Name | search-me JSON |
Version |
1.4
JSON |
| download |
home_page | https://is.gd/search_me |
Summary | Search in Google, Searx, Rambler. Explore VK, Facebook, Telegram, Twitter, TikTok, Snapchat, Instagram, Tumblr, YouTube. |
upload_time | 2021-06-19 17:28:28 |
maintainer | |
docs_url | None |
author | Kisel Michael R. |
requires_python | >=3.7 |
license | MIT |
keywords |
google
searx
rambler
search
web search
web scraper
vk
telegram
instagram
youtube
twitter
facebook
tumblr
snapchat
tik tok
tiktok
socials
downloader
parser
scraper
pdf report
pdf parse
text summary
|
VCS |
|
bugtrack_url |
|
requirements |
No requirements were recorded.
|
Travis-CI |
No Travis.
|
coveralls test coverage |
No coveralls.
|
[](https://is.gd/_pypi)
# Search Me
Search in: **Google**, **Searx**, **Rambler**. Extract data from: **VK, Facebook, Telegram, Twitter, TikTok, Snapchat, Instagram, Tumblr, YouTube**.
[](https://pypi.org/project/search-me)
[](https://pypi.org/project/search-me)
[](https://pypi.org/project/search-me)
[](https://pypi.org/project/search-me)
[](https://pepy.tech/project/search-me)
## PRE-INSTALLING
- If you want to generate PDF documents (param *pdf_report*), setup [wkhtmltopdf](https://is.gd/html2pdf)
- If you want to download video from youtube (param *socials*), setup [youtube-dl](https://is.gd/youtube_dl)
## INSTALLING
```bash
pip install search-me
```
## USAGE
### Imports
```python
from search_me import Google, Searx, Rambler
```
### Init search engine
```python
engine = Google()
engine = Searx()
engine = Rambler()
```
Parameters:
- *results*: Number of search results on page (*default: 10*)
- *retry*: Number of retries for one query (*default: 10*)
- *show_results*: Show results in table (*default: True*)
- *cache*: Caching searched data after each search query in json file (*default: True*)
- *sleep_min*: Minimum time in seconds to sleep after each query (*default: 0.0*)
- *sleep_max*: Maximum time in seconds to sleep after each query (*default: 1.5*)
- *pdf_report*: Export searched data to pdf-documents (*default: False*)
- *pdf_timeout*: Waiting time in seconds for create pdf-document (*default: 30*)
- *pdf_threads*: Number of threads for generating pdf-documents (*default: multiprocessing.cpu_count()*)
- *pdf_parse*: Parse generated pdf-documents; used, when *pdf_report=True* (*default: False*)
- *pdf_options*: Used, when *pdf_parse=True* (*default: {"text": True, "summary": True, "summary_params": ("ratio", 0.15), "urls": True, "keywords": True}*)
- *text*: Extract text
- *summary*: Generate summary from extracted text
- *summary_params*: Tuple, where first element - type of summarizing ("ratio" or "words"); the second element - value (percent of text or count of words)
- *urls*: Extract urls
- *keywords*: Generate keywords from extracted text
- *use_social_search*: Use search across socials (*default: False*)
- *socials*: Tuple with names of social nets (*default: ("vk", "instagram", "telegram", "twitter", "youtube", "facebook", "tumblr", "snapchat", "tiktok")*)
- *social_threads*: Number of threads for social search (*default: multiprocessing.cpu_count()*)
- *social_options*: Used, when *use_social_search=True* (*default: {"posts_limit": 10, "export_data": True, "export_format": "csv", "download_media": True}*)
- *posts_limit*: Number of posts for VK, Facebook, Telegram, Twitter, Youtube, Snapchat
- *export_data*: Export data about posts in file
- *export_format*: Export file format (csv, xls, html, json)
- *download_media*: Download media from Instagram, Tumblr, Youtube, Snapchat
### Start search
```python
engine.search(items=["query 1", "query 2"])
```
### Access result
```python
print(engine.results)
```
## EXAMPLE USAGE
```python
import logging
log = logging.getLogger().setLevel(logging.DEBUG)
from search_me import Google
g = Google(
retry=3,
pdf_report=True,
pdf_timeout=10,
cache=True,
use_social_search=True,
pdf_parse=True,
socials=("vk", "telegram", "twitter", "youtube", "facebook")
)
g.search(items=["社會信用體系", "0x0007ee", "журнал медуза"])
for search_result in g.search_results:
print(f"Item: {search_result['item']}")
print("Links:")
print("\n".join(search_result['links']))
print("Socials:")
for social, social_v in search_result['socials'].items():
print(f"{social} {social_v}")
for pdf in search_result['pdf']:
print(f"Path: {pdf['path']}\nText: {pdf['text']}\nSummary: {pdf['summary']}")
print("Urls:")
print("\n".join(list(pdf['urls'])))
print("Keywords:")
print("\n".join(list(pdf['keywords'])))
print()
print("=" * 40)
```
## LINKS
- [Search Language Codes](https://is.gd/lang_codes)
- [List of Google domains](https://is.gd/domains_list)
## SUPPORT
[](https://is.gd/mypaypal)
Raw data
{
"_id": null,
"home_page": "https://is.gd/search_me",
"name": "search-me",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "google,searx,rambler,search,web search,web scraper,vk,telegram,instagram,youtube,twitter,facebook,tumblr,snapchat,tik tok,tiktok,socials,downloader,parser,scraper,pdf report,pdf parse,text summary",
"author": "Kisel Michael R.",
"author_email": "deploy-me@yandex.ru",
"download_url": "https://files.pythonhosted.org/packages/cd/4c/9bae2772845c6de78f082fff79898fc8a6a47bbbdab3080479774b7dcb22/search-me-1.4.tar.gz",
"platform": "",
"description": "[](https://is.gd/_pypi)\n\n# Search Me\n\nSearch in: **Google**, **Searx**, **Rambler**. Extract data from: **VK, Facebook, Telegram, Twitter, TikTok, Snapchat, Instagram, Tumblr, YouTube**.\n\n[](https://pypi.org/project/search-me)\n[](https://pypi.org/project/search-me)\n[](https://pypi.org/project/search-me)\n[](https://pypi.org/project/search-me)\n[](https://pepy.tech/project/search-me)\n\n## PRE-INSTALLING\n\n- If you want to generate PDF documents (param *pdf_report*), setup [wkhtmltopdf](https://is.gd/html2pdf)\n- If you want to download video from youtube (param *socials*), setup [youtube-dl](https://is.gd/youtube_dl)\n\n## INSTALLING\n\n```bash\npip install search-me\n```\n\n## USAGE\n\n### Imports\n\n```python\nfrom search_me import Google, Searx, Rambler\n```\n\n### Init search engine\n\n```python\nengine = Google()\nengine = Searx()\nengine = Rambler()\n```\n\nParameters:\n\n- *results*: Number of search results on page (*default: 10*)\n- *retry*: Number of retries for one query (*default: 10*)\n- *show_results*: Show results in table (*default: True*)\n- *cache*: Caching searched data after each search query in json file (*default: True*)\n- *sleep_min*: Minimum time in seconds to sleep after each query (*default: 0.0*)\n- *sleep_max*: Maximum time in seconds to sleep after each query (*default: 1.5*)\n- *pdf_report*: Export searched data to pdf-documents (*default: False*)\n- *pdf_timeout*: Waiting time in seconds for create pdf-document (*default: 30*)\n- *pdf_threads*: Number of threads for generating pdf-documents (*default: multiprocessing.cpu_count()*)\n- *pdf_parse*: Parse generated pdf-documents; used, when *pdf_report=True* (*default: False*)\n- *pdf_options*: Used, when *pdf_parse=True* (*default: {\"text\": True, \"summary\": True, \"summary_params\": (\"ratio\", 0.15), \"urls\": True, \"keywords\": True}*)\n\t- *text*: Extract text\n\t- *summary*: Generate summary from extracted text\n\t- *summary_params*: Tuple, where first element - type of summarizing (\"ratio\" or \"words\"); the second element - value (percent of text or count of words)\n\t- *urls*: Extract urls\n\t- *keywords*: Generate keywords from extracted text\n- *use_social_search*: Use search across socials (*default: False*)\n- *socials*: Tuple with names of social nets (*default: (\"vk\", \"instagram\", \"telegram\", \"twitter\", \"youtube\", \"facebook\", \"tumblr\", \"snapchat\", \"tiktok\")*)\n- *social_threads*: Number of threads for social search (*default: multiprocessing.cpu_count()*)\n- *social_options*: Used, when *use_social_search=True* (*default: {\"posts_limit\": 10, \"export_data\": True, \"export_format\": \"csv\", \"download_media\": True}*)\n\t- *posts_limit*: Number of posts for VK, Facebook, Telegram, Twitter, Youtube, Snapchat\n\t- *export_data*: Export data about posts in file\n\t- *export_format*: Export file format (csv, xls, html, json)\n\t- *download_media*: Download media from Instagram, Tumblr, Youtube, Snapchat\n\n\n### Start search\n\n```python\nengine.search(items=[\"query 1\", \"query 2\"])\n```\n\n### Access result\n\n```python\nprint(engine.results)\n```\n\n## EXAMPLE USAGE\n\n```python\nimport logging\nlog = logging.getLogger().setLevel(logging.DEBUG)\n\nfrom search_me import Google\ng = Google(\n\tretry=3,\n\tpdf_report=True,\n\tpdf_timeout=10,\n\tcache=True,\n\tuse_social_search=True,\n\tpdf_parse=True,\n\tsocials=(\"vk\", \"telegram\", \"twitter\", \"youtube\", \"facebook\")\n\t)\ng.search(items=[\"\u793e\u6703\u4fe1\u7528\u9ad4\u7cfb\", \"0x0007ee\", \"\u0436\u0443\u0440\u043d\u0430\u043b \u043c\u0435\u0434\u0443\u0437\u0430\"])\nfor search_result in g.search_results:\n\tprint(f\"Item: {search_result['item']}\")\n\tprint(\"Links:\")\n\tprint(\"\\n\".join(search_result['links']))\n\tprint(\"Socials:\")\n\tfor social, social_v in search_result['socials'].items():\n\t\tprint(f\"{social} {social_v}\")\n\tfor pdf in search_result['pdf']:\n\t\tprint(f\"Path: {pdf['path']}\\nText: {pdf['text']}\\nSummary: {pdf['summary']}\")\n\t\tprint(\"Urls:\")\n\t\tprint(\"\\n\".join(list(pdf['urls'])))\n\t\tprint(\"Keywords:\")\n\t\tprint(\"\\n\".join(list(pdf['keywords'])))\n\t\tprint()\n\tprint(\"=\" * 40)\n```\n\n## LINKS\n- [Search Language Codes](https://is.gd/lang_codes)\n- [List of Google domains](https://is.gd/domains_list)\n\n## SUPPORT\n\n[](https://is.gd/mypaypal)\n\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Search in Google, Searx, Rambler. Explore VK, Facebook, Telegram, Twitter, TikTok, Snapchat, Instagram, Tumblr, YouTube.",
"version": "1.4",
"split_keywords": [
"google",
"searx",
"rambler",
"search",
"web search",
"web scraper",
"vk",
"telegram",
"instagram",
"youtube",
"twitter",
"facebook",
"tumblr",
"snapchat",
"tik tok",
"tiktok",
"socials",
"downloader",
"parser",
"scraper",
"pdf report",
"pdf parse",
"text summary"
],
"urls": [
{
"comment_text": "",
"digests": {
"md5": "df98c917d06cd38886a1175d0eafa63b",
"sha256": "07cfe9f34b286808f3ac9020346167ae14a334610767d35e85a7c80736f37d57"
},
"downloads": -1,
"filename": "search_me-1.4-py3-none-any.whl",
"has_sig": false,
"md5_digest": "df98c917d06cd38886a1175d0eafa63b",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 11707,
"upload_time": "2021-06-19T17:28:26",
"upload_time_iso_8601": "2021-06-19T17:28:26.877590Z",
"url": "https://files.pythonhosted.org/packages/3d/be/e1c00ed5df41650cd6c42f9638e5b7ff67cde1e9deceb54eed7820d8e044/search_me-1.4-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "1a8959af016fde0d56d68c30327d3a23",
"sha256": "5e632e9de56c0d2307c325178778468763d4c92542949ce7c59620567c9f88b6"
},
"downloads": -1,
"filename": "search-me-1.4.tar.gz",
"has_sig": false,
"md5_digest": "1a8959af016fde0d56d68c30327d3a23",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 13214,
"upload_time": "2021-06-19T17:28:28",
"upload_time_iso_8601": "2021-06-19T17:28:28.765676Z",
"url": "https://files.pythonhosted.org/packages/cd/4c/9bae2772845c6de78f082fff79898fc8a6a47bbbdab3080479774b7dcb22/search-me-1.4.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2021-06-19 17:28:28",
"github": false,
"gitlab": false,
"bitbucket": false,
"lcname": "search-me"
}