better path

app.py

@@ -124,7 +124,7 @@ with block:
             btn_run.click(fn=nllb_inf.translate_file,
                           inputs=[file_subs, dd_nllb_model, dd_nllb_sourcelang, dd_nllb_targetlang],
                           outputs=[tb_indicator])
-            btn_openfolder.click(fn=lambda: open_folder("outputs\\translations"), inputs=None, outputs=None)
+            btn_openfolder.click(fn=lambda: open_folder(os.path.join("outputs", "translations")), inputs=None, outputs=None)
 
 
 block.launch()

modules/nllb_inference.py

@@ -37,9 +37,9 @@ class NLLBInference(BaseInterface):
                 progress(0, desc="Initializing NLLB Model..")
                 self.current_model_size = model_size
                 self.model = AutoModelForSeq2SeqLM.from_pretrained(pretrained_model_name_or_path=model_size,
-                                                                   cache_dir="models/NLLB")
+                                                                   cache_dir=os.path.join("models", "NLLB"))
                 self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_size,
-                                                               cache_dir=f"models/NLLB/tokenizers")
+                                                               cache_dir=os.path.join("models", "NLLB", "tokenizers"))
 
             src_lang = NLLB_AVAILABLE_LANGS[src_lang]
             tgt_lang = NLLB_AVAILABLE_LANGS[tgt_lang]
@@ -66,7 +66,7 @@ class NLLBInference(BaseInterface):
 
                     timestamp = datetime.now().strftime("%m%d%H%M%S")
                     file_name = file_name[:-9]
-                    output_path = f"outputs/translations/{file_name}-{timestamp}"
+                    output_path = os.path.join("outputs", "translations", f"{file_name}-{timestamp}")
 
                     write_file(subtitle, f"{output_path}.srt")
 

modules/whisper_Inference.py

@@ -28,7 +28,7 @@ class WhisperInference(BaseInterface):
             if model_size != self.current_model_size or self.model is None:
                 progress(0, desc="Initializing Model..")
                 self.current_model_size = model_size
-                self.model = whisper.load_model(name=model_size, download_root="models/Whisper")
+                self.model = whisper.load_model(name=model_size, download_root=os.path.join("models", "Whisper"))
 
             if lang == "Automatic Detection":
                 lang = None
@@ -54,7 +54,7 @@ class WhisperInference(BaseInterface):
                 file_name = file_name[:-9]
                 file_name = safe_filename(file_name)
                 timestamp = datetime.now().strftime("%m%d%H%M%S")
-                output_path = f"outputs/{file_name}-{timestamp}"
+                output_path = os.path.join("outputs", f"{file_name}-{timestamp}")
 
                 if subformat == "SRT":
                     subtitle = get_srt(result["segments"])
@@ -89,7 +89,7 @@ class WhisperInference(BaseInterface):
             if model_size != self.current_model_size or self.model is None:
                 progress(0, desc="Initializing Model..")
                 self.current_model_size = model_size
-                self.model = whisper.load_model(name=model_size, download_root="models/Whisper")
+                self.model = whisper.load_model(name=model_size, download_root=os.path.join("models", "Whisper"))
 
             if lang == "Automatic Detection":
                 lang = None
@@ -110,7 +110,7 @@ class WhisperInference(BaseInterface):
 
             file_name = safe_filename(yt.title)
             timestamp = datetime.now().strftime("%m%d%H%M%S")
-            output_path = f"outputs/{file_name}-{timestamp}"
+            output_path = os.path.join("outputs", f"{file_name}-{timestamp}")
 
             if subformat == "SRT":
                 subtitle = get_srt(result["segments"])
@@ -139,7 +139,7 @@ class WhisperInference(BaseInterface):
             if model_size != self.current_model_size or self.model is None:
                 progress(0, desc="Initializing Model..")
                 self.current_model_size = model_size
-                self.model = whisper.load_model(name=model_size, download_root="models/Whisper")
+                self.model = whisper.load_model(name=model_size, download_root=os.path.join("models", "Whisper"))
 
             if lang == "Automatic Detection":
                 lang = None
@@ -157,7 +157,7 @@ class WhisperInference(BaseInterface):
             progress(1, desc="Completed!")
 
             timestamp = datetime.now().strftime("%m%d%H%M%S")
-            output_path = f"outputs/Mic-{timestamp}"
+            output_path = os.path.join("outputs", f"{file_name}-{timestamp}")
 
             if subformat == "SRT":
                 subtitle = get_srt(result["segments"])

modules/youtube_manager.py

@@ -1,4 +1,5 @@
 from pytube import YouTube
+import os
 
 def get_ytdata(link):
     return YouTube(link)
@@ -8,4 +9,4 @@ def get_ytmetas(link):
     return yt.thumbnail_url,yt.title,yt.description
 
 def get_ytaudio(ytdata:YouTube):
-    return ytdata.streams.get_audio_only().download(filename="modules/yt_tmp.wav")
+    return ytdata.streams.get_audio_only().download(filename=os.path.join("modules", "yt_tmp.wav"))