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:
        return gr.update(recording=False), state
    else:
        return None, state

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=[{"role": "user", "content": [{"type": "audio", "data": audio_data}]}],
            temperature=0.7,
            max_tokens=256,
            stream=True,
        )

        for chunk in stream:
            if not chunk.choices:
                continue
            content = chunk.choices[0].delta.content
            audio = getattr(chunk.choices[0], 'audio', [])
            if content or audio:
                audio_bytes = b''.join([base64.b64decode(a) for a in audio]) if audio else None
                yield content, audio_bytes, state

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

def response(state: AppState):
    if state.stream is None or len(state.stream) == 0:
        yield None, None, state
        return

    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)

    # Add the user's audio input to the conversation
    state.conversation.append({"role": "user", "content": "Audio input"})

    # Prepare assistant's message
    assistant_message = {"role": "assistant", "content": ""}
    state.conversation.append(assistant_message)

    for text, audio, updated_state in generator:
        if text:
            assistant_message["content"] += text
        state = updated_state
        chatbot_output = state.conversation[-2:]  # Get the last two messages
        yield chatbot_output, audio, state

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

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", source="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
    )

    respond = input_audio.stop_recording(
        response,
        [state],
        [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=[respond, restart])

    demo.launch(queue=True, stream=True)