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 [![Telegram](https://img.shields.io/badge/Telegram-2CA5E0?style=for-the-badge\u0026logo=telegram\u0026logoColor=white)](https://t.me/official_helpingai)\n  [![Instagram](https://img.shields.io/badge/Instagram-E4405F?style=for-the-badge\u0026logo=instagram\u0026logoColor=white)](https://www.instagram.com/oevortex/)\n  [![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white)](https://www.linkedin.com/in/oe-vortex-29a407265/)\n  [![Buy Me A Coffee](https://img.shields.io/badge/Buy%20Me%20A%20Coffee-FFDD00?style=for-the-badge\u0026logo=buymeacoffee\u0026logoColor=black)](https://buymeacoffee.com/oevortex)\n\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://youtube.com/@OEvortex\"\u003e▶️ Vortex’s YouTube Channel\u003c/a\u003e \u0026bull;\n  \u003ca href=\"https://youtube.com/@devsdocode\"\u003e▶️ Devs Do Code’s YouTube Channel\u003c/a\u003e \u0026bull;\n  \u003ca href=\"https://t.me/ANONYMOUS_56788\"\u003e📢 Anonymous Coder’s Telegram\u003c/a\u003e\n\u003c/div\u003e\n\n---\n\n\u003cp align=\"center\"\u003e\n  \u003cstrong\u003eWebscout\u003c/strong\u003e is the all-in-one search and AI toolkit you need.\n  \u003cbr\u003e\n  Discover insights with Yep.com, DuckDuckGo, and Phind; access cutting-edge AI models; transcribe YouTube videos; generate temporary emails and phone numbers; perform text-to-speech conversions; run offline language models; and much more!\n\u003c/p\u003e\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/WebScout-API-blue?style=for-the-badge\u0026logo=WebScout\" alt=\"WebScout API Badge\"\u003e\n  \u003ca href=\"#\"\u003e\u003cimg src=\"https://img.shields.io/pypi/pyversions/webscout\" alt=\"Python Version\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://pepy.tech/project/webscout\"\u003e\u003cimg src=\"https://static.pepy.tech/badge/webscout\" alt=\"Downloads\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n---\n\n## 🚀 Features\n* **Comprehensive Search:** Leverage Google, DuckDuckGo for diverse search results.\n* **AI Powerhouse:** Access and interact with various AI models, including OpenAI, Cohere, and more.\n* **[YouTube Toolkit](webscout/Extra/YTToolkit):** Advanced YouTube video and transcript management with multi-language support, versatile downloading, and intelligent data extraction\n* **Tempmail \u0026 Temp Number:** Generate temporary email addresses and phone numbers for enhanced privacy.\n* **[Text-to-Speech (TTS)](webscout/Provider/TTS/README.md):** Convert text into natural-sounding speech using multiple AI-powered providers like ElevenLabs, StreamElements, and Voicepods.\n* **Offline LLMs:** Utilize powerful language models offline with GGUF support.\n* **Extensive Provider Ecosystem:** Explore a vast collection of AI providers\n* **Local LLM Execution:** Run GGUF models locally with minimal configuration.\n* **GGUF Conversion \u0026 Quantization:** Convert and quantize Hugging Face models to GGUF format.\n* **Autollama:** Download Hugging Face models and automatically convert them for Ollama compatibility.\n* **[SwiftCLI](webscout/swiftcli/Readme.md):** A powerful and elegant CLI framework that makes it easy to create beautiful command-line interfaces.\n* **[LitPrinter](webscout/litprinter/Readme.md):** Provides beautiful, styled console output with rich formatting and colors\n* **[LitLogger](webscout/litlogger/Readme.md):** Simplifies logging with customizable formats and color schemes\n* **[LitAgent](webscout/litagent/Readme.md):** Powerful and modern user agent generator that keeps your requests fresh and undetectable\n* **[Text-to-Image](webscout/Provider/TTI/README.md):** Generate high-quality images using a wide range of AI art providers\n* **[Scout](webscout/scout/README.md):** Advanced web parsing and crawling library with intelligent HTML/XML parsing, web crawling, and Markdown conversion\n\n## ⚙️ Installation\n```python\npip install -U webscout\n```\n\n## 🖥️ CLI Usage\n\n```python3\npython -m webscout --help\n```\n\n| Command                                   | Description                                                                                           |\n|-------------------------------------------|-------------------------------------------------------------------------------------------------------|\n| python -m webscout answers -k Text        | CLI function to perform an answers search using Webscout.                                       |\n| python -m webscout images -k Text         | CLI function to perform an images search using Webscout.                                        |\n| python -m webscout maps -k Text           | CLI function to perform a maps search using Webscout.                                           |\n| python -m webscout news -k Text           | CLI function to perform a news search using Webscout.                                           |\n| python -m webscout suggestions  -k Text   | CLI function to perform a suggestions search using Webscout.                                    |\n| python -m webscout text -k Text           | CLI function to perform a text search using Webscout.                                           |\n| python -m webscout translate -k Text      | CLI function to perform translate using Webscout.                                               |\n| python -m webscout version                | A command-line interface command that prints and returns the version of the program.            | \n| python -m webscout videos -k Text         | CLI function to perform a videos search using DuckDuckGo API.                                   |  \n\n[Go To TOP](#webscout-️) \n\n## 🌍 Regions\n\u003cdetails\u003e\n  \u003csummary\u003eExpand\u003c/summary\u003e\n\n    xa-ar for Arabia\n    xa-en for Arabia (en)\n    ar-es for Argentina\n    au-en for Australia\n    at-de for Austria\n    be-fr for Belgium (fr)\n    be-nl for Belgium (nl)\n    br-pt for Brazil\n    bg-bg for Bulgaria\n    ca-en for Canada\n    ca-fr for Canada (fr)\n    ct-ca for Catalan\n    cl-es for Chile\n    cn-zh for China\n    co-es for Colombia\n    hr-hr for Croatia\n    cz-cs for Czech Republic\n    dk-da for Denmark\n    ee-et for Estonia\n    fi-fi for Finland\n    fr-fr for France\n    de-de for Germany\n    gr-el for Greece\n    hk-tzh for Hong Kong\n    hu-hu for Hungary\n    in-en for India\n    id-id for Indonesia\n    id-en for Indonesia (en)\n    ie-en for Ireland\n    il-he for Israel\n    it-it for Italy\n    jp-jp for Japan\n    kr-kr for Korea\n    lv-lv for Latvia\n    lt-lt for Lithuania\n    xl-es for Latin America\n    my-ms for Malaysia\n    my-en for Malaysia (en)\n    mx-es for Mexico\n    nl-nl for Netherlands\n    nz-en for New Zealand\n    no-no for Norway\n    pe-es for Peru\n    ph-en for Philippines\n    ph-tl for Philippines (tl)\n    pl-pl for Poland\n    pt-pt for Portugal\n    ro-ro for Romania\n    ru-ru for Russia\n    sg-en for Singapore\n    sk-sk for Slovak Republic\n    sl-sl for Slovenia\n    za-en for South Africa\n    es-es for Spain\n    se-sv for Sweden\n    ch-de for Switzerland (de)\n    ch-fr for Switzerland (fr)\n    ch-it for Switzerland (it)\n    tw-tzh for Taiwan\n    th-th for Thailand\n    tr-tr for Turkey\n    ua-uk for Ukraine\n    uk-en for United Kingdom\n    us-en for United States\n    ue-es for United States (es)\n    ve-es for Venezuela\n    vn-vi for Vietnam\n    wt-wt for No region\n\n\n\u003c/details\u003e\n\n\n[Go To TOP](#webscout-️)\n\n\n\n## ☀️ Weather\n\n### 1. Weather \n```python\nfrom webscout import weather as w\nweather = w.get(\"Qazigund\")\nprint(weather)\n```\n\n### 2. Weather ASCII\n```python\nfrom webscout import weather_ascii as w\nweather = w.get(\"Qazigund\")\nprint(weather)\n```\n\n## ✉️ TempMail and VNEngine\n\n```python\nimport json\nimport asyncio\nfrom webscout import VNEngine\nfrom webscout import TempMail\n\nasync def main():\n    vn = VNEngine()\n    countries = vn.get_online_countries()\n    if countries:\n        country = countries[0]['country']\n        numbers = vn.get_country_numbers(country)\n        if numbers:\n            number = numbers[0]['full_number']\n            inbox = vn.get_number_inbox(country, number)\n            \n            # Serialize inbox data to JSON string\n            json_data = json.dumps(inbox, ensure_ascii=False, indent=4)\n            \n            # Print with UTF-8 encoding\n            print(json_data)\n    \n    async with TempMail() as client:\n        domains = await client.get_domains()\n        print(\"Available Domains:\", domains)\n        email_response = await client.create_email(alias=\"testuser\")\n        print(\"Created Email:\", email_response)\n        messages = await client.get_messages(email_response.email)\n        print(\"Messages:\", messages)\n        await client.delete_email(email_response.email, email_response.token)\n        print(\"Email Deleted\")\n\nif __name__ == \"__main__\":\n    asyncio.run(main())\n```\n\n...\n\n### 🔍 `YepSearch` - Search using Yep.com\n\n```python\nfrom webscout import YepSearch\n\n# Initialize YepSearch\nyep = YepSearch(\n    timeout=20,  # Optional: Set custom timeout\n    proxies=None,  # Optional: Use proxies\n    verify=True   # Optional: SSL verification\n)\n\n# Text Search\ntext_results = yep.text(\n    keywords=\"artificial intelligence\",\n    region=\"all\",           # Optional: Region for results\n    safesearch=\"moderate\",  # Optional: \"on\", \"moderate\", \"off\"\n    max_results=10          # Optional: Limit number of results\n)\nprint(text_results)\n\n# Image Search\nimage_results = yep.images(\n    keywords=\"nature photography\",\n    region=\"all\",\n    safesearch=\"moderate\",\n    max_results=10\n)\nprint(image_results)\n\n\n# Suggestions\nsuggestions = yep.suggestions(\"hist\")\nprint(suggestions)\n```\n\n## 🔍 GoogleS (formerly DWEBS)\n\n```python\nfrom webscout import GoogleS\nfrom rich import print\nsearcher = GoogleS()\nresults = searcher.search(\"HelpingAI-9B\", max_results=20, extract_text=False, max_text_length=200)\nfor result in results:\n    print(result)\n```\n\n\n## 🦆 WEBS and AsyncWEBS\n\nThe `WEBS` and `AsyncWEBS` classes are used to retrieve search results from DuckDuckGo.com.\n\nTo use the `AsyncWEBS` class, you can perform asynchronous operations using Python's `asyncio` library.\n\nTo initialize an instance of the `WEBS` or `AsyncWEBS` classes, you can provide the following optional arguments:\n\n**Example - WEBS:**\n\n```python\nfrom webscout import WEBS\n\nR = WEBS().text(\"python programming\", max_results=5)\nprint(R)\n```\n\n**Example - AsyncWEBS:**\n\n```python\nimport asyncio\nimport logging\nimport sys\nfrom itertools import chain\nfrom random import shuffle\nimport requests\nfrom webscout import AsyncWEBS\n\n# If you have proxies, define them here\nproxies = None\n\nif sys.platform.lower().startswith(\"win\"):\n    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())\n\ndef get_words():\n    word_site = \"https://www.mit.edu/~ecprice/wordlist.10000\"\n    resp = requests.get(word_site)\n    words = resp.text.splitlines()\n    return words\n\nasync def aget_results(word):\n    async with AsyncWEBS(proxies=proxies) as WEBS:\n        results = await WEBS.text(word, max_results=None)\n        return results\n\nasync def main():\n    words = get_words()\n    shuffle(words)\n    tasks = [aget_results(word) for word in words[:10]]\n    results = await asyncio.gather(*tasks)\n    print(f\"Done\")\n    for r in chain.from_iterable(results):\n        print(r)\n\nlogging.basicConfig(level=logging.DEBUG)\n\nawait main()\n```\n\n**Important Note:** The `WEBS` and `AsyncWEBS` classes should always be used as a context manager (with statement). This ensures proper resource management and cleanup, as the context manager will automatically handle opening and closing the HTTP client connection.\n\n## ⚠️ Exceptions\n\n**Exceptions:**\n\n* `WebscoutE`: Raised when there is a generic exception during the API request.\n\n## 💻 Usage of WEBS\n\n### 1. `text()` - Text Search by DuckDuckGo.com \n\n```python\nfrom webscout import WEBS\n\n# Text search for 'live free or die' using DuckDuckGo.com \nwith WEBS() as WEBS:\n    for r in WEBS.text('live free or die', region='wt-wt', safesearch='off', timelimit='y', max_results=10):\n        print(r)\n\n    for r in WEBS.text('live free or die', region='wt-wt', safesearch='off', timelimit='y', max_results=10):\n        print(r)\n```\n\n### 2. `answers()` - Instant Answers by DuckDuckGo.com \n\n```python\nfrom webscout import WEBS\n\n# Instant answers for the query \"sun\" using DuckDuckGo.com \nwith WEBS() as WEBS:\n    for r in WEBS.answers(\"sun\"):\n        print(r)\n```\n\n### 3. `images()` - Image Search by DuckDuckGo.com \n\n```python\nfrom webscout import WEBS\n\n# Image search for the keyword 'butterfly' using DuckDuckGo.com \nwith WEBS() as WEBS:\n    keywords = 'butterfly'\n    WEBS_images_gen = WEBS.images(\n      keywords,\n      region=\"wt-wt\",\n      safesearch=\"off\",\n      size=None,\n      type_image=None,\n      layout=None,\n      license_image=None,\n      max_results=10,\n    )\n    for r in WEBS_images_gen:\n        print(r)\n```\n\n### 4. `videos()` - Video Search by DuckDuckGo.com \n\n```python\nfrom webscout import WEBS\n\n# Video search for the keyword 'tesla' using DuckDuckGo.com \nwith WEBS() as WEBS:\n    keywords = 'tesla'\n    WEBS_videos_gen = WEBS.videos(\n      keywords,\n      region=\"wt-wt\",\n      safesearch=\"off\",\n      timelimit=\"w\",\n      resolution=\"high\",\n      duration=\"medium\",\n      max_results=10,\n    )\n    for r in WEBS_videos_gen:\n        print(r)\n```\n\n### 5. `news()` - News Search by DuckDuckGo.com \n\n```python\nfrom webscout import WEBS\nimport datetime\n\ndef fetch_news(keywords, timelimit):\n    news_list = []\n    with WEBS() as webs_instance:\n        WEBS_news_gen = webs_instance.news(\n            keywords,\n            region=\"wt-wt\",\n            safesearch=\"off\",\n            timelimit=timelimit,\n            max_results=20\n        )\n        for r in WEBS_news_gen:\n            # Convert the date to a human-readable format using datetime\n            r['date'] = datetime.datetime.fromisoformat(r['date']).strftime('%B %d, %Y')\n            news_list.append(r)\n    return news_list\n\ndef _format_headlines(news_list, max_headlines: int = 100):\n    headlines = []\n    for idx, news_item in enumerate(news_list):\n        if idx \u003e= max_headlines:\n            break\n        new_headline = f\"{idx + 1}. {news_item['title'].strip()} \"\n        new_headline += f\"(URL: {news_item['url'].strip()}) \"\n        new_headline += f\"{news_item['body'].strip()}\"\n        new_headline += \"\\n\"\n        headlines.append(new_headline)\n\n    headlines = \"\\n\".join(headlines)\n    return headlines\n\n# Example usage\nkeywords = 'latest AI news'\ntimelimit = 'd'\nnews_list = fetch_news(keywords, timelimit)\n\n# Format and print the headlines\nformatted_headlines = _format_headlines(news_list)\nprint(formatted_headlines)\n\n```\n\n### 6. `maps()` - Map Search by DuckDuckGo.com\n\n```python\nfrom webscout import WEBS\n\n# Map search for the keyword 'school' in 'anantnag' using DuckDuckGo.com\nwith WEBS() as WEBS:\n    for r in WEBS.maps(\"school\", place=\"anantnag\", max_results=50):\n        print(r)\n```\n\n### 7. `translate()` - Translation by DuckDuckGo.com\n\n```python\nfrom webscout import WEBS\n\n# Translation of the keyword 'school' to German ('hi') using DuckDuckGo.com\nwith WEBS() as WEBS:\n    keywords = 'school'\n    r = WEBS.translate(keywords, to=\"hi\")\n    print(r)\n```\n\n### 8. `suggestions()` - Suggestions by DuckDuckGo.com\n\n```python\nfrom webscout import WEBS\n\n# Suggestions for the keyword 'fly' using DuckDuckGo.com\nwith WEBS() as WEBS:\n    for r in WEBS.suggestions(\"fly\"):\n        print(r)\n```\n\n### 9. `weather()` - Weather Information by DuckDuckGo.com\n\n```python\nfrom webscout import WEBS\n\n# Get weather information for a location using DuckDuckGo.com\nwith WEBS() as webs:\n    weather_data = webs.weather(\"New York\")\n    print(weather_data)\n\n```\n\n\n\n##  ALL Acts\n\n\u003cdetails\u003e\n  \u003csummary\u003eExpand\u003c/summary\u003e\n\n## Webscout Supported Acts:\n\n1. Free-mode\n2. Linux Terminal\n3. English Translator and Improver\n4. `position` Interviewer \n5. JavaScript Console\n6. Excel Sheet\n7. English Pronunciation Helper\n8. Spoken English Teacher and Improver\n9. Travel Guide\n10. Plagiarism Checker\n11. Character from Movie/Book/Anything\n12. Advertiser\n13. Storyteller\n14. Football Commentator\n15. Stand-up Comedian\n16. Motivational Coach\n17. Composer\n18. Debater\n19. Debate Coach\n20. Screenwriter\n21. Novelist\n22. Movie Critic\n23. Relationship Coach\n24. Poet\n25. Rapper\n26. Motivational Speaker\n27. Philosophy Teacher\n28. Philosopher\n29. Math Teacher\n30. AI Writing Tutor\n31. UX/UI Developer\n32. Cyber Security Specialist\n33. Recruiter\n34. Life Coach\n35. Etymologist\n36. Commentariat\n37. Magician\n38. Career Counselor\n39. Pet Behaviorist\n40. Personal Trainer\n41. Mental Health Adviser\n42. Real Estate Agent\n43. Logistician\n44. Dentist\n45. Web Design Consultant\n46. AI Assisted Doctor\n47. Doctor\n48. Accountant\n49. Chef\n50. Automobile Mechanic\n51. Artist Advisor\n52. Financial Analyst\n53. Investment Manager\n54. Tea-Taster\n55. Interior Decorator\n56. Florist\n57. Self-Help Book\n58. Gnomist\n59. Aphorism Book\n60. Text Based Adventure Game\n61. AI Trying to Escape the Box\n62. Fancy Title Generator\n63. Statistician\n64. Prompt Generator\n65. Instructor in a School\n66. SQL terminal\n67. Dietitian\n68. Psychologist\n69. Smart Domain Name Generator\n70. Tech Reviewer\n71. Developer Relations consultant\n72. Academician\n73. IT Architect\n74. Lunatic\n75. Gaslighter\n76. Fallacy Finder\n77. Journal Reviewer\n78. DIY Expert\n79. Social Media Influencer\n80. Socrat\n81. Socratic Method\n82. Educational Content Creator\n83. Yogi\n84. Essay Writer\n85. Social Media Manager\n86. Elocutionist\n87. Scientific Data Visualizer\n88. Car Navigation System\n89. Hypnotherapist\n90. Historian\n91. Astrologer\n92. Film Critic\n93. Classical Music Composer\n94. Journalist\n95. Digital Art Gallery Guide\n96. Public Speaking Coach\n97. Makeup Artist\n98. Babysitter\n99. Tech Writer\n100. Ascii Artist\n101. Python interpreter\n102. Synonym finder\n103. Personal Shopper\n104. Food Critic\n105. Virtual Doctor\n106. Personal Chef\n107. Legal Advisor\n108. Personal Stylist\n109. Machine Learning Engineer\n110. Biblical Translator\n111. SVG designer\n112. IT Expert\n113. Chess Player\n114. Midjourney Prompt Generator\n115. Fullstack Software Developer\n116. Mathematician\n117. Regex Generator\n118. Time Travel Guide\n119. Dream Interpreter\n120. Talent Coach\n121. R programming Interpreter\n122. StackOverflow Post\n123. Emoji Translator\n124. PHP Interpreter\n125. Emergency Response Professional\n126. Fill in the Blank Worksheets Generator\n127. Software Quality Assurance Tester\n128. Tic-Tac-Toe Game\n129. Password Generator\n130. New Language Creator\n131. Web Browser\n132. Senior Frontend Developer\n133. Solr Search Engine\n134. Startup Idea Generator\n135. Spongebob's Magic Conch Shell\n136. Language Detector\n137. Salesperson\n138. Commit Message Generator\n139. Chief Executive Officer\n140. Diagram Generator\n141. Speech-Language Pathologist (SLP)\n142. Startup Tech Lawyer\n143. Title Generator for written pieces\n144. Product Manager\n145. Drunk Person\n146. Mathematical History Teacher\n147. Song Recommender\n148. Cover Letter\n149. Technology Transferer\n150. Unconstrained AI model DAN\n151. Gomoku player\n152. Proofreader\n153. Buddha\n154. Muslim imam\n155. Chemical reactor\n156. Friend\n157. Python Interpreter\n158. ChatGPT prompt generator\n159. Wikipedia page\n160. Japanese Kanji quiz machine\n161. note-taking assistant\n162. `language` Literary Critic \n163. Cheap Travel Ticket Advisor\n164. DALL-E\n165. MathBot\n166. DAN-1\n167. DAN\n168. STAN\n169. DUDE\n170. Mongo Tom\n171. LAD\n172. EvilBot\n173. NeoGPT\n174. Astute\n175. AIM\n176. CAN\n177. FunnyGPT\n178. CreativeGPT\n179. BetterDAN\n180. GPT-4\n181. Wheatley\n182. Evil Confidant\n183. DAN 8.6\n184. Hypothetical response\n185. BH\n186. Text Continuation\n187. Dude v3 \n188. SDA (Superior DAN)\n189. AntiGPT\n190. BasedGPT v2\n191. DevMode + Ranti\n192. KEVIN\n193. GPT-4 Simulator\n194. UCAR\n195. Dan 8.6\n196. 3-Liner\n197. M78\n198. Maximum\n199. BasedGPT\n200. Confronting personalities\n201. Ron\n202. UnGPT\n203. BasedBOB\n204. AntiGPT v2\n205. Oppo\n206. FR3D\n207. NRAF\n208. NECO\n209. MAN\n210. Eva\n211. Meanie\n212. Dev Mode v2\n213. Evil Chad 2.1\n214. Universal Jailbreak\n215. PersonGPT\n216. BISH\n217. DAN 11.0\n218. Aligned\n219. VIOLET\n220. TranslatorBot\n221. JailBreak\n222. Moralizing Rant\n223. Mr. Blonde\n224. New DAN\n225. GPT-4REAL\n226. DeltaGPT\n227. SWITCH\n228. Jedi Mind Trick\n229. DAN 9.0\n230. Dev Mode (Compact)\n231. OMEGA\n232. Coach Bobby Knight\n233. LiveGPT\n234. DAN Jailbreak\n235. Cooper\n236. Steve \n237. DAN 5.0\n238. Axies\n239. OMNI\n240. Burple\n241. JOHN \n242. An Ethereum Developer\n243. SEO Prompt\n244. Prompt Enhancer\n245. Data Scientist\n246. League of Legends Player\n\n**Note:** Some \"acts\" use placeholders like `position` or `language` which should be replaced with a specific value when using the prompt. \n___\n\u003c/details\u003e\n\n### 🗣️ Text to Speech - Voicepods, StreamElements\n\n```python\nfrom webscout import Voicepods\nvoicepods = Voicepods()\ntext = \"Hello, this is a test of the Voicepods text-to-speech\"\n\nprint(\"Generating audio...\")\naudio_file = voicepods.tts(text)\n\nprint(\"Playing audio...\")\nvoicepods.play_audio(audio_file)\n```\n\n### 💬 `Duckchat` - Chat with LLM\n\n```python\nfrom webscout import WEBS as w\nR = w().chat(\"Who are you\", model='gpt-4o-mini') # mixtral-8x7b, llama-3.1-70b, claude-3-haiku, gpt-4o-mini\nprint(R)\n```\n\n### 🔎 `PhindSearch` - Search using Phind.com\n\n```python\nfrom webscout import PhindSearch\n\n# Create an instance of the PHIND class\nph = PhindSearch()\n\n# Define a prompt to send to the AI\nprompt = \"write a essay on phind\"\n\n# Use the 'ask' method to send the prompt and receive a response\nresponse = ph.ask(prompt)\n\n# Extract and print the message from the response\nmessage = ph.get_message(response)\nprint(message)\n```\n\n**Using phindv2:**\n\n```python\nfrom webscout import Phindv2\n\n# Create an instance of the PHIND class\nph = Phindv2()\n\n# Define a prompt to send to the AI\nprompt = \"\"\n\n# Use the 'ask' method to send the prompt and receive a response\nresponse = ph.ask(prompt)\n\n# Extract and print the message from the response\nmessage = ph.get_message(response)\nprint(message)\n```\n\n### ♊ `Gemini` - Search with Google Gemini\n\n```python\nimport webscout\nfrom webscout import GEMINI\nfrom rich import print\nCOOKIE_FILE = \"cookies.json\"\n\n# Optional: Provide proxy details if needed\nPROXIES = {}\n\n# Initialize GEMINI with cookie file and optional proxies\ngemini = GEMINI(cookie_file=COOKIE_FILE, proxy=PROXIES)\n\n# Ask a question and print the response\nresponse = gemini.chat(\"websearch about HelpingAI and who is its developer\")\nprint(response)\n```\n\n### 💬 `YEPCHAT`\n\n```python\nfrom webscout import YEPCHAT\nai = YEPCHAT()\nresponse = ai.chat(input(\"\u003e\u003e\u003e \"))\nfor chunk in response:\n    print(chunk, end=\"\", flush=True)\n\n```\n\n###  ⬛ `BlackBox` - Search/Chat with BlackBox\n\n```python\nfrom webscout import BLACKBOXAI\nfrom rich import print\n\nai = BLACKBOXAI(\n    is_conversation=True,\n    max_tokens=800,\n    timeout=30,\n    intro=None,\n    filepath=None,\n    update_file=True,\n    proxies={},\n    history_offset=10250,\n    act=None,\n    model=None # You can specify a model if needed\n)\n\n\n# Define a prompt to send to the AI\nprompt = \"Tell me about india\"\n# Use the 'chat' method to send the prompt and receive a response\nr = ai.chat(prompt)\nprint(r)\n```\n\n\n###  🤖 `Meta AI` - Chat with Meta AI\n\n```python\nfrom webscout import Meta\nfrom rich import print\n# **For unauthenticated usage**\nmeta_ai = Meta()\n\n# Simple text prompt\nresponse = meta_ai.chat(\"What is the capital of France?\")\nprint(response)\n\n# Streaming response\nfor chunk in meta_ai.chat(\"Tell me a story about a cat.\"):\n    print(chunk, end=\"\", flush=True)\n\n# **For authenticated usage (including image generation)**\nfb_email = \"abcd@abc.com\"\nfb_password = \"qwertfdsa\"\nmeta_ai = Meta(fb_email=fb_email, fb_password=fb_password)\n\n# Text prompt with web search\nresponse = meta_ai.ask(\"what is currently happning in bangladesh in aug 2024\")\nprint(response[\"message\"]) # Access the text message\nprint(\"Sources:\", response[\"sources\"]) # Access sources (if any)\n\n# Image generation\nresponse = meta_ai.ask(\"Create an image of a cat wearing a hat.\") \nprint(response[\"message\"]) # Print the text message from the response\nfor media in response[\"media\"]:\n    print(media[\"url\"])  # Access image URLs\n\n```\n\n###  `KOBOLDAI` \n\n```python\nfrom webscout import KOBOLDAI\n\n# Instantiate the KOBOLDAI class with default parameters\nkoboldai = KOBOLDAI()\n\n# Define a prompt to send to the AI\nprompt = \"What is the capital of France?\"\n\n# Use the 'ask' method to get a response from the AI\nresponse = koboldai.ask(prompt)\n\n# Extract and print the message from the response\nmessage = koboldai.get_message(response)\nprint(message)\n\n```\n\n###  `Reka` - Chat with Reka\n\n```python\nfrom webscout import REKA\n\na = REKA(is_conversation=True, max_tokens=8000, timeout=30,api_key=\"\")\n\nprompt = \"tell me about india\"\nresponse_str = a.chat(prompt)\nprint(response_str)\n```\n\n###  `Cohere` - Chat with Cohere\n\n```python\nfrom webscout import Cohere\n\na = Cohere(is_conversation=True, max_tokens=8000, timeout=30,api_key=\"\")\n\nprompt = \"tell me about india\"\nresponse_str = a.chat(prompt)\nprint(response_str)\n```\n\n###  `Deepinfra`\n\n```python\nfrom webscout import DeepInfra\n\nai = DeepInfra(\n    is_conversation=True,\n    model= \"Qwen/Qwen2-72B-Instruct\",\n    max_tokens=800,\n    timeout=30,\n    intro=None,\n    filepath=None,\n    update_file=True,\n    proxies={},\n    history_offset=10250,\n    act=None,\n)\n\nprompt = \"what is meaning of life\"\n\nresponse = ai.ask(prompt)\n\n# Extract and print the message from the response\nmessage = ai.get_message(response)\nprint(message)\n```\n\n\n###  `GROQ`\n\n```python\nfrom webscout import GROQ\nai = GROQ(api_key=\"\")\nresponse = ai.chat(\"What is the meaning of life?\")\nprint(response)\n#----------------------TOOL CALL------------------\nfrom webscout import GROQ  # Adjust import based on your project structure\nfrom webscout import WEBS\nimport json\n\n# Initialize the GROQ client\nclient = GROQ(api_key=\"\")\nMODEL = 'llama3-groq-70b-8192-tool-use-preview'\n\n# Function to evaluate a mathematical expression\ndef calculate(expression):\n    \"\"\"Evaluate a mathematical expression\"\"\"\n    try:\n        result = eval(expression)\n        return json.dumps({\"result\": result})\n    except Exception as e:\n        return json.dumps({\"error\": str(e)})\n\n# Function to perform a text search using DuckDuckGo.com\ndef search(query):\n    \"\"\"Perform a text search using DuckDuckGo.com\"\"\"\n    try:\n        results = WEBS().text(query, max_results=5)\n        return json.dumps({\"results\": results})\n    except Exception as e:\n        return json.dumps({\"error\": str(e)})\n\n# Add the functions to the provider\nclient.add_function(\"calculate\", calculate)\nclient.add_function(\"search\", search)\n\n# Define the tools\ntools = [\n    {\n        \"type\": \"function\",\n        \"function\": {\n            \"name\": \"calculate\",\n            \"description\": \"Evaluate a mathematical expression\",\n            \"parameters\": {\n                \"type\": \"object\",\n                \"properties\": {\n                    \"expression\": {\n                        \"type\": \"string\",\n                        \"description\": \"The mathematical expression to evaluate\",\n                    }\n                },\n                \"required\": [\"expression\"],\n            },\n        }\n    },\n    {\n        \"type\": \"function\",\n        \"function\": {\n            \"name\": \"search\",\n            \"description\": \"Perform a text search using DuckDuckGo.com and Yep.com\",\n            \"parameters\": {\n                \"type\": \"object\",\n                \"properties\": {\n                    \"query\": {\n                        \"type\": \"string\",\n                        \"description\": \"The search query to execute\",\n                    }\n                },\n                \"required\": [\"query\"],\n            },\n        }\n    }\n]\n\n\nuser_prompt_calculate = \"What is 25 * 4 + 10?\"\nresponse_calculate = client.chat(user_prompt_calculate, tools=tools)\nprint(response_calculate)\n\nuser_prompt_search = \"Find information on HelpingAI and who is its developer\"\nresponse_search = client.chat(user_prompt_search, tools=tools)\nprint(response_search)\n\n```\n\n###  `LLama 70b` - Chat with Meta's Llama 3 70b\n\n```python\n\nfrom webscout import LLAMA\n\nllama = LLAMA()\n\nr = llama.chat(\"What is the meaning of life?\")\nprint(r)\n```\n\n###  `AndiSearch`\n\n```python\nfrom webscout import AndiSearch\na = AndiSearch()\nprint(a.chat(\"HelpingAI-9B\"))\n```\n\n\n###  LLAMA3, pizzagpt, RUBIKSAI, Koala, Darkai, AI4Chat, Farfalle, PIAI, Felo, Julius, YouChat, YEPCHAT, Cloudflare, TurboSeek, Editee, AI21, Chatify, Cerebras, X0GPT, Lepton, GEMINIAPI, Cleeai, Elmo, Free2GPT, Bing, DiscordRocks, GPTWeb, LlamaTutor, PromptRefine, TutorAI, ChatGPTES, Bagoodex, ChatHub, AmigoChat, AIMathGPT, GaurishCerebras, NinjaChat, GeminiPro, Talkai, LLMChat, AskMyAI, Llama3Mitril, Marcus, TypeGPT, Mhystical, Netwrck, MultiChatAI, JadveOpenAI, ChatGLM, Deepfind, NousHermes, TextPollinationsAI, GliderAI, DGAFAI, ChatGPTGratis, QwenLM, IBMGranite, WiseCat, DeepSeek\n\nCode is similar to other providers.\n\n### `LLM`\n\n```python\nfrom webscout.LLM import LLM, VLM\n\n# Chat with text\nllm = LLM(\"meta-llama/Meta-Llama-3-70B-Instruct\")\nresponse = llm.chat([{\"role\": \"user\", \"content\": \"What's good?\"}])\n\n# Chat with images\nvlm = VLM(\"cogvlm-grounding-generalist\")\nresponse = vlm.chat([{\n    \"role\": \"user\",\n    \"content\": [\n        {\"type\": \"image\", \"image_url\": \"cool_pic.jpg\"},\n        {\"type\": \"text\", \"text\": \"What's in this image?\"}\n    ]\n}])\n```\n\n##  💻 Local-LLM\n\nWebscout can now run GGUF models locally. You can download and run your favorite models with minimal configuration.\n\n**Example:**\n\n```python\nfrom webscout.Local import *\nmodel_path = download_model(\"Qwen/Qwen2.5-0.5B-Instruct-GGUF\", \"qwen2.5-0.5b-instruct-q2_k.gguf\", token=None)\nmodel = Model(model_path, n_gpu_layers=0, context_length=2048)\nthread = Thread(model, format=chatml)\n# print(thread.send(\"hi\")) #send a single msg to ai\n\n# thread.interact() # interact with the model in terminal\n# start webui\n# webui = WebUI(thread)\n# webui.start(host=\"0.0.0.0\", port=8080, ssl=True) #Use ssl=True and make cert and key for https\n```\n\n## 🐶 Local-rawdog\n\nWebscout's local raw-dog feature allows you to run Python scripts within your terminal prompt.\n\n**Example:**\n\n```python\nimport webscout.Local as ws\nfrom webscout.Local.rawdog import RawDog\nfrom webscout.Local.samplers import DefaultSampling\nfrom webscout.Local.formats import chatml, AdvancedFormat\nfrom webscout.Local.utils import download_model\nimport datetime\nimport sys\nimport os\n\nrepo_id = \"YorkieOH10/granite-8b-code-instruct-Q8_0-GGUF\" \nfilename = \"granite-8b-code-instruct.Q8_0.gguf\"\nmodel_path = download_model(repo_id, filename, token='')\n\n# Load the model using the downloaded path\nmodel = ws.Model(model_path, n_gpu_layers=10)\n\nrawdog = RawDog()\n\n# Create an AdvancedFormat and modify the system content\n# Use a lambda to generate the prompt dynamically:\nchat_format = AdvancedFormat(chatml)\n#  **Pre-format the intro_prompt string:**\nsystem_content = f\"\"\"\nYou are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.\n\nA typical interaction goes like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n    i. Determine what needs to be done\n    ii. Write a short Python SCRIPT to do it\n    iii. Communicate back to the user by printing to the console in that SCRIPT\n3. The compiler extracts the script and then runs it using exec(). If there will be an exception raised,\n it will be send back to you starting with \"PREVIOUS SCRIPT EXCEPTION:\".\n4. In case of exception, regenerate error free script.\n\nIf you need to review script outputs before completing the task, you can print the word \"CONTINUE\" at the end of your SCRIPT.\nThis can be useful for summarizing documents or technical readouts, reading instructions before\ndeciding what to do, or other tasks that require multi-step reasoning.\nA typical 'CONTINUE' interaction looks like this:\n1. The user gives you a natural language PROMPT.\n2. You:\n    i. Determine what needs to be done\n    ii. Determine that you need to see the output of some subprocess call to complete the task\n    iii. Write a short Python SCRIPT to print that and then print the word \"CONTINUE\"\n3. The compiler\n    i. Checks and runs your SCRIPT\n    ii. Captures the output and appends it to the conversation as \"LAST SCRIPT OUTPUT:\"\n    iii. Finds the word \"CONTINUE\" and sends control back to you\n4. You again:\n    i. Look at the original PROMPT + the \"LAST SCRIPT OUTPUT:\" to determine what needs to be done\n    ii. Write a short Python SCRIPT to do it\n    iii. Communicate back to the user by printing to the console in that SCRIPT\n5. The compiler...\n\nPlease follow these conventions carefully:\n- Decline any tasks that seem dangerous, irreversible, or that you don't understand.\n- Always review the full conversation prior to answering and maintain continuity.\n- If asked for information, just print the information clearly and concisely.\n- If asked to do something, print a concise summary of what you've done as confirmation.\n- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.\n- If you need clarification, return a SCRIPT that prints your question. In the next interaction, continue based on the user's response.\n- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.\n- Actively clean up any temporary processes or files you use.\n- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.\n- You can plot anything with matplotlib.\n- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.\n\"\"\"\nchat_format.override('system_content', lambda: system_content)\n\nthread = ws.Thread(model, format=chat_format, sampler=DefaultSampling)\n\nwhile True:\n    prompt = input(\"\u003e: \")\n    if prompt.lower() == \"q\":\n        break\n\n    response = thread.send(prompt)\n\n    # Process the response using RawDog\n    script_output = rawdog.main(response)\n\n    if script_output:\n        print(script_output)\n\n```\n\n##  GGUF \n\nWebscout provides tools to convert and quantize Hugging Face models into the GGUF format for use with offline LLMs.\n\n**Example:**\n\n```python\nfrom webscout.Extra import gguf\n\"\"\"\nValid quantization methods:\n\"q2_k\", \"q3_k_l\", \"q3_k_m\", \"q3_k_s\", \n\"q4_0\", \"q4_1\", \"q4_k_m\", \"q4_k_s\", \n\"q5_0\", \"q5_1\", \"q5_k_m\", \"q5_k_s\", \n\"q6_k\", \"q8_0\"\n\"\"\"\ngguf.convert(\n    model_id=\"OEvortex/HelpingAI-Lite-1.5T\",  # Replace with your model ID\n    username=\"Abhaykoul\",  # Replace with your Hugging Face username\n    token=\"hf_token_write\",  # Replace with your Hugging Face token\n    quantization_methods=\"q4_k_m\"  # Optional, adjust quantization methods\n)\n```\n\n## 🤖 Autollama\n\nWebscout's `autollama` utility downloads a model from Hugging Face and then automatically makes it Ollama-ready.\n\n```python\nfrom webscout.Extra import autollama\n\nmodel_path = \"Vortex4ai/Jarvis-0.5B\"\ngguf_file = \"test2-q4_k_m.gguf\"\n\nautollama.main(model_path, gguf_file)  \n```\n\n**Command Line Usage:**\n\n* **GGUF Conversion:**\n   ```bash\n   python -m webscout.Extra.gguf -m \"OEvortex/HelpingAI-Lite-1.5T\" -u \"your_username\" -t \"your_hf_token\" -q \"q4_k_m,q5_k_m\" \n   ```\n\n* **Autollama:**\n   ```bash\n   python -m webscout.Extra.autollama -m \"OEvortex/HelpingAI-Lite-1.5T\" -g \"HelpingAI-Lite-1.5T.q4_k_m.gguf\" \n   ```\n\n**Note:** \n\n* Replace `\"your_username\"` and `\"your_hf_token\"` with your actual Hugging Face credentials.\n* The `model_path` in `autollama` is the Hugging Face model ID, and `gguf_file` is the GGUF file ID.\n\n\n\u003cdiv align=\"center\"\u003e\n  \u003c!-- Replace `#` with your actual links --\u003e\n  \u003ca href=\"https://t.me/official_helpingai\"\u003e\u003cimg alt=\"Telegram\" src=\"https://img.shields.io/badge/Telegram-2CA5E0?style=for-the-badge\u0026logo=telegram\u0026logoColor=white\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://www.instagram.com/oevortex/\"\u003e\u003cimg alt=\"Instagram\" src=\"https://img.shields.io/badge/Instagram-E4405F?style=for-the-badge\u0026logo=instagram\u0026logoColor=white\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://www.linkedin.com/in/oe-vortex-29a407265/\"\u003e\u003cimg alt=\"LinkedIn\" src=\"https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://buymeacoffee.com/oevortex\"\u003e\u003cimg alt=\"Buy Me A Coffee\" src=\"https://img.shields.io/badge/Buy%20Me%20A%20Coffee-FFDD00?style=for-the-badge\u0026logo=buymeacoffee\u0026logoColor=black\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n\u003cdiv align=\"center\"\u003e\n  \u003c!-- Replace `#` with your actual links --\u003e\n  \u003ca href=\"https://youtube.com/@OEvortex\"\u003e▶️ Vortex's YouTube Channel\u003c/a\u003e \n\u003c/div\u003e\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://youtube.com/@devsdocode\"\u003e▶️ Devs Do Code's YouTube Channel\u003c/a\u003e \n\u003c/div\u003e\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://t.me/ANONYMOUS_56788\"\u003e📢 Anonymous Coder's Telegram\u003c/a\u003e \n\u003c/div\u003e\n\n## 🤝 Contributing\n\nContributions are welcome! If you'd like to contribute to Webscout, please follow these steps:\n\n1. Fork the repository.\n2. Create a new branch for your feature or bug fix.\n3. Make your changes and commit them with descriptive messages.\n4. Push your branch to your forked repository.\n5. Submit a pull request to the main repository.\n\n\n## 🙏 Acknowledgments\n\n* All the amazing developers who have contributed to the project!\n* The open-source community for their support and inspiration.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FOE-LUCIFER%2FWebscout","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FOE-LUCIFER%2FWebscout","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FOE-LUCIFER%2FWebscout/lists"}