import subprocess
import sys
from datetime import datetime
from pathlib import Path

# Configuration
OUTPUT_DIR = Path(r"C:\Users\antoi\antoine\My Libraries\Antoine Brain Extension\+\Transcripts")
MODEL = "openai/whisper-large-v3"

def transcribe(audio_path: str):
    audio_file = Path(audio_path)
    timestamp = datetime.now().strftime("%Y-%m-%d %H-%M")
    note_name = f"Voice Note {timestamp}.md"
    temp_file = Path.home() / "AppData/Local/Temp/whisper_output.txt"

    print(f"\n🎙️ Transcribing: {audio_file.name}")
    print(f"📝 Output: {note_name}\n")

    # Run whisper
    subprocess.run([
        "insanely-fast-whisper",
        "--file-name", str(audio_file),
        "--transcript-path", str(temp_file),
        "--model-name", MODEL
    ])

    # Read transcript
    transcript = temp_file.read_text(encoding="utf-8")

    # Create markdown note
    note_content = f"""---
created: {datetime.now().strftime("%Y-%m-%d %H:%M")}
type: voice-note
status: raw
tags:
  - transcript
  - voice-memo
---

# Voice Note - {datetime.now().strftime("%Y-%m-%d")} at {datetime.now().strftime("%H:%M")}

## Metadata

- **Source file:** `{audio_file.name}`
- **Transcribed:** {datetime.now().strftime("%Y-%m-%d %H:%M")}

---

## Raw Transcript

{transcript}

---

## Notes distillees

<!-- Coller le transcript dans Claude pour organiser et distiller -->

"""

    output_path = OUTPUT_DIR / note_name
    output_path.write_text(note_content, encoding="utf-8")

    print(f"\n✅ Done! Created: {note_name}")
    print(f"📁 Location: {OUTPUT_DIR}")

if __name__ == "__main__":
    if len(sys.argv) > 1:
        transcribe(sys.argv[1])
    else:
        audio = input("Enter audio file path: ").strip('"')
        transcribe(audio)