app.py

import transformers
import gradio as gr
import librosa
import torch
import spaces
import numpy as np

# Initialize the conversation history globally
conversation_history = []

@spaces.GPU(duration=120)
def transcribe_and_respond(audio_file, chat_history):
    try:
        pipe = transformers.pipeline(
            model='sarvamai/shuka_v1',
            trust_remote_code=True,
            device=0,
            torch_dtype=torch.bfloat16
        )

        # Load the audio file
        audio, sr = librosa.load(audio_file, sr=16000)

        # Debug: Print audio properties for debugging
        print(f"Audio dtype: {audio.dtype}, Audio shape: {audio.shape}, Sample rate: {sr}")

        # Prepare conversation turns
        turns = chat_history.copy()  # Take the existing chat history and append user input
        turns.append({'role': 'user', 'content': '<|audio|>'})

        # Debug: Print the updated turns for debugging purposes
        print(f"Updated turns: {turns}")

        # Call the model with the updated conversation turns and audio
        output = pipe({'audio': audio, 'turns': turns, 'sampling_rate': sr}, max_new_tokens=512)

        # Append the model's response to the conversation history
        turns.append({'role': 'system', 'content': output})

        # Debug: Print the model's response
        print(f"Model output: {output}")

        # Format the chat history for Gradio's Chatbot
        chat_history_for_display = []
        for turn in turns:
            if turn['role'] == 'user':
                chat_history_for_display.append(("User", "🗣️ (Spoken Audio)"))
            else:
                chat_history_for_display.append(("AI", turn['content']))

        return chat_history_for_display, turns  # Return the formatted chat history for display and the updated history

    except Exception as e:
        return f"Error: {str(e)}", chat_history  # Ensure history is returned even on error

# Define the Gradio interface
iface = gr.Interface(
    fn=transcribe_and_respond,
    inputs=[
        gr.Audio(sources="microphone", type="filepath", label="Your Audio (Microphone)"), 
        gr.State([])  # Hidden state to maintain conversation history
    ],
    outputs=[
        gr.Chatbot(label="Conversation History"),  # Display the conversation
        gr.State([])  # Hidden state to keep track of the updated conversation history
    ],
    title="Shuka demo",
    description="shuka live demo",
    live=True,  # Enable live mode for real-time interaction
    allow_flagging="auto",
#    enable_queue=True
)

if __name__ == "__main__":
    iface.launch()