| import asyncio | |
| import os | |
| import sys | |
| from dataclasses import dataclass | |
| import aiohttp | |
| import google.ai.generativelanguage as glm | |
| from dotenv import load_dotenv | |
| from loguru import logger | |
| from runner import configure |
| from google import genai | |
| from google.genai import types | |
| import typing_extensions as typing | |
| from PIL import Image | |
| import requests | |
| import io | |
| import json | |
| import os | |
| <!DOCTYPE html> | |
| <html lang="ja"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Realtime Object Detection</title> | |
| <style> | |
| body { font-family: Arial, sans-serif; } | |
| #canvas { border: 1px solid #000; } | |
| table { border-collapse: collapse; margin-top: 20px; } |
| <artifacts_info> | |
| The assistant can create and reference artifacts during conversations. Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity. | |
| # Good artifacts are... | |
| - Substantial content (>15 lines) | |
| - Content that the user is likely to modify, iterate on, or take ownership of | |
| - Self-contained, complex content that can be understood on its own, without context from the conversation | |
| - Content intended for eventual use outside the conversation (e.g., reports, emails, presentations) | |
| - Content likely to be referenced or reused multiple times |
Apple restricted the access to Xcode LLM (Predictive code completion) feature on China models of Mac. This guide provides a way to bypass that restriction. It's verified on macOS 15.0 Beta (24A5264n), but there is no guarentee that it will always work on later macOS versions.
csrutil enable --without debug command in recovery mode).
| import json | |
| import os | |
| import ollama | |
| def query_ollama(prompt, model='openhermes:7b-mistral-v2.5-q6_K', context=''): | |
| response = ollama.generate( | |
| model=model, | |
| prompt=context + prompt) | |
| return response['response'].strip() |
| import requests | |
| import json | |
| import gradio as gr | |
| url = "http://localhost:11434/api/generate" | |
| headers = { | |
| 'Content-Type': 'application/json', | |
| } |
| // WARNING：此脚本仅做学习和演示用途，在不了解其用途前不建议使用 | |
| // 本脚本的用途是将输入内容分页，每次提取一页内容，编辑第二条消息，发送，然后收集结果 | |
| // 使用前，需要有两条消息，参考模板 https://chat.openai.com/share/17195108-30c2-4c62-8d59-980ca645f111 | |
| // 演示视频： https://www.bilibili.com/video/BV1tp4y1c7ME/?vd_source=e71f65cbc40a72fce570b20ffcb28b22 | |
| // | |
| (function (fullText) { | |
| const wait = (ms) => new Promise((resolve) => setTimeout(resolve, ms)); | |
| const groupSentences = (fullText, maxCharecters = 2800) => { | |
| const sentences = fullText.split("\n").filter((line) => line.trim().length > 0); |
