codes[to_lang]}: "
-    words = txt.split()
-    ret = ""
-    for index in range(math.ceil( len(words) / 500 )):
-        chunk = " ".join(words[index*500:(index+1)*500])
-        log(f'DBG translate chunk is {chunk}')
-        inputs = tokenizer.encode(prefix+chunk, return_tensors="pt", truncation=False, add_special_tokens=True)
-        gen = model.generate(inputs,num_beams=3)
-        toks = tokenizer.decode(gen[0], skip_special_tokens=True)
-        ret = ret + ("" if ret == "" else " ") + toks
-    log(f'RET translate with ret as {ret}')
-    return ret
+        
+    translator = Translator(from_lang=from_lang,to_lang=to_lang)
+    translation = translator.translate(txt)
+    log(f'RET translate with translation as {translation}')
+    return translation
 
 @spaces.GPU(duration=300)
 def handle_generation(artist,song,genre,lyrics):