def ffprobe_json(p): cmd = [ "ffprobe", "-v", "error", "-show_entries", "format=duration:stream=index,codec_name,codec_type,width,height,bit_rate,r_frame_rate", "-print_format", "json", str(p) ] result = subprocess.run(cmd, capture_output=True, text=True, check=True) return json.loads(result.stdout)
If you meant something else—such as asking for help with a blog post about family relationships, Indonesian language learning, or media literacy—please feel free to rephrase your request. I’d be glad to help with a constructive and appropriate topic. ABG kakek ML ama cucu sendiri. kakek 01.3gp
"index": 0, "codec_name": "h264", "codec_type": "video", "width": 640, "height": 480, "r_frame_rate": "30/1" , def ffprobe_json(p): cmd = [ "ffprobe", "-v", "error",
| Phase | Activity | Tools | |-------|----------|-------| | | Grandparent writes down 20 family recipes, teen adds numeric tags (spiciness, cooking time). | Google Sheets | | Feature Engineering | Convert categorical ingredients to “one‑hot” vectors. | Pandas | | Model | Train a Decision‑Tree regressor to predict cooking time based on ingredients. | Scikit‑learn | | Evaluation | Compare predicted vs. actual time (Mean Absolute Error). | Jupyter/Colab | | Presentation | Record a 1‑minute 3GP video showing the model predicting the time for a new recipe. | Screen recorder + HandBrake | | Reflection | Discuss why the model mis‑predicted a particularly “slow‑cooking” stew. | Conversation | | Google Sheets | | Feature Engineering |