philschmid

Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.
- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.
- Reasoning Before Conclusions: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!
    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.
    - Conclusion, classifications, or results should ALWAYS appear last.
- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.
   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from p

lanzani

import cv2
import numpy as np
import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
from mediapipe.framework.formats import landmark_pb2

# Model available to download here: https://developers.google.com/mediapipe/solutions/vision/pose_landmarker#models
model_path = "pose_landmarker_full.task"

dirkliebich

# Sebastian Raschka 09/24/2022
# Create a new conda environment and packages
#   conda create -n whisper python=3.9
#   conda activate whisper
#   conda install mlxtend -c conda-forge

# Install ffmpeg
# macOS & homebrew
#   brew install ffmpeg
# Ubuntu

VictorTaelin

Notes on this tweet.

• The screenshots were taken on different sessions.

• The entire sessions are included on the screenshots.

• I lost the original prompts, so I had to reconstruct them, and still managed to reproduce.

• The "compressed" version is actually longer! Emojis and abbreviations use more tokens than common words.

wiseman

from io import StringIO
import sys
from typing import Dict, Optional

from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents.tools import Tool
from langchain.llms import OpenAI

rasbt

# Sebastian Raschka 09/24/2022
# Create a new conda environment and packages
#   conda create -n whisper python=3.9
#   conda activate whisper
#   conda install mlxtend -c conda-forge

# Install ffmpeg
# macOS & homebrew
#   brew install ffmpeg
# Ubuntu

schaumb

import streamlit as st
import io
import contextlib
import sys
import re
import threading


class _Redirect:
    class IOStuff(io.StringIO):

Breakthrough

import json

from scenedetect import VideoManager
from scenedetect import SceneManager
from scenedetect.detectors import ContentDetector
from scenedetect.scene_manager import save_images

def get_scenes_json(video_path, threshold=30.0):
    # Create our video & scene managers, then add the detector.
    video_manager = VideoManager([video_path])

ESWZY

# Simplified version and explanation at: https://stackoverflow.com/a/64439347/12866353

import os
import ffmpeg

def compress_video(video_full_path, size_upper_bound, two_pass=True, filename_suffix='cps_'):
    """
    Compress video file to max-supported size.
    :param video_full_path: the video you want to compress.
    :param size_upper_bound: Max video size in KB.

fedarko

# your link goes here
link = "https://github.com/knightlab-analyses/qurro-mackerel-analysis/blob/master/AnalysisOutput/qurro-plot.qzv"

# note: this will break if a repo/organization or subfolder is named "blob" -- would be ideal to use a fancy regex
# to be more precise here
print(link.replace("github.com", "raw.githubusercontent.com").replace("/blob/", "/"))

# example output link:
# https://raw.githubusercontent.com/knightlab-analyses/qurro-mackerel-analysis/master/AnalysisOutput/qurro-plot.qzv