import subprocess
import json
import datetime

# === CONFIG ===
# Use current timestamp for file naming
TIMESTAMP = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
MEETING_FILE = f"meeting_audio_{TIMESTAMP}.wav"
WHISPER_MODEL = "small.en"
DURATION = 36000  # seconds (10 hour max)
OUTPUT_TRANSCRIPT = f"meeting_audio_{TIMESTAMP}.json"
OUTPUT_SUMMARY = f"summary_{TIMESTAMP}.txt"

def record_audio():
    print("🎙 Recording meeting audio... (q to stop early)")
    subprocess.run([
        "ffmpeg",
        "-f", "pulse",              # Linux (change for Windows/macOS)
        "-i", "default",            # capture default input/output mix
        "-t", str(DURATION),
        "-ac", "2",
        "-ar", "44100",
        MEETING_FILE
    ], check=True)

def transcribe_audio():
    print("🔠 Transcribing with WhisperX...")
    subprocess.run([
        # "pipenv run python3 -m whisperx",
        "whisper",
        MEETING_FILE,
        "--device", "cpu",  # Use CPU
        "--language", "en",
        # "--diarize", Put back when whisperX works.
        "--model", WHISPER_MODEL,
        "--output_format", "json",
        "--output_dir", "."
    ], check=True)

def summarize_with_local_model():
    print("🧾 Summarizing locally with Ollama...")
    with open(OUTPUT_TRANSCRIPT, "r", encoding="utf-8") as f:
        transcript = json.load(f)

    text = "\n".join(
        seg["text"]
        for seg in transcript["segments"]
    )

    prompt = f"""Summarize this meeting transcript into clear bullet points with:
    - Key decisions
    - Action items
    - Notable discussion points
    
    Transcript:
    {text}
    """

    result = subprocess.run(
        ["ollama", "run", "llama3:8b"],
        input=prompt.encode(),
        capture_output=True,
        check=True
    )

    summary = result.stdout.decode()

    with open(OUTPUT_SUMMARY, "w", encoding="utf-8") as f:
        f.write(summary)

    print("✅ Summary written to", OUTPUT_SUMMARY)

def cleanup():
    import os
    if os.path.exists(MEETING_FILE):
        os.remove(MEETING_FILE)
    if os.path.exists(OUTPUT_TRANSCRIPT):
        os.remove(OUTPUT_TRANSCRIPT)

if __name__ == "__main__":
    record_audio()
    transcribe_audio()
    summarize_with_local_model()
    cleanup()