Use LLM Gateway with Streaming Speech-to-Text (STT)

import pyaudio
import websocket
import json
import threading
import time
import requests
from urllib.parse import urlencode

YOUR_API_KEY = "YOUR_API_KEY"  # Replace with your actual API key

CONNECTION_PARAMS = {
    "sample_rate": 16000,
    "speech_model": "u3-rt-pro",
}
API_ENDPOINT_BASE_URL = "wss://streaming.assemblyai.com/v3/ws"
API_ENDPOINT = f"{API_ENDPOINT_BASE_URL}?{urlencode(CONNECTION_PARAMS)}"

FRAMES_PER_BUFFER = 800
SAMPLE_RATE = CONNECTION_PARAMS["sample_rate"]
CHANNELS = 1
FORMAT = pyaudio.paInt16

audio = None
stream = None
ws_app = None
audio_thread = None
stop_event = threading.Event()
conversation_data = ""

def analyze_with_llm_gateway(text):
    """Called when the WebSocket connection is closing and the transcript text is sent to LLM Gateway to be analyzed."""
    headers = {
        "authorization": YOUR_API_KEY,
        "content-type": "application/json"
    }

    prompt = "You are a helpful coach. Provide an analysis of the transcript and offer areas to improve with exact quotes. Include no preamble. Start with an overall summary then get into the examples with feedback."

    llm_gateway_data = {
        "model": "claude-sonnet-4-20250514",
        "messages": [
            {"role": "user", "content": f"{prompt}\n\nTranscript: {text}"}
        ],
        "max_tokens": 4000
    }

    result = requests.post(
        "https://docs/llm-gateway.assemblyai.com/v1/chat/completions",
        headers=headers,
        json=llm_gateway_data
    )
    return result.json()["choices"][0]["message"]["content"]

def on_open(ws):
    print("WebSocket connection opened.")
    def stream_audio():
        global stream
        while not stop_event.is_set():
            try:
                audio_data = stream.read(FRAMES_PER_BUFFER, exception_on_overflow=False)
                ws.send(audio_data, websocket.ABNF.OPCODE_BINARY)
            except Exception as e:
                print(f"Error streaming audio: {e}")
                break

    global audio_thread
    audio_thread = threading.Thread(target=stream_audio)
    audio_thread.daemon = True
    audio_thread.start()

def on_message(ws, message):
    try:
        data = json.loads(message)
        msg_type = data.get("type")

        if msg_type == "Begin":
            print(f"Session began: ID={data.get('id')}")
        elif msg_type == "Turn":
            transcript = data.get("transcript", "")
            if data.get("end_of_turn"):
                global conversation_data
                print(f"\r{' ' * 80}\r{transcript}")
                conversation_data += f"{transcript}\n"
            else:
                print(f"\r{transcript}", end="")
        elif msg_type == "Termination":
            print(f"\nSession terminated: {data.get('audio_duration_seconds', 0)}s of audio")
    except Exception as e:
        print(f"Error handling message: {e}")

def on_error(ws, error):
    print(f"\nWebSocket Error: {error}")
    stop_event.set()

def on_close(ws, close_status_code, close_msg):
    print(f"\nWebSocket Disconnected: Status={close_status_code}")
    global stream, audio
    stop_event.set()
    if stream:
        if stream.is_active():
            stream.stop_stream()
        stream.close()
    if audio:
        audio.terminate()

def run():
    global audio, stream, ws_app

    audio = pyaudio.PyAudio()
    stream = audio.open(
        input=True,
        frames_per_buffer=FRAMES_PER_BUFFER,
        channels=CHANNELS,
        format=FORMAT,
        rate=SAMPLE_RATE,
    )
    print("Speak into your microphone. Press Ctrl+C to stop.")

    ws_app = websocket.WebSocketApp(
        API_ENDPOINT,
        header={"Authorization": YOUR_API_KEY},
        on_open=on_open,
        on_message=on_message,
        on_error=on_error,
        on_close=on_close,
    )

    ws_thread = threading.Thread(target=ws_app.run_forever)
    ws_thread.daemon = True
    ws_thread.start()

    try:
        while ws_thread.is_alive():
            time.sleep(0.1)
    except KeyboardInterrupt:
        print("\nStopping...")
        stop_event.set()
        if ws_app and ws_app.sock and ws_app.sock.connected:
            ws_app.send(json.dumps({"type": "Terminate"}))
            time.sleep(2)
        if ws_app:
            ws_app.close()
        ws_thread.join(timeout=2.0)

        if conversation_data.strip():
            print("Analyzing conversation with LLM Gateway...")
            print(analyze_with_llm_gateway(conversation_data))
        else:
            print("No conversation data to analyze.")

if __name__ == "__main__":
    run()

def analyze_with_llm_gateway(text):
    """Called when the WebSocket connection is closing and the transcript text is sent to LLM Gateway to be analyzed."""
    headers = {
        "authorization": YOUR_API_KEY,
        "content-type": "application/json"
    }

    prompt = "You are a helpful coach. Provide an analysis of the transcript and offer areas to improve with exact quotes. Include no preamble. Start with an overall summary then get into the examples with feedback."

    llm_gateway_data = {
        "model": "claude-sonnet-4-20250514",
        "messages": [
            {"role": "user", "content": f"{prompt}\n\nTranscript: {text}"}
        ],
        "max_tokens": 4000
    }

    result = requests.post(
        "https://docs/llm-gateway.assemblyai.com/v1/chat/completions",
        headers=headers,
        json=llm_gateway_data
    )
    return result.json()["choices"][0]["message"]["content"]