app.py

import gradio as gr
import numpy as np
import io
from pydub import AudioSegment
import tempfile
import openai
import time
from dataclasses import dataclass, field
from threading import Lock
import base64

@dataclass
class AppState:
    stream: np.ndarray | None = None
    sampling_rate: int = 0
    pause_detected: bool = False
    conversation: list = field(default_factory=list)
    client: openai.OpenAI = None
    output_format: str = "mp3"
    stopped: bool = False

# Global lock for thread safety
state_lock = Lock()

def create_client(api_key):
    return openai.OpenAI(
        base_url="https://llama3-1-8b.lepton.run/api/v1/",
        api_key=api_key
    )

def determine_pause(audio, sampling_rate, state):
    # Take the last 1 second of audio
    pause_length = int(sampling_rate * 1)  # 1 second
    if len(audio) < pause_length:
        return False
    last_audio = audio[-pause_length:]
    amplitude = np.abs(last_audio)

    # Calculate the average amplitude in the last 1 second
    avg_amplitude = np.mean(amplitude)
    silence_threshold = 0.01  # Adjust this threshold as needed
    if avg_amplitude < silence_threshold:
        return True
    else:
        return False

def process_audio(audio: tuple, state: AppState):
    if state.stream is None:
        state.stream = audio[1]
        state.sampling_rate = audio[0]
    else:
        state.stream = np.concatenate((state.stream, audio[1]))

    pause_detected = determine_pause(state.stream, state.sampling_rate, state)
    state.pause_detected = pause_detected

    if state.pause_detected:
        # Stop recording
        return gr.update(recording=False), state
    else:
        return None, state

def update_or_append_conversation(conversation, id, role, content):
    # Find if there's an existing message with the given id
    for message in conversation:
        if message.get("id") == id and message.get("role") == role:
            message["content"] = content
            return
    # If not found, append a new message
    conversation.append({"id": id, "role": role, "content": content})

def generate_response_and_audio(audio_bytes: bytes, state: AppState):
    if state.client is None:
        raise gr.Error("Please enter a valid API key first.")

    format_ = state.output_format
    bitrate = 128 if format_ == "mp3" else 32  # Higher bitrate for MP3, lower for OPUS
    audio_data = base64.b64encode(audio_bytes).decode()

    try:
        stream = state.client.chat.completions.create(
            extra_body={
                "require_audio": True,
                "tts_preset_id": "jessica",
                "tts_audio_format": format_,
                "tts_audio_bitrate": bitrate
            },
            model="llama3.1-8b",
            messages=state.conversation + [{"role": "user", "content": [{"type": "audio", "data": audio_data}]}],
            temperature=0.7,
            max_tokens=256,
            stream=True,
        )

        id = str(time.time())
        full_response = ""
        asr_result = ""
        audio_bytes_accumulated = b''

        for chunk in stream:
            if not chunk.choices:
                continue
            delta = chunk.choices[0].delta
            content = delta.get("content", "")
            audio = getattr(chunk.choices[0], "audio", [])
            asr_results = getattr(chunk.choices[0], "asr_results", [])

            if asr_results:
                asr_result += "".join(asr_results)
                yield id, None, asr_result, None, state

            if content:
                full_response += content
                yield id, full_response, None, None, state

            if audio:
                # Accumulate audio bytes and yield them
                audio_bytes_accumulated += b''.join([base64.b64decode(a) for a in audio])
                yield id, None, None, audio_bytes_accumulated, state

        yield id, full_response, asr_result, audio_bytes_accumulated, state

    except Exception as e:
        raise gr.Error(f"Error during audio streaming: {e}")

def response(state: AppState):
    if not state.pause_detected:
        return gr.update(), gr.update(), state

    if state.stream is None or len(state.stream) == 0:
        return gr.update(), gr.update(), state

    audio_buffer = io.BytesIO()
    segment = AudioSegment(
        state.stream.tobytes(),
        frame_rate=state.sampling_rate,
        sample_width=state.stream.dtype.itemsize,
        channels=(1 if len(state.stream.shape) == 1 else state.stream.shape[1]),
    )
    segment.export(audio_buffer, format="wav")

    generator = generate_response_and_audio(audio_buffer.getvalue(), state)

    for id, text, asr, audio, updated_state in generator:
        state = updated_state
        if asr:
            update_or_append_conversation(state.conversation, id, "user", asr)
        if text:
            update_or_append_conversation(state.conversation, id, "assistant", text)
        chatbot_output = state.conversation
        yield chatbot_output, audio, state

    # Reset the audio stream for the next interaction
    state.stream = None
    state.pause_detected = False

def maybe_call_response(state):
    if state.pause_detected:
        return response(state)
    else:
        # Do nothing
        return gr.update(), gr.update(), state

def start_recording_user(state: AppState):
    if not state.stopped:
        return gr.update(recording=True)
    else:
        return gr.update(recording=False)

def set_api_key(api_key, state):
    if not api_key:
        raise gr.Error("Please enter a valid API key.")
    state.client = create_client(api_key)
    return "API key set successfully!", state

def update_format(format, state):
    state.output_format = format
    return state

with gr.Blocks() as demo:
    with gr.Row():
        api_key_input = gr.Textbox(type="password", label="Enter your Lepton API Key")
        set_key_button = gr.Button("Set API Key")

    api_key_status = gr.Textbox(label="API Key Status", interactive=False)

    with gr.Row():
        format_dropdown = gr.Dropdown(choices=["mp3", "opus"], value="mp3", label="Output Audio Format")

    with gr.Row():
        with gr.Column():
            input_audio = gr.Audio(label="Input Audio", sources="microphone", type="numpy")
        with gr.Column():
            chatbot = gr.Chatbot(label="Conversation", type="messages")
            output_audio = gr.Audio(label="Output Audio", autoplay=True)

    state = gr.State(AppState())

    set_key_button.click(set_api_key, inputs=[api_key_input, state], outputs=[api_key_status, state])
    format_dropdown.change(update_format, inputs=[format_dropdown, state], outputs=[state])

    stream = input_audio.stream(
        process_audio,
        [input_audio, state],
        [input_audio, state],
        stream_every=0.25,  # Reduced to make it more responsive
        time_limit=60,  # Increased to allow for longer messages
    )

    stream.then(
        maybe_call_response,
        inputs=[state],
        outputs=[chatbot, output_audio, state],
    )

    # Automatically restart recording after the assistant's response
    restart = output_audio.change(
        start_recording_user,
        [state],
        [input_audio]
    )

    # Add a "Stop Conversation" button
    cancel = gr.Button("Stop Conversation", variant="stop")
    cancel.click(lambda: (AppState(stopped=True), gr.update(recording=False)), None,
                 [state, input_audio], cancels=[stream, restart])

    demo.launch(queue=True)