Update app.py

app.py

@@ -2,6 +2,14 @@ import gradio as gr
 import torch
 from transformers import pipeline
 from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration
+import fasttext
+from huggingface_hub import hf_hub_download
+
+
+model_path = hf_hub_download(repo_id="cis-lmu/glotlid", filename="model.bin")
+identification_model = fasttext.load_model(model_path)
+def lang_ident(text):
+    return indetification_model.predict(text)
 
 pretrained_model: str = "facebook/m2m100_1.2B"
 cache_dir: str = "models/"
@@ -148,7 +156,6 @@ with demo:
   audio = gr.Audio(type="filepath", label = "Upload a file")
   text0 = gr.Textbox()
   text = gr.Textbox()
-  text2 = gr.Textbox()
   source_lang = gr.Dropdown(label="Source lang", choices=list(lang_id.keys()), value=list(lang_id.keys())[0])
   target_lang = gr.Dropdown(label="target lang", choices=list(lang_id.keys()), value=list(lang_id.keys())[0])
   
@@ -157,10 +164,10 @@ with demo:
     #                source_lang])
   b1 = gr.Button("convert to text")
   b3 = gr.Button("translate")
-  b3.click(translation_text, inputs = [source_lang, target_lang, text0], outputs = text2)
+  b3.click(translation_text, inputs = [source_lang, target_lang, text0], outputs = text)
   b1.click(audio_a_text, inputs=audio, outputs=text)
 
-  b2 = gr.Button("Classification of speech")
-  b2.click(print_s, inputs= [source_lang,target_lang, text0], outputs=text)
+  b2 = gr.Button("Classification of language")
+  b2.click(lang_ident,inputs  = text0, outputs=text)
 
 demo.launch()