Update app.py

app.py

@@ -10,8 +10,6 @@ client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1")
 # Initialize the ASR pipeline
 asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
 
-INITIAL_MESSAGE = "Hi! I'm your music buddy—tell me about your mood and the type of tunes you're in the mood for today!"
-
 def speech_to_text(speech):
     """Converts speech to text using the ASR pipeline."""
     return asr(speech)["text"]
@@ -66,6 +64,7 @@ def format_prompt(message, history):
 
     Note: if user asks something like i need a coffee then do not classify the mood directly and ask more follow-up questions as asked in examples.
 
+    [Examples omitted for brevity]
     """
     prompt = f"{fixed_prompt}\n"
     for user_prompt, bot_response in history:
@@ -94,9 +93,6 @@ async def generate_audio(history):
         return audio_path
     return None
 
-def init_chat():
-    return [("", INITIAL_MESSAGE)], [("", INITIAL_MESSAGE)], None
-
 # Gradio interface setup
 with gr.Blocks() as demo:
     gr.Markdown("# Mood-Based Music Recommender with Continuous Voice Chat")
@@ -109,10 +105,7 @@ with gr.Blocks() as demo:
 
     with gr.Row():
         submit = gr.Button("Send")
-        voice_input = gr.Audio(sources="microphone", type="filepath", label="Voice Input")
-
-    # Initialize chat with greeting
-    demo.load(init_chat, outputs=[state, chatbot, audio_output])
+        voice_input = gr.Audio(source="microphone", type="filepath", label="Voice Input")
 
     # Handle text input
     msg.submit(process_input, inputs=[msg, state], outputs=[state, chatbot, msg, voice_input]).then(
@@ -134,4 +127,4 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True)