Update app.py

app.py

@@ -1,61 +1,42 @@
 import gradio as gr
-
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import fasttext
 
-model_path = "anzorq/m2m100_418M_ft_ru-kbd_44K"  
-src_lang="ru" 
-tgt_lang="zu"
+# Initialize fastText model
+model_path = 'lid.323.ftz'
+language_model = fasttext.load_model(model_path)
 
-# tokenizer = AutoTokenizer.from_pretrained(model_path, src_lang=src_lang)
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_path, use_safetensors=True)#, load_in_4bit=True, device_map="auto")
+model_path_translation = "anzorq/m2m100_418M_ft_ru-kbd_44K"
+tokenizer = AutoTokenizer.from_pretrained(model_path_translation)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_path_translation, use_safetensors=True)
 
 def translate(text, num_beams=4, num_return_sequences=4):
-  inputs = tokenizer(text, return_tensors="pt")
-
-  num_return_sequences = min(num_return_sequences, num_beams)
-
-  translated_tokens = model.generate(
-      **inputs, forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang], num_beams=num_beams, num_return_sequences=num_return_sequences
-  )
-
-  translations = []
-  for translation in tokenizer.batch_decode(translated_tokens, skip_special_tokens=True):
-      translations.append(translation)
-
-  # result = {"input":text, "translations":translations}
-  return text, translations
-
-output = gr.Textbox()
-# with gr.Accordion("Advanced Options"):
-num_beams = gr.inputs.Slider(2, 10, step=1, label="Number of beams", default=4)
-num_return_sequences = gr.inputs.Slider(2, 10, step=1, label="Number of returned sentences", default=4)
-
+    # Detect language
+    languages, _ = language_model.predict(text, k=1)
+    detected_language = languages[0].replace("__label__", "")
+    
+    inputs = tokenizer(text, return_tensors="pt")
+    num_return_sequences = min(num_return_sequences, num_beams)
+    translated_tokens = model.generate(
+        **inputs, forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang], num_beams=num_beams, num_return_sequences=num_return_sequences
+    )
+    translations = [tokenizer.decode(translation, skip_special_tokens=True) for translation in translated_tokens]
+    
+    return detected_language, text, translations
 
 title = "Russian-Circassian translator demo"
 article = "<p style='text-align: center'>Want to help? Join the <a href='https://discord.gg/cXwv495r' target='_blank'>Discord server</a></p>"
 
-examples = [
-    ["Мы идем домой"],
-    ["Сегодня хорошая погода"],
-    ["Дети играют во дворе"],
-    ["We live in a big house"],
-    ["Tu es une bonne personne."],
-    ["أين تعيش؟"],
-    ["Bir şeyler yapmak istiyorum."],
-    ["– Если я его отпущу, то ты вовек не сможешь его поймать, – заявил Сосруко."],
-    ["Как только старик ушел, Сатаней пошла к Саусырыко."],
-    ["我永远不会放弃你。"],
-    ["우리는 소치에 살고 있습니다."],
-]
+num_beams = gr.inputs.Slider(2, 10, step=1, label="Number of beams", default=4)
+num_return_sequences = gr.inputs.Slider(2, 10, step=1, label="Number of returned sentences", default=4)
 
 gr.Interface(
-  fn=translate,
-  inputs=["text", num_beams, num_return_sequences],
-  outputs=["text", output],
-  title=title,
-  # examples=examples,
-  article=article).launch()
+    fn=translate,
+    inputs=["text", num_beams, num_return_sequences],
+    outputs=["text", "text", gr.Textbox()],
+    titles=["Detected Language", "Input", "Translations"],
+    title=title,
+    article=article).launch()
 
 # import gradio as gr