Update app.py

app.py

@@ -23,6 +23,14 @@ import whisper
 # tts_model = VitsModel.from_pretrained("facebook/mms-tts-pol")
 # tts_model.to("cuda")
 # print("TTS Loaded!")
+DESCRIPTION = """\
+# Ai Trek - Generative AI usage
+This Space demonstrates LAIONBOT functionalities, 
+🔎 Large Language Models is a model notable for its ability to achieve general-purpose language generation and understanding. 
+🔨 On this demo, we can play with it not only by using text, but also asking questions and getting answers by Text to  speech model.
+"""
+
+
 
 def load_whisper():
     return whisper.load_model("medium", device = 'cpu')
@@ -168,17 +176,18 @@ def regenerate(_chatbot, _task_history):
 
 
 with gr.Blocks() as chat_demo:
+    gr.Markdown(DESCRIPTION)
     chatbot = gr.Chatbot(label='Llama Voice Chatbot', elem_classes="control-height")
     query = gr.Textbox(lines=2, label='Input')
     task_history = gr.State([])
     audio_output = gr.Audio('temp_file.wav', label="Generated Audio (wav)", type='filepath', autoplay=False)
     
     with gr.Row():
-        submit_btn = gr.Button("🚀 Wyślij tekst")
+        submit_btn = gr.Button("🚀 Send an input file to LLM")
 
     with gr.Row():
         audio_upload = gr.Audio(source="microphone", type="filepath", show_label=False)
-        submit_audio_btn = gr.Button("🎙️ Wyślij audio")
+        submit_audio_btn = gr.Button("🎙️ Send an audio")
 
     submit_btn.click(predict, [query, chatbot, task_history], [chatbot], show_progress=True)
     submit_audio_btn.click(predict, [audio_upload, chatbot, task_history], [chatbot], show_progress=True).then(update_audio, chatbot, audio_output)