Weekly coding

Code

import os
from faster_whisper import WhisperModel
import requests
import json

# 1. LOCAL TRANSCRIPTION (Whisper)
def transcribe_audio(audio_path):
    print("Transcribing audio locally...")
    model = WhisperModel("base", device="cpu", compute_type="int8")
    segments, info = model.transcribe(audio_path, beam_size=5)

    transcript = " ".join([segment.text for segment in segments])
    return transcript

# 2. LOCAL AI PROCESSING (Ollama + Llama 3)
def structure_recipe(raw_transcript):
    print("Structuring recipe with local Llama 3...")
    url = "http://localhost:11434/api/generate"

    system_prompt = (
        "You are a helpful culinary archivist. Your job is to take a messy, conversational "
        "audio transcript from a grandparent and turn it into a beautifully structured recipe. "
        "Extract the Title, a brief heartwarming description, Ingredients with measurements, "
        "and Step-by-Step Instructions. Do not add any conversational filler in your final response."
    )

    payload = {
        "model": "llama3",
        "prompt": f"{system_prompt}\n\nTranscript: {raw_transcript}",
        "stream": False
    }

    response = requests.post(url, json=payload)
    return response.json()['response']

# 3. RUN THE PIPELINE
if __name__ == "__main__":
    audio_file = "grandpas_spaghetti_story.mp3" 

    if os.path.exists(audio_file):
        raw_text = transcribe_audio(audio_file)
        final_recipe = structure_recipe(raw_text)

        with open("heirloom_recipe.md", "w") as f:
            f.write(final_recipe)
        print("Success! Your structured recipe is ready in heirloom_recipe.md")
    else:
        print(f"Please place an audio file named '{audio_file}' in this directory.")