Update app.py

app.py

@@ -19,6 +19,8 @@ import yt_dlp  # Added import for yt-dlp
 MODEL_NAME = "NbAiLab/nb-whisper-large"
 lang = "no"
 
+logo_path = "/home/angelina/Nedlastinger/Screenshot 2024-10-10 at 13-30-13 Nasjonalbiblioteket — Melkeveien designkontor.png"
+
 share = (os.environ.get("SHARE", "False")[0].lower() in "ty1") or None
 auth_token = os.environ.get("AUTH_TOKEN") or True
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
@@ -29,7 +31,7 @@ def pipe(file, return_timestamps=False):
     asr = pipeline(
         task="automatic-speech-recognition",
         model=MODEL_NAME,
-        chunk_length_s=30,
+        chunk_length_s=28,
         device=device,
         token=auth_token,
         torch_dtype=torch.float16,
@@ -62,6 +64,7 @@ def transcribe(file, return_timestamps=False):
             line = f"[{start_time} -> {end_time}] {chunk['text']}"
             text.append(line)
         formatted_text = "\n".join(text)
+    formatted_text += "\n\nTranskribert med NB-Whisper demo"
     return formatted_text
 
 def _return_yt_html_embed(yt_url):
@@ -97,6 +100,7 @@ def yt_transcribe(yt_url, return_timestamps=False):
 demo = gr.Blocks()
 
 with demo:
+    gr.Image(value=logo_path, label="Nasjonalbibliotek Logo", elem_id="logo")  # No tool parameter for static display
     mf_transcribe = gr.Interface(
         fn=transcribe,
         inputs=[
@@ -110,6 +114,7 @@ with demo:
             f" modellen [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) og 🤗 Transformers til å transkribere lydfiler opp til 30 minutter."
         ),
         allow_flagging="never",
+        show_submit_button=False,
     )
 
     # Uncomment to add the YouTube transcription interface if needed