text stringlengths 367 5.14M | id stringlengths 19 24 | metadata dict |
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## Overview
The ChatGPT Arduino Library provides a convenient way to interact with the OpenAI GPT models from Arduino environments, such as ESP32 devices. With this library, you can easily send text and vision queries to the ChatGPT API and receive responses directly in your Arduino projects.
## Features
- **Text Gene... | train_000.parquet/0 | {
"file_path": "README.md",
"repo_id": "0015/ChatGPT_Client_For_Arduino",
"size": 6552,
"token_count": 2187
} |
/*
* Project ChatGPT Client
* Description: The official method using API Key for communication with ChatGPT
* Author: Eric Nam
* Date: 07-17-2024
*/
#ifndef __CHATGPT__
#define __CHATGPT__
template <class T>
class ChatGPT
{
public:
/*
* Description:
* Constructor for the ChatGPT class.
*
* Paramete... | train_000.parquet/1 | {
"file_path": "src/ChatGPT.hpp",
"repo_id": "0015/ChatGPT_Client_For_Arduino",
"size": 10335,
"token_count": 3511
} |
#ifndef BASE64_IMAGE_DATA_H
#define BASE64_IMAGE_DATA_H
// The image is a solid red color.
const char* base64ImageData = "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEASABIAAD//gATQ3JlYXRlZCB3aXRoIEdJTVD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsKCwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFB... | train_000.parquet/2 | {
"file_path": "examples/Arduino_SSLClient/Base64ImageData.h",
"repo_id": "0015/ChatGPT_Client_For_Arduino",
"size": 1076,
"token_count": 695
} |
/*
* Project ChatGPT Client For Arduino (Arduino Giga R1, Arduino Portenta H7)
* Description: For HTTPS connection using Arduino WiFiSSLClient
* Author: Eric Nam
* Date: 07-17-2024
*/
#include <WiFi.h>
#include <WiFiSSLClient.h>
#include <ArduinoJson.h>
#include <ChatGPT.hpp>
#include "Base64ImageData.h"
static ... | train_000.parquet/3 | {
"file_path": "examples/Arduino_SSLClient/Arduino_SSLClient.ino",
"repo_id": "0015/ChatGPT_Client_For_Arduino",
"size": 4453,
"token_count": 1559
} |
#ifndef BASE64_IMAGE_DATA_H
#define BASE64_IMAGE_DATA_H
// The image is a solid red color.
const char* base64ImageData = "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEASABIAAD//gATQ3JlYXRlZCB3aXRoIEdJTVD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsKCwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFB... | train_000.parquet/4 | {
"file_path": "examples/ESP32_WiFiClientSecure/Base64ImageData.h",
"repo_id": "0015/ChatGPT_Client_For_Arduino",
"size": 1076,
"token_count": 695
} |
/*
* Project ChatGPT Client For ESP32
* Description: For HTTPS connection using WiFiClientSecure
* Author: Eric Nam
* Date: 07-17-2024
*/
#include <WiFi.h>
#include <WiFiClientSecure.h>
#include <ArduinoJson.h>
#include <ChatGPT.hpp>
#include "Base64ImageData.h"
static const char *ssid = "<WIFI_SSID>";
static co... | train_000.parquet/5 | {
"file_path": "examples/ESP32_WiFiClientSecure/ESP32_WiFiClientSecure.ino",
"repo_id": "0015/ChatGPT_Client_For_Arduino",
"size": 4002,
"token_count": 1407
} |
#ifndef BASE64_IMAGE_DATA_H
#define BASE64_IMAGE_DATA_H
// The image is a solid red color.
const char* base64ImageData = "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEASABIAAD//gATQ3JlYXRlZCB3aXRoIEdJTVD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsKCwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFB... | train_000.parquet/6 | {
"file_path": "examples/Arduino_BearSSLExample/Base64ImageData.h",
"repo_id": "0015/ChatGPT_Client_For_Arduino",
"size": 1076,
"token_count": 695
} |
/*
* Project ChatGPT Client For Arduino
* Description: For HTTPS connection using ArduinoBearSSL
* Author: Eric Nam
* Date: 07-17-2024
*/
//#define ARDUINO_DISABLE_ECCX08
#include <ArduinoBearSSL.h>
#include <WiFi.h>
#include <ArduinoJson.h>
#include <ChatGPT.hpp>
#include "Base64ImageData.h"
static const char *... | train_000.parquet/7 | {
"file_path": "examples/Arduino_BearSSLExample/Arduino_BearSSLExample.ino",
"repo_id": "0015/ChatGPT_Client_For_Arduino",
"size": 4223,
"token_count": 1513
} |
<div align="center">
<img src="https://i.imgur.com/Y2XXof7.jpeg" width="900">
<br>
___
<br>
<img src="https://img.shields.io/github/license/oppsec/blobber?color=blue&logo=github&style=for-the-badge">
<img src="https://img.shields.io/github/issues/oppsec/blobber?color=blue&logo=github&style=for-the-badge">
<img src=... | train_000.parquet/8 | {
"file_path": "README.md",
"repo_id": "000pp/blobber",
"size": 1421,
"token_count": 594
} |
from rich.console import Console
console = Console()
import argparse
from blobber.ui.banner import get_banner
from blobber.modules.status import request
from blobber.modules.blobs import blob_handler
def initialize() -> None:
""" Initiates Blobber """
get_banner()
parser = argparse.ArgumentParser(
... | train_000.parquet/9 | {
"file_path": "src/blobber/main.py",
"repo_id": "000pp/blobber",
"size": 1071,
"token_count": 361
} |
"""
This module aims to enumerate and get Name, Last-Modified, Content-Length and Content-Typpe blob value.
"""
from lxml import etree
from rich.console import Console
console = Console()
def blob_handler(content: str, file_filter: str) -> None:
try:
content_bytes = content.encode('utf-8')
root =... | train_000.parquet/10 | {
"file_path": "src/blobber/modules/blobs.py",
"repo_id": "000pp/blobber",
"size": 1809,
"token_count": 594
} |
"""
This module checks the status from the Azure Container passed
"""
from rich.console import Console
console = Console()
from requests import get
from urllib3 import disable_warnings
disable_warnings()
from blobber.modules.errors import error_handler
def request(url: str) -> tuple:
"""
Perform a GET reques... | train_000.parquet/11 | {
"file_path": "src/blobber/modules/status.py",
"repo_id": "000pp/blobber",
"size": 1092,
"token_count": 333
} |
"""
This module handle the common errors when interacting with Azure Blobs
"""
from rich.console import Console
console = Console()
from requests import get
from urllib3 import disable_warnings
disable_warnings()
errors_list = {
"InvalidQueryParameterValue": "Value for one of the query parameters specified in the... | train_000.parquet/12 | {
"file_path": "src/blobber/modules/errors.py",
"repo_id": "000pp/blobber",
"size": 2492,
"token_count": 708
} |
[](https://www.ardu-badge.com/OV5640%20Auto%20Focus%20for%20ESP32%20Camera)
# Enable OV5640's autofocus function on ESP32 AI-THINKER Board
This library contains the necessary firmware and source code to enable t... | train_000.parquet/13 | {
"file_path": "README.md",
"repo_id": "0015/ESP32-OV5640-AF",
"size": 2676,
"token_count": 1007
} |
/*
ESP32_OV5640_cfg.h - Library for OV5640 Auto Focus (ESP32 Camera)
Created by Eric Nam, December 08, 2021.
Released into the public domain.
*/
#define OV5640_CHIPID_HIGH 0x300a
#define OV5640_CHIPID_LOW 0x300b
#define OV5640_CMD_MAIN 0x3022
#define OV5640_CMD_A... | train_000.parquet/14 | {
"file_path": "src/ESP32_OV5640_cfg.h",
"repo_id": "0015/ESP32-OV5640-AF",
"size": 28448,
"token_count": 26247
} |
/*
ESP32_OV5640_AF.cpp - Library for OV5640 Auto Focus (ESP32 Camera)
Created by Eric Nam, December 08, 2021.
Released into the public domain.
*/
#include "ESP32_OV5640_AF.h"
OV5640::OV5640() {
isOV5640 = false;
}
bool OV5640::start(sensor_t* _sensor) {
sensor = _sensor;
uint8_t vid, pid;
vid = sensor-... | train_000.parquet/15 | {
"file_path": "src/ESP32_OV5640_AF.cpp",
"repo_id": "0015/ESP32-OV5640-AF",
"size": 2413,
"token_count": 1352
} |
#include "esp_camera.h"
#include "ESP32_OV5640_AF.h"
// ESP32 AI-THINKER Board
#define PWDN_GPIO_NUM 32
#define RESET_GPIO_NUM -1
#define XCLK_GPIO_NUM 0
#define SIOD_GPIO_NUM 26
#define SIOC_GPIO_NUM 27
#define Y9_GPIO_NUM 35
#define Y8_GPIO_NUM 34
#define Y7_GPIO_NUM 39
#define Y6_GPIO_NUM 36
#define Y5_GPIO_NUM 21... | train_000.parquet/16 | {
"file_path": "examples/OV5640_Console_Test/OV5640_Console_Test.ino",
"repo_id": "0015/ESP32-OV5640-AF",
"size": 2478,
"token_count": 1217
} |
# 📲 Apepe
> 📲 Enumerate information from an app based on the APK file
<div align="center">
<img src="https://i.imgur.com/0qh6sHq.jpg" width="850">
</div>
<br>
<p align="center">
<img src="https://img.shields.io/github/license/oppsec/Apepe?color=orange&logo=github&logoColor=orange&style=for-the-badge">
<im... | train_000.parquet/17 | {
"file_path": "README.md",
"repo_id": "000pp/Apepe",
"size": 1984,
"token_count": 731
} |
from rich.console import Console
console = Console()
from os import path, chdir
from pathlib import Path
from zipfile import ZipFile, BadZipFile
from shutil import rmtree
from androguard.core.apk import APK
from androguard.core.dex import DEX
from src.apepe.modules.suggest import suggest_sslpinning
from src.apepe.mod... | train_000.parquet/18 | {
"file_path": "src/apepe/main.py",
"repo_id": "000pp/Apepe",
"size": 5812,
"token_count": 1941
} |
import json
import re
from rich.console import Console
console = Console()
from pathlib import Path
from androguard.core.dex import DEX
from androguard.core.apk import APK
from lxml.etree import tostring
DEEPLINK_PATTERN = r'\b\w+://[^\s]+'
def dex_handler(file_path: Path) -> list:
"""
Extract readable st... | train_000.parquet/19 | {
"file_path": "src/apepe/modules/deeplink.py",
"repo_id": "000pp/Apepe",
"size": 4821,
"token_count": 1581
} |
from rich.console import Console
console = Console()
def exported(target) -> None:
"""
Lists activities, services, receivers, and providers from AndroidManifest.xml file.
"""
manifest = target.get_android_manifest_xml()
endpoints = ["activity", "service", "receiver", "provider"]
for endpoint ... | train_000.parquet/20 | {
"file_path": "src/apepe/modules/exported.py",
"repo_id": "000pp/Apepe",
"size": 1131,
"token_count": 367
} |
# 😹 Tomcter
> Stealing credentials from a yellow cat
<div align="center">
<img src="https://i.imgur.com/ePw5RQ5.png">
</div>
<br>
<p align="center">
<img src="https://img.shields.io/github/license/oppsec/tomcter?color=yellow&logo=github&logoColor=yellow&style=for-the-badge">
<img src="https://img.shield... | train_000.parquet/21 | {
"file_path": "README.md",
"repo_id": "000pp/tomcter",
"size": 2320,
"token_count": 874
} |
from random import choice
def get_file_data(path) -> str:
" Return the content inside text file "
with open(path) as file:
return file.read()
def get_usernames() -> list:
" Get all usernames inside usernames.txt file "
raw = get_file_data('src/core/data/usernames.txt')
raw = raw.split('... | train_000.parquet/22 | {
"file_path": "src/core/manager.py",
"repo_id": "000pp/tomcter",
"size": 1031,
"token_count": 368
} |
from requests import get
from base64 import b64encode
from time import sleep
from rich.console import Console
console = Console()
from urllib3 import disable_warnings
disable_warnings()
from src.core.manager import props, get_usernames, get_passwords
def check_and_connect(target):
url: str = f"{target}/manager/... | train_000.parquet/23 | {
"file_path": "src/core/bruteforce.py",
"repo_id": "000pp/tomcter",
"size": 2234,
"token_count": 722
} |
# 🎉 0.1.6 - 02/05/2024
- Added check to config and backup files scanner if result is not HTML response
- Changed print to console.print from rich
- Changed interface
- Improved the code
<br><br>
# 🎉 0.1.5 - 08/07/2023
- Improved the code
- Fixed error when trying to get Joomla version on get_version_second() func
-... | train_000.parquet/24 | {
"file_path": "CHANGELOG.md",
"repo_id": "000pp/juumla",
"size": 1350,
"token_count": 481
} |
# 🦁 Juumla
<div align="center">
<img src="https://i.imgur.com/0RvLKOP.png" width="900">
</div>
<br>
<p align="center">
<img src="https://img.shields.io/github/license/oppsec/juumla?color=yellow&logo=github&style=for-the-badge">
<img src="https://img.shields.io/github/issues/oppsec/juumla?color=yellow&log... | train_000.parquet/25 | {
"file_path": "README.md",
"repo_id": "000pp/juumla",
"size": 2752,
"token_count": 1026
} |
from requests import get, exceptions
from rich.console import Console
console = Console()
from urllib3 import disable_warnings
disable_warnings()
from src.juumla.settings import props
from src.juumla.modules.version import get_version
def perform_checks(args) -> None:
" Connect to the target and check if statu... | train_000.parquet/26 | {
"file_path": "src/juumla/main.py",
"repo_id": "000pp/juumla",
"size": 1339,
"token_count": 415
} |
from requests import get, exceptions
from xmltodict import parse, expat
from rich.console import Console
console = Console()
from urllib3 import disable_warnings
disable_warnings()
from src.juumla.settings import props
from src.juumla.modules.vulns import vuln_manager
app_xml_header = "application/xml"
text_xml_hea... | train_000.parquet/27 | {
"file_path": "src/juumla/modules/version.py",
"repo_id": "000pp/juumla",
"size": 2160,
"token_count": 698
} |
from requests import get, exceptions
from rich.console import Console
console = Console()
from src.juumla.settings import props
def file_scan(file_to_check: str, file_url: str) -> None:
try:
response = get(f"{file_url}/{file_to_check}", **props)
if response.ok and 'text/html' not in response.head... | train_000.parquet/28 | {
"file_path": "src/juumla/modules/files.py",
"repo_id": "000pp/juumla",
"size": 4744,
"token_count": 2159
} |
package main;
import java.util.Base64;
import javax.crypto.BadPaddingException;
import javax.crypto.Cipher;
import javax.crypto.IllegalBlockSizeException;
import javax.crypto.NoSuchPaddingException;
import javax.crypto.spec.SecretKeySpec;
import java.io.File;
import java.io.IOException;
import java.nio.file.Files;
i... | train_000.parquet/29 | {
"file_path": "Decrypter/src/main/Decrypter.java",
"repo_id": "000JustMe/PewCrypt",
"size": 4434,
"token_count": 2354
} |
package main;
import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.net.URL;
import java.net.URLConnection;
public class Scraper {
public Scraper() {
}
public int getPewdiepieCount() {
try {
return this.parseJson(this.getJson("https://www.googleapis.com/youtube/v3/channels?part=s... | train_000.parquet/30 | {
"file_path": "Encryptor/src/main/Scraper.java",
"repo_id": "000JustMe/PewCrypt",
"size": 1533,
"token_count": 618
} |
package main;
import java.io.File;
import java.util.ArrayList;
public class FileItter {
/*
* Class constructor will iterate though target paths Function itterFiles
* recursively walks directory finding files Function getPaths to access paths
* found and encrypt-able
*/
private ArrayList<String> paths;
pr... | train_000.parquet/31 | {
"file_path": "Encryptor/src/main/FileItter.java",
"repo_id": "000JustMe/PewCrypt",
"size": 1300,
"token_count": 539
} |
package main;
import java.io.File;
import java.io.IOException;
import java.nio.file.Files;
import java.security.InvalidKeyException;
import java.security.Key;
import java.security.KeyFactory;
import java.security.NoSuchAlgorithmException;
import java.security.PublicKey;
import java.security.SecureRandom;
import java.... | train_000.parquet/32 | {
"file_path": "Encryptor/src/main/Crypto.java",
"repo_id": "000JustMe/PewCrypt",
"size": 5370,
"token_count": 2992
} |
# XUnity.AutoTranslator-deepseek
本项目通过调用腾讯的DeepSeek V3 API，实现Unity游戏中日文文本的自动翻译。
## 准备工作
### 1. 获取API密钥
- 访问[腾讯云API控制台](https://console.cloud.tencent.com/lkeap/api)申请DeepSeek的API密钥（限时免费）。
- 也可以使用其他平台提供的DeepSeek API。
### 2. 安装依赖
确保已安装以下软件和库：
- **XUnity.AutoTranslator**
- **Python 3.x**
安装必要的Python库：
```bash
pip inst... | train_000.parquet/33 | {
"file_path": "README.md",
"repo_id": "0001lizhubo/XUnity.AutoTranslator-deepseek",
"size": 1147,
"token_count": 661
} |
import os
import re
import json
import time
from flask import Flask, request # 导入 Flask 库，用于创建 Web 应用，需要安装：pip install Flask
from gevent.pywsgi import WSGIServer # 导入 gevent 的 WSGIServer，用于提供高性能的异步服务器，需要安装：pip install gevent
from urllib.parse import unquote # 导入 unquote 函数，用于 URL 解码
from threading import Thread # 导... | train_000.parquet/34 | {
"file_path": "deepseekv3.py",
"repo_id": "0001lizhubo/XUnity.AutoTranslator-deepseek",
"size": 14868,
"token_count": 7189
} |
# 1.7 - 10/05/2025
- Removed "banner.txt" file
- Removed ASCII art from banner
- Changed color and print scheme
- Changed "rich.print" to "rich.console.print"
- Improved the code
- Add -H option, now you can specify custom headers, for example:
```
python3 main.py -u https://webhook.site/793d267f-c86a-48e6-94e6... | train_000.parquet/35 | {
"file_path": "CHANGELOG.md",
"repo_id": "000pp/Pinkerton",
"size": 3190,
"token_count": 1245
} |
package main;
import java.awt.EventQueue;
import javax.swing.JFrame;
import javax.swing.JTextField;
import java.awt.Color;
import java.awt.Desktop;
import javax.swing.SwingConstants;
import javax.swing.JTextArea;
import java.awt.Font;
import javax.swing.JProgressBar;
import javax.imageio.ImageIO;
import javax.swi... | train_000.parquet/36 | {
"file_path": "Encryptor/src/main/UI.java",
"repo_id": "000JustMe/PewCrypt",
"size": 67303,
"token_count": 51059
} |
# 🕵️ Pinkerton
> Investigating JavaScript files since 1850
<div align="center">
<img src="https://i.imgur.com/qQXb2ha.png" width=700>
</div>
<br>
<p align="center">
<img src="https://img.shields.io/github/license/000pp/Pinkerton?color=yellow&logo=github&style=for-the-badge">
<img src="https://img.shield... | train_000.parquet/37 | {
"file_path": "README.md",
"repo_id": "000pp/Pinkerton",
"size": 2175,
"token_count": 824
} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AI截图分析 - 高级截图覆盖层
提供流畅的截图体验，包括区域选择、确认/取消等功能
"""
import logging
from PIL import ImageGrab, Image
from PyQt6.QtCore import Qt, QRect, QPoint, pyqtSignal
from PyQt6.QtGui import (
QPainter, QPen, QColor, QFont,
QFontMetrics, QKeySequence, QShortcut
)
from PyQt6.Q... | train_000.parquet/38 | {
"file_path": "screenshot_overlay.py",
"repo_id": "00000O00000/ask-ai-screenshot",
"size": 17898,
"token_count": 6511
} |
import re
from requests import get, exceptions
from urllib3 import disable_warnings
from rich.console import Console
console = Console()
from src.pinkerton.settings import get_user_agent
from src.pinkerton.modules.secret import scan
disable_warnings()
def perform_checks(args) -> None:
" Check if target is acces... | train_000.parquet/39 | {
"file_path": "src/pinkerton/main.py",
"repo_id": "000pp/Pinkerton",
"size": 2175,
"token_count": 728
} |
from rich.console import Console
console = Console()
from requests import get
from jsbeautifier import beautify
from re import findall
regex_list = {
'Google API': r'AIza[0-9A-Za-z-_]{35}',
"Artifactory API Token": r'(?:\s|=|:|"|^)AKC[a-zA-Z0-9]{10,}',
"Artifactory Password": r'(?:\s|=|:|"|^)AP[\dABCDEF][a... | train_000.parquet/40 | {
"file_path": "src/pinkerton/modules/secret.py",
"repo_id": "000pp/Pinkerton",
"size": 6029,
"token_count": 3257
} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AI截图分析工具模块
包含配置管理、日志管理、错误处理等功能
"""
import os
import logging
import time
import glob
from datetime import datetime
from typing import Dict, Any, Optional
from PyQt6.QtWidgets import QMessageBox
from PyQt6.QtCore import QObject, pyqtSignal
try:
import tomllib
i... | train_000.parquet/41 | {
"file_path": "util.py",
"repo_id": "00000O00000/ask-ai-screenshot",
"size": 18578,
"token_count": 7133
} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AI截图分析主程序
作者: AI Assistant
版本: 1.0.0-simplified
"""
import sys
import logging
import threading
import time
from PyQt6.QtWidgets import QApplication
from main_window import MainWindow
from util import ConfigManager, LogManager, ErrorHandler
from core import ScreenshotM... | train_000.parquet/42 | {
"file_path": "main.py",
"repo_id": "00000O00000/ask-ai-screenshot",
"size": 4371,
"token_count": 1448
} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AI截图分析主窗口界面
只包含主页、配置文件选择、关于页
"""
import os
import logging
from PyQt6.QtWidgets import (
QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, QTabWidget,
QLabel, QPushButton, QTextEdit, QLineEdit, QGroupBox,
QFileDialog, QComboBox, QFormLayout
)
from PyQt6.QtCo... | train_000.parquet/43 | {
"file_path": "main_window.py",
"repo_id": "00000O00000/ask-ai-screenshot",
"size": 32806,
"token_count": 11705
} |
"""内置图标模块
包含应用程序图标的base64编码数据
"""
import base64
import io
from PIL import Image
# 图标的base64编码数据
ICON_BASE64 = """AAABAAEAAAAAAAEAIAAoIAQAFgAAACgAAAAAAQAAAAIAAAEAIAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA... | train_000.parquet/44 | {
"file_path": "icon_data.py",
"repo_id": "00000O00000/ask-ai-screenshot",
"size": 360981,
"token_count": 123840
} |
# AI截图分析 v2.1
一个方便的AI截图分析工具，快速使用AI解释你在屏幕上看到的东西，或让他帮你解题。
exe网盘链接：[https://o-zwz-o.lanzouq.com/b002v8ande](https://o-zwz-o.lanzouq.com/b002v8ande) 密码：52pj
Github 项目地址：[https://github.com/00000O00000/ask-ai-screenshot](https://github.com/00000O00000/ask-ai-screenshot)
软件目前处于测试版，可能存在Bug，若有问题，欢迎前往 Github 提交 issue。
本软件... | train_000.parquet/45 | {
"file_path": "README.md",
"repo_id": "00000O00000/ask-ai-screenshot",
"size": 2128,
"token_count": 1368
} |
# ai.py
# pip install requests flask flask-cors oss2
# 使用Qwen3 Coder写的chat.qwen.ai网页API逆向，AI的刀先插在自己身上了说是。
# 扒源码的哥们，网页端token数上传限制为96000，我没有对大于这个token的请求做错误处理，别被坑了。
import requests
import uuid
import time
import json
import os
import warnings
import base64
import hashlib
import random
from urllib.parse import urlparse
... | train_000.parquet/46 | {
"file_path": "ai.py",
"repo_id": "00000O00000/ask-ai-screenshot",
"size": 28707,
"token_count": 9680
} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AI截图分析 - 自定义窗口组件
包含自定义消息框、通知窗口、Markdown渲染器等
"""
import re
import logging
from typing import Optional
from PyQt6.QtWidgets import (
QDialog, QVBoxLayout, QHBoxLayout, QLabel, QPushButton,
QTextEdit, QWidget, QFrame, QScrollArea, QApplication
)
from PyQt6.QtCor... | train_000.parquet/47 | {
"file_path": "custom_window.py",
"repo_id": "00000O00000/ask-ai-screenshot",
"size": 29524,
"token_count": 9847
} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AI截图分析核心功能模块
包含截图、OCR、AI客户端等核心功能
"""
import os
import io
import base64
import json
import logging
import smtplib
import time
import hashlib
import hmac
from datetime import datetime
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
fr... | train_000.parquet/48 | {
"file_path": "core.py",
"repo_id": "00000O00000/ask-ai-screenshot",
"size": 22366,
"token_count": 7178
} |
Data visualization
==================
This is a collection of tools to represent and navigate through high-dimensional datasets.
* The algorithm t-SNE is set as default to construct the 2D space.
* The module should be agnostic of the data provided.
* It ships with MNIST for quick testing.
For commercial use, and ... | train_000.parquet/49 | {
"file_path": "README.md",
"repo_id": "0011001011/vizuka",
"size": 7558,
"token_count": 2188
} |
from setuptools import setup, find_packages
import os
data = {
'vizuka': [
'data/set/.gitkeep',
'data/models/.gitkeep',
'data/cache/.gitkeep',
'data/saved_clusters/.gitkeep',
'data/reduced/.gitkeep',
'example/tsne#MNIST_example#learning_rate::1000#n_iter::12000#perpl... | train_000.parquet/50 | {
"file_path": "setup.py",
"repo_id": "0011001011/vizuka",
"size": 1059,
"token_count": 387
} |
"""
This is the main script to launch everything
Is able to reduce the data and launch a vizualization
from it.
"""
import logging
import os
import argparse
import matplotlib
matplotlib.use('Qt5Agg') # noqa
import numpy as np
from pyfiglet import Figlet
from vizuka import data_loader
from vizuka import vizualizatio... | train_000.parquet/51 | {
"file_path": "vizuka/launch_viz.py",
"repo_id": "0011001011/vizuka",
"size": 11475,
"token_count": 3755
} |
import logging
import os
import itertools
import numpy as np
from vizuka import dimension_reduction
from vizuka.dimension_reduction.projector import Projector
from vizuka.config import (
MODEL_PATH,
VERSION,
DEFAULT_PREDICTOR,
PROJECTION_DEFAULT_PARAMS,
REDUCED_DATA_PATH,
... | train_000.parquet/52 | {
"file_path": "vizuka/data_loader.py",
"repo_id": "0011001011/vizuka",
"size": 3904,
"token_count": 1359
} |
"""
Module built around the class Vizualization
It is the main module that draws a nice IHM interface to explore your data
See class Vizualization
"""
import sys
import os
import logging
from collections import Counter
import pickle
import csv
from scipy import stats
import numpy as np
import matplotlib
matplotlib.us... | train_000.parquet/53 | {
"file_path": "vizuka/vizualization.py",
"repo_id": "0011001011/vizuka",
"size": 47857,
"token_count": 14235
} |
import argparse
import logging
from pyfiglet import Figlet
from vizuka import dimension_reduction
from vizuka import data_loader
from vizuka import config
from vizuka.config import (
VERSION,
INPUT_FILE_BASE_NAME,
DATA_PATH,
BASE_PATH,
PROJECTION_DEFAULT_PARAMS,
)
logger = logging.getLogger()
log... | train_000.parquet/54 | {
"file_path": "vizuka/launch_reduce.py",
"repo_id": "0011001011/vizuka",
"size": 4952,
"token_count": 1540
} |
"""
Here are the default parameters used in all the package
There are 1.path 2.filenames 3.learning parameters
"""
import os
#
# ALL DEFAULT PATH
#
def path_builder(base_path):
folders = [
os.path.join(base_path, relative) for relative in [
'set/',
'reduced/',
'models/... | train_000.parquet/55 | {
"file_path": "vizuka/config.py",
"repo_id": "0011001011/vizuka",
"size": 1512,
"token_count": 592
} |
import os
import pkgutil
import inspect
import importlib
import pickle
from vizuka.heatmap import heatmap
from vizuka.heatmap import accuracy
from vizuka.heatmap import entropy
from vizuka.plugins import heatmap as heatmap_plugins
def list_heatmap():
"""
List all the available heatmaps
First it looks in... | train_000.parquet/56 | {
"file_path": "vizuka/heatmap/__init__.py",
"repo_id": "0011001011/vizuka",
"size": 1622,
"token_count": 497
} |
import numpy as np
import logging
from vizuka.helpers.ml_helper import cross_entropy
from vizuka.heatmap import heatmap
class EntropyHeatmap(heatmap.Heatmap):
def __init__(self, vizualization):
self.update_colors(vizualization)
def get_all_colors(self):
"""
Returns all colors in a d... | train_000.parquet/57 | {
"file_path": "vizuka/heatmap/entropy.py",
"repo_id": "0011001011/vizuka",
"size": 3412,
"token_count": 1018
} |
"""
This module provides heatmaps to be drawn on the side of the main map.
Basically it takes a vizualization object and returns an array with
associated colors.
If you want to provide new ones dont forget to register them in qt_handler
"""
import logging
from vizuka.heatmap import heatmap
class AccuracyHeatmap(he... | train_000.parquet/58 | {
"file_path": "vizuka/heatmap/accuracy.py",
"repo_id": "0011001011/vizuka",
"size": 2896,
"token_count": 839
} |
import os
import pkgutil
import inspect
import importlib
import pickle
import logging
from vizuka.clustering import (
kMeans,
dbscan,
grid,
clusterizer,
)
from vizuka.plugins import clustering as clustering_plugins
def get_required_arguments(method_name):
if method_name=='... | train_000.parquet/59 | {
"file_path": "vizuka/clustering/__init__.py",
"repo_id": "0011001011/vizuka",
"size": 2135,
"token_count": 640
} |
from scipy.spatial import cKDTree
from vizuka.clustering.clusterizer import Clusterizer
class DummyClusterizer(Clusterizer):
"""
The DummyClusterizer is a clustering engine which
return the index of your point in a big mesh.
Give it the resolution of your mesh and its amplitude,
it will center it... | train_000.parquet/60 | {
"file_path": "vizuka/clustering/grid.py",
"repo_id": "0011001011/vizuka",
"size": 1175,
"token_count": 347
} |
'''
Clustering engine to use with Vizualization
If you want to implement one, dont forget to add it
on qt_handler, to be able to select on the IHM
Also, adds it in make_clusterizer obviously
3 methods are necessary to implement, cf Clusterizer():
init - set main params
fit - to prepare the algo for the... | train_000.parquet/61 | {
"file_path": "vizuka/clustering/clusterizer.py",
"repo_id": "0011001011/vizuka",
"size": 4943,
"token_count": 1387
} |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.cluster import DBSCAN
from scipy.spatial import cKDTree
import logging
from vizuka.clustering.clusterizer import Clusterizer
class DBSCANClusterizer(Clusterizer):
def __init__(self, epsilon=1.5, min_samples=30):
"""
Inits a DBS... | train_000.parquet/62 | {
"file_path": "vizuka/clustering/dbscan.py",
"repo_id": "0011001011/vizuka",
"size": 3430,
"token_count": 1057
} |
"""
Qt handler for drawing buttons and IHM black magic.
Please do read it you fool. I must admit I am not
proud of everything written down there.
This should be rewritten with QtCreator's help.
"""
import sys
import logging
import os
import matplotlib
matplotlib.use('Qt5Agg') # noqa
from matplotlib.backends.backend_... | train_000.parquet/63 | {
"file_path": "vizuka/graphics/qt_handler.py",
"repo_id": "0011001011/vizuka",
"size": 22929,
"token_count": 7121
} |
import matplotlib
matplotlib.use('Qt5Agg') # noqa
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
from PyQt5 import QtCore, QtWidgets
from PyQt5.QtWidgets import (
QApplication,
QMainWindo... | train_000.parquet/64 | {
"file_path": "vizuka/graphics/qt_helpers.py",
"repo_id": "0011001011/vizuka",
"size": 6593,
"token_count": 2228
} |
"""
Functions to draw things on some axes
Probably uninteresting
"""
from matplotlib import pyplot as plt
import matplotlib
import numpy as np
import logging
def draw_scatterplot_from_indexes(
indexes_to_display,
indexes_bad_predictions,
indexes_good_predictions,
indexes_special_predi... | train_000.parquet/65 | {
"file_path": "vizuka/graphics/drawing.py",
"repo_id": "0011001011/vizuka",
"size": 10684,
"token_count": 3301
} |
import pkgutil
import inspect
import importlib
from vizuka.dimension_reduction import (
projector,
pca,
tsne,
)
from vizuka.plugins import dimension_reduction as projector_plugins
def list_projectors():
"""
List all projector available (in vizuka/plugin/dimension_reduction
... | train_000.parquet/66 | {
"file_path": "vizuka/dimension_reduction/__init__.py",
"repo_id": "0011001011/vizuka",
"size": 1408,
"token_count": 451
} |
"""
Here is the code summoned to reduce the dimension of your
precious data, and also to load it.
We use t-SNE and, if you want, PCA just before it.
..note:: tSNE from sklearn is not the best but is standard
I suggest you to uncomment 'from MulticoreTSNE import TSNE as tsne'
as it will be much faster and won't crash i... | train_000.parquet/67 | {
"file_path": "vizuka/dimension_reduction/pca.py",
"repo_id": "0011001011/vizuka",
"size": 2845,
"token_count": 824
} |
import os
import logging
import numpy as np
from vizuka.config import (
VERSION,
REDUCED_DATA_PATH,
NAME_VALUE_SEPARATOR,
PARAMETERS_SEPARATOR,
)
class Projector():
def __init__(self):
"""
Instanciate a Projector engine, you should give it
... | train_000.parquet/68 | {
"file_path": "vizuka/dimension_reduction/projector.py",
"repo_id": "0011001011/vizuka",
"size": 3131,
"token_count": 867
} |
import numpy as np
import logging
try:
from MulticoreTSNE import MulticoreTSNE as tsne_algorithm
using_multicore_tsne = True
multicore_parameters = {'n_jobs':3}
logging.info('dimension_reduction = Using fast tSNE')
except:
from sklearn.manifold import TSNE as tsne_algorithm
using_multicore_tsn... | train_000.parquet/69 | {
"file_path": "vizuka/dimension_reduction/tsne.py",
"repo_id": "0011001011/vizuka",
"size": 1373,
"token_count": 404
} |
import os
import pkgutil
import inspect
import importlib
import pickle
from vizuka.frontier import (
similarity,
deterministic,
bhattacharyya,
)
from vizuka.plugins import frontier as frontier_plugins
def list_frontiers():
"""
List all frontiers available (in vizuka/plugin/fr... | train_000.parquet/70 | {
"file_path": "vizuka/frontier/__init__.py",
"repo_id": "0011001011/vizuka",
"size": 1446,
"token_count": 443
} |
import importlib
import pkgutil
import inspect
from vizuka.plugins import cluster_viewer as plotter_plugins
from vizuka.cluster_viewer import (
plotter,
image,
density,
counter,
#wordcloud,
)
def list_plotter():
built_in_plotters = {
'images': image.RandomImages,
'density': de... | train_000.parquet/71 | {
"file_path": "vizuka/cluster_viewer/__init__.py",
"repo_id": "0011001011/vizuka",
"size": 1375,
"token_count": 435
} |
import random
import matplotlib
matplotlib.use('Qt5Agg') # noqa
from matplotlib import pyplot as plt
from matplotlib import gridspec
from vizuka.cluster_viewer.plotter import Plotter
class RandomImages(Plotter):
@classmethod
def get_help(self):
h = "Use it to display 25 randomly selected images"
... | train_000.parquet/72 | {
"file_path": "vizuka/cluster_viewer/image.py",
"repo_id": "0011001011/vizuka",
"size": 1240,
"token_count": 374
} |
import numpy as np
import matplotlib
matplotlib.use('Qt5Agg') # noqa
from matplotlib import pyplot as plt
from vizuka.cluster_viewer.plotter import Plotter
class Density(Plotter):
@classmethod
def get_help(self):
h = "Use it to display the distribution of *numerical* values"
return h
de... | train_000.parquet/73 | {
"file_path": "vizuka/cluster_viewer/density.py",
"repo_id": "0011001011/vizuka",
"size": 2058,
"token_count": 660
} |
from collections import Counter as pyCounter
import numpy as np
import matplotlib
matplotlib.use('Qt5Agg') # noqa
from matplotlib import pyplot as plt
from vizuka.cluster_viewer.plotter import Plotter
class Counter(Plotter):
@classmethod
def get_help(self):
h = "Use it to display a counter, for *ca... | train_000.parquet/74 | {
"file_path": "vizuka/cluster_viewer/counter.py",
"repo_id": "0011001011/vizuka",
"size": 1199,
"token_count": 391
} |
import os
import shutil
import sklearn.datasets as dt
import sklearn.preprocessing as pr
from sklearn.linear_model import LogisticRegression
import numpy as np
from vizuka.config import (
DATA_PATH,
MODEL_PATH,
RAW_NAME,
INPUT_FILE_BASE_NAME,
REDUCED_DATA_PATH,
)
pred... | train_000.parquet/75 | {
"file_path": "vizuka/example/load_mnist.py",
"repo_id": "0011001011/vizuka",
"size": 1540,
"token_count": 576
} |
"""
Cool functions for ML
"""
import math
import numpy as np
def entropy(my_dic):
"""
StraightForward entropy calculation
:param my_dict: dict of occurence of different classes
:return: discrete entropy calculation
"""
effectif_total = sum(my_dic.values())
s = 0
for effectif in my_dic.... | train_000.parquet/76 | {
"file_path": "vizuka/helpers/ml_helper.py",
"repo_id": "0011001011/vizuka",
"size": 1271,
"token_count": 380
} |
"""
Some uninteresting functions you need for the vizualization objects
"""
import math
import logging
import matplotlib
import numpy as np
def remove_pathCollection(ax):
for child in ax.get_children():
if isinstance(child, matplotlib.collections.PathCollection):
child.remove()
def find_gri... | train_000.parquet/77 | {
"file_path": "vizuka/helpers/viz_helper.py",
"repo_id": "0011001011/vizuka",
"size": 7309,
"token_count": 2273
} |
##################################################################
#
# HOW TO ADD A CUSTOM HEATMAP ?
###############################
#
#
# This one plugin is a headache to implements : actually you will need
# to request some variable from the Vizualization object (good luck!)
#
# You can define here a heatmap, its nam... | train_000.parquet/78 | {
"file_path": "vizuka/plugins/heatmap/How_to_add_heatmap.py",
"repo_id": "0011001011/vizuka",
"size": 3914,
"token_count": 1190
} |
###########################################################################################
#
# How to add a clustering engine ?
#
##################################
#
# Simply creates a class in a new module that
# implements vizuka.clustering.clusterizer.Clusterizer
#
# Below is a simple example, with kmeans.
# If yo... | train_000.parquet/79 | {
"file_path": "vizuka/plugins/clustering/How_to_add_a_clustering_engine.py",
"repo_id": "0011001011/vizuka",
"size": 1679,
"token_count": 520
} |
#############################################
#
# How to add a dimension reduction algorithm ?
#
##################
#
# Below is a working example. You should simply inherits
# vizuka.dimension_reduction.projector.Projector and implements
# a contructor and a project method.
#
# Typically in the __init__ you instancia... | train_000.parquet/80 | {
"file_path": "vizuka/plugins/dimension_reduction/How_to_add_dimension_reduction_plugin.py",
"repo_id": "0011001011/vizuka",
"size": 1859,
"token_count": 525
} |
# HOW TO ADD A CLUSTER VIZUALIZATION ?
######################################
#
# If you want to add another tool toi visualize the inner observations inside a selected cluster
# you can add here an new tool.
#
# You should inherits vizuka.cluster_viewer.plotter.Plotter, and define a callable,
# cf the simple the exa... | train_000.parquet/81 | {
"file_path": "vizuka/plugins/cluster_viewer/How_to_add_cluster_vizualization.py",
"repo_id": "0011001011/vizuka",
"size": 2007,
"token_count": 675
} |
#!/usr/bin/env python3
"""
数据库修复脚本 - 添加缺失的总结字段
"""
import sqlite3
import os
import sys
DB_PATH = "/mnt/h/NEW-DS2/NEW-DS/ai-mistake-manager/src-tauri/app_data/ai-mistake-manager/mistakes.db"
def check_db_exists():
if not os.path.exists(DB_PATH):
print(f"❌ 数据库文件不存在: {DB_PATH}")
return False
prin... | train_000.parquet/82 | {
"file_path": "fix_database_schema.py",
"repo_id": "000haoji/deep-student",
"size": 4582,
"token_count": 1756
} |
import { useState, useEffect, useCallback } from 'react';
import { MistakeLibrary } from './components/MistakeLibrary';
import { Settings } from './components/Settings';
import { BatchAnalysis } from './components/BatchAnalysis';
import { ReviewAnalysis } from './components/ReviewAnalysis';
import { Dashboard } from '.... | train_000.parquet/83 | {
"file_path": "temp_app_before_cleanup.tsx",
"repo_id": "000haoji/deep-student",
"size": 57191,
"token_count": 18256
} |
# CogniGraph 知识图谱模块
## 概述
CogniGraph 是集成到 AI 错题管理器中的个性化知识网络系统，基于 Neo4j 图数据库实现。该模块允许用户将学习过程中的问题、灵感和解题方法结构化存储，并通过 AI 实现智能检索、关联推荐和深度洞察。
## 核心功能
### 🎯 主要特性
1. **问题卡片管理**
- 创建和存储数学问题及解题洞察
- 自动生成向量嵌入用于语义搜索
- 支持标签分类和状态管理
2. **智能搜索**
- 多路召回：向量搜索 + 全文检索
- 融合排序：综合相似度、访问频次、时间因子
- 语义理解：基于 AI 的内容理解
3. **AI... | train_000.parquet/84 | {
"file_path": "COGNI-GRAPH-README.md",
"repo_id": "000haoji/deep-student",
"size": 4436,
"token_count": 2640
} |
# 设置环境变量以使用MSVC
$vsPath = "${env:ProgramFiles}\Microsoft Visual Studio\2022\BuildTools"
if (!(Test-Path $vsPath)) {
$vsPath = "${env:ProgramFiles}\Microsoft Visual Studio\2022\Community"
}
if (!(Test-Path $vsPath)) {
$vsPath = "${env:ProgramFiles}\Microsoft Visual Studio\2022\Professional"
}
if (!(Test-Path $vs... | train_000.parquet/85 | {
"file_path": "compile.ps1",
"repo_id": "000haoji/deep-student",
"size": 1055,
"token_count": 454
} |
# Deep Student
官网 www.deepstudent.com
当前正在进行公开测试中，此为尚未完善的版本。
该项目前身使用flask + python开发，现已完全重构。
**Deep Student** 是一个功能强大、集多种 AI 能力于一体的现代化桌面学习平台。它不仅仅是一个错题本，更是一个完整的知识获取、管理、复习和创造的智能中心。
该应用基于 **Tauri** 构建，结合了 **Rust** 的高性能后端和 **React** 的现代化前端，旨在为学生、研究者和终身学习者提供无缝、高效的学习体验。
![应用截图](https://www.deepstudent.com/QQ20250615-184... | train_000.parquet/86 | {
"file_path": "README.md",
"repo_id": "000haoji/deep-student",
"size": 3907,
"token_count": 2416
} |
# CogniGraph Neo4j Setup Guide
## Quick Setup Options
### Option 1: Docker (Recommended)
```bash
# Run Neo4j with default credentials
docker run \
--name neo4j-cogni \
--publish=7474:7474 \
--publish=7687:7687 \
--env=NEO4J_AUTH=neo4j/password \
neo4j:latest
```
Then use these settings in the CogniGraph in... | train_000.parquet/87 | {
"file_path": "COGNI-GRAPH-SETUP.md",
"repo_id": "000haoji/deep-student",
"size": 1782,
"token_count": 623
} |
[package]
name = "ai-mistake-manager"
version = "0.1.0"
description = "A Tauri App"
authors = ["you"]
edition = "2021"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[lib]
# The `_lib` suffix may seem redundant but it is necessary
# to make the lib name unique and wou... | train_000.parquet/88 | {
"file_path": "src-tauri/Cargo.toml",
"repo_id": "000haoji/deep-student",
"size": 1772,
"token_count": 786
} |
/* Deep Student Design - 简洁优雅的侧边栏布局 */
/* 紧急修复：彻底禁用所有旧tooltip相关的伪元素 */
.tooltip-container,
.tooltip-container::before,
.tooltip-container::after,
.tooltip-container.tooltip-right::before,
.tooltip-container.tooltip-right::after,
.tooltip-container.tooltip-left::before,
.tooltip-container.tooltip-left::after,
.tooltip... | train_000.parquet/89 | {
"file_path": "src/DeepStudent.css",
"repo_id": "000haoji/deep-student",
"size": 50281,
"token_count": 24124
} |
/* 全局样式 */
* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
/* 页面保活机制样式 - 确保隐藏的组件不影响布局 */
.page-container {
width: 100%;
}
.page-container[style*="display: none"] {
position: absolute;
visibility: hidden;
pointer-events: none;
}
/* 模态框样式 */
.modal-overlay {
position: fixed;
top: 0;
left: 0;
... | train_000.parquet/90 | {
"file_path": "src/App.css",
"repo_id": "000haoji/deep-student",
"size": 128793,
"token_count": 63674
} |
import { useState, useEffect, useCallback, useMemo } from 'react';
import { getCurrentWebviewWindow } from '@tauri-apps/api/webviewWindow';
import { MistakeLibrary } from './components/MistakeLibrary';
import { Settings } from './components/Settings';
import { BatchAnalysis } from './components/BatchAnalysis';
import U... | train_000.parquet/91 | {
"file_path": "src/App.tsx",
"repo_id": "000haoji/deep-student",
"size": 70864,
"token_count": 23515
} |
个性化知识网络系统：最终设计报告 (Neo4j版)
项目代号: Project CogniGraph
版本: 9.0 (Final - Neo4j Architecture)
日期: 2025年6月17日
核心: 一个以Neo4j为统一知识库，融合了多模态输入、AI推荐和高级检索的智能学习系统。
1. 绪论 (Introduction)
1.1 项目愿景与目标
构建一个智能的学习伴侣，将用户在解题过程中的思考（疑问、灵感、方法）结构化、网络化，并通过AI赋能，实现知识的智能检索、主动关联与深度洞察。
1.2 设计哲学与原则
极简核心，智慧涌现: 采用最简化的基础设施（单体Rust后端 + 单一Neo4j数据库），通过设计精良的数据... | train_000.parquet/92 | {
"file_path": "ds-graph/1.md",
"repo_id": "000haoji/deep-student",
"size": 4144,
"token_count": 2477
} |
use serde::{Deserialize, Serialize};
use std::path::PathBuf;
use std::fs::{File, self};
use std::io::Write;
use rusqlite::{Connection, Result as SqliteResult, params};
use zip::{ZipWriter, write::FileOptions};
use crate::models::AnkiCard;
use chrono::Utc;
/// 清理卡片内容中的无效模板占位符
fn clean_template_placeholders(content: &st... | train_000.parquet/93 | {
"file_path": "src-tauri/src/apkg_exporter_service.rs",
"repo_id": "000haoji/deep-student",
"size": 24720,
"token_count": 8655
} |
use aes_gcm::{
aead::{Aead, AeadCore, KeyInit, OsRng},
Aes256Gcm, Nonce, Key
};
use base64::{Engine as _, engine::general_purpose};
use keyring::Entry;
use rand::RngCore;
use serde::{Deserialize, Serialize};
use std::path::PathBuf;
#[derive(Debug, Serialize, Deserialize)]
pub struct EncryptedData {
pub cip... | train_000.parquet/94 | {
"file_path": "src-tauri/src/crypto.rs",
"repo_id": "000haoji/deep-student",
"size": 9386,
"token_count": 3590
} |
use crate::models::{DocumentTask, TaskStatus, AnkiGenerationOptions, AppError};
use crate::database::Database;
use std::sync::Arc;
use uuid::Uuid;
use chrono::Utc;
pub struct DocumentProcessingService {
db: Arc<Database>,
}
impl DocumentProcessingService {
pub fn new(db: Arc<Database>) -> Self {
Self ... | train_000.parquet/95 | {
"file_path": "src-tauri/src/document_processing_service.rs",
"repo_id": "000haoji/deep-student",
"size": 15555,
"token_count": 5434
} |
use std::fs;
use std::path::{Path, PathBuf};
use std::io::Write;
use tokio::fs as async_fs;
// use tokio::io::AsyncWriteExt; // Removed unused import
use base64::{Engine as _, engine::general_purpose};
use uuid::Uuid;
use crate::models::AppError;
use serde::{Serialize, Deserialize};
use std::collections::HashMap;
type... | train_000.parquet/96 | {
"file_path": "src-tauri/src/file_manager.rs",
"repo_id": "000haoji/deep-student",
"size": 22935,
"token_count": 8332
} |
use crate::models::{ChatMessage, AppError, StandardModel1Output, StandardModel2Output, ModelAssignments, StreamChunk};
use crate::database::Database;
use crate::file_manager::FileManager;
use crate::crypto::{CryptoService, EncryptedData};
use crate::gemini_adapter;
use reqwest::{Client, ClientBuilder, header::HeaderMap... | train_000.parquet/97 | {
"file_path": "src-tauri/src/llm_manager.rs",
"repo_id": "000haoji/deep-student",
"size": 128636,
"token_count": 44021
} |
use crate::models::{
AnalysisRequest, AnalysisResponse, ContinueChatRequest, ContinueChatResponse,
InitialAnalysisData, SaveMistakeRequest, SaveMistakeResponse,
MistakeItem, ChatMessage, ModelAssignments, AppError, ReviewAnalysisItem,
ReviewSessionResponse, ReviewSession, ReviewChatMessage, StartStreami... | train_000.parquet/98 | {
"file_path": "src-tauri/src/commands.rs",
"repo_id": "000haoji/deep-student",
"size": 158584,
"token_count": 60038
} |
use crate::models::{
ImageOcrRequest, ImageOcrResponse, TextRegion, CreateImageOcclusionRequest,
ImageOcclusionResponse, ImageOcclusionCard, OcclusionMask, MaskStyle, AppError
};
use crate::llm_manager::{LLMManager, ApiConfig};
use crate::database::Database;
use std::sync::Arc;
use uuid::Uuid;
use chrono::Utc;
... | train_000.parquet/99 | {
"file_path": "src-tauri/src/image_occlusion_service.rs",
"repo_id": "000haoji/deep-student",
"size": 16820,
"token_count": 6163
} |