Update app.py

app.py

@@ -106,18 +106,37 @@ def treat_wav_file(file_mic, file_upload, asr=asr_brain, device="cpu"):
 
 #Gradio interface
 import gradio as gr
+
 title = "Tunisian Speech Recognition"
-description = ''' This is a Tunisian ASR based on WavLM Model, fine-tuned on a dataset of 2.5 Hours resulting in a WER of 24%.
+
+description = ''' 
+This is a Tunisian ASR based on the **WavLM Model**, fine-tuned on a dataset of **2.5 hours**, resulting in a **W.E.R of 24%** and a **C.E.R of 9%**.  
 \n
-                                        \n Interesting isn\'t it !'''
-
-gr.Interface(
-    fn=treat_wav_file,
-    inputs=[
-        gr.Audio(sources="microphone", type='filepath', label="Record"),
-        gr.Audio(sources="upload", type='filepath', label="Upload File")
-    ],
-    outputs="text",
-    title=title,
-    description=description
-).launch()
+Interested? Try it out!
+'''
+
+disclaimer = '''
+> ⚠️ **Disclaimer:**  
+> This is a **demo model**, and transcription accuracy is limited due to Hugging Face model storage constraints.  
+> For better performance, can run the full model locally.  
+> Check out the repository and follow the instructions: [LINK](https://huggingface.co/brdhaker3/TunASR)  
+'''
+
+with gr.Blocks() as demo:
+    gr.Markdown(f"# {title}")
+    gr.Markdown(description)
+    gr.Markdown(disclaimer)
+
+    interface = gr.Interface(
+        fn=treat_wav_file,
+        inputs=[
+            gr.Audio(sources="microphone", type='filepath', label="Record"),
+            gr.Audio(sources="upload", type='filepath', label="Upload File")
+        ],
+        outputs="text",
+        title="",
+        description=""
+    )
+    
+
+demo.launch()