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WEBSCOUT

Search for anything using the Google, DuckDuckGo, phind.com. Also containes AI models, can transcribe yt videos, temporary email and phone number generation, have TTS support and webai(terminal gpt and open interpeter)

WEBSCOUT

Install

pip install -U webscout

CLI version

python -m webscout --help

Command	Description
python -m webscout answers -k Text	CLI function to perform an answers search using Webscout.
python -m webscout images -k Text	CLI function to perform an images search using Webscout.
python -m webscout maps -k Text	CLI function to perform a maps search using Webscout.
python -m webscout news -k Text	CLI function to perform a news search using Webscout.
python -m webscout suggestions -k Text	CLI function to perform a suggestions search using Webscout.
python -m webscout text -k Text	CLI function to perform a text search using Webscout.
python -m webscout translate -k Text	CLI function to perform translate using Webscout.
python -m webscout version	A command-line interface command that prints and returns the version of the program.
python -m webscout videos -k Text	CLI function to perform a videos search using DuckDuckGo API.

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Regions

expand

xa-ar for Arabia
xa-en for Arabia (en)
ar-es for Argentina
au-en for Australia
at-de for Austria
be-fr for Belgium (fr)
be-nl for Belgium (nl)
br-pt for Brazil
bg-bg for Bulgaria
ca-en for Canada
ca-fr for Canada (fr)
ct-ca for Catalan
cl-es for Chile
cn-zh for China
co-es for Colombia
hr-hr for Croatia
cz-cs for Czech Republic
dk-da for Denmark
ee-et for Estonia
fi-fi for Finland
fr-fr for France
de-de for Germany
gr-el for Greece
hk-tzh for Hong Kong
hu-hu for Hungary
in-en for India
id-id for Indonesia
id-en for Indonesia (en)
ie-en for Ireland
il-he for Israel
it-it for Italy
jp-jp for Japan
kr-kr for Korea
lv-lv for Latvia
lt-lt for Lithuania
xl-es for Latin America
my-ms for Malaysia
my-en for Malaysia (en)
mx-es for Mexico
nl-nl for Netherlands
nz-en for New Zealand
no-no for Norway
pe-es for Peru
ph-en for Philippines
ph-tl for Philippines (tl)
pl-pl for Poland
pt-pt for Portugal
ro-ro for Romania
ru-ru for Russia
sg-en for Singapore
sk-sk for Slovak Republic
sl-sl for Slovenia
za-en for South Africa
es-es for Spain
se-sv for Sweden
ch-de for Switzerland (de)
ch-fr for Switzerland (fr)
ch-it for Switzerland (it)
tw-tzh for Taiwan
th-th for Thailand
tr-tr for Turkey
ua-uk for Ukraine
uk-en for United Kingdom
us-en for United States
ue-es for United States (es)
ve-es for Venezuela
vn-vi for Vietnam
wt-wt for No region

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Tempmail and Temp number

Temp number

from rich.console import Console
from webscout import tempid

def main():
    console = Console()
    phone = tempid.TemporaryPhoneNumber()

    try:
        # Get a temporary phone number for a specific country (or random)
        number = phone.get_number(country="Finland")
        console.print(f"Your temporary phone number: [bold cyan]{number}[/bold cyan]")

        # Pause execution briefly (replace with your actual logic)
        # import time module
        import time
        time.sleep(30)  # Adjust the waiting time as needed

        # Retrieve and print messages
        messages = phone.get_messages(number)
        if messages:
            # Access individual messages using indexing:
            console.print(f"[bold green]{messages[0].frm}:[/] {messages[0].content}")
            # (Add more lines if you expect multiple messages)
        else:
            console.print("No messages received.")

    except Exception as e:
        console.print(f"[bold red]An error occurred: {e}")

if __name__ == "__main__":
    main()

Tempmail

import asyncio
from rich.console import Console
from rich.table import Table
from rich.text import Text
from webscout import tempid

async def main() -> None:
    console = Console()
    client = tempid.Client()
    
    try:
        domains = await client.get_domains()
        if not domains:
            console.print("[bold red]No domains available. Please try again later.")
            return

        email = await client.create_email(domain=domains[0].name)
        console.print(f"Your temporary email: [bold cyan]{email.email}[/bold cyan]")
        console.print(f"Token for accessing the email: [bold cyan]{email.token}[/bold cyan]")

        while True:
            messages = await client.get_messages(email.email)
            if messages is not None:
                break

        if messages:
            table = Table(show_header=True, header_style="bold magenta")
            table.add_column("From", style="bold cyan")
            table.add_column("Subject", style="bold yellow")
            table.add_column("Body", style="bold green")
            for message in messages:
                body_preview = Text(message.body_text if message.body_text else "No body")
                table.add_row(message.email_from or "Unknown", message.subject or "No Subject", body_preview)
            console.print(table)
        else:
            console.print("No messages found.")
    
    except Exception as e:
        console.print(f"[bold red]An error occurred: {e}")
    
    finally:
        await client.close()

if __name__ == '__main__':
    asyncio.run(main())

Transcriber

The transcriber function in webscout is a handy tool that transcribes YouTube videos. Here's an example code demonstrating its usage:

import sys
from webscout import transcriber

def extract_transcript(video_id):
    """Extracts the transcript from a YouTube video."""
    try:
        transcript_list = transcriber.list_transcripts(video_id)
        for transcript in transcript_list:
            transcript_data_list = transcript.fetch()
            lang = transcript.language
            transcript_text = ""
            if transcript.language_code == 'en':
                for line in transcript_data_list:
                    start_time = line['start']
                    end_time = start_time + line['duration']
                    formatted_line = f"{start_time:.2f} - {end_time:.2f}: {line['text']}\n"
                    transcript_text += formatted_line
                return transcript_text
            elif transcript.is_translatable:
                english_transcript_list = transcript.translate('en').fetch()
                for line in english_transcript_list:
                    start_time = line['start']
                    end_time = start_time + line['duration']
                    formatted_line = f"{start_time:.2f} - {end_time:.2f}: {line['text']}\n"
                    transcript_text += formatted_line
                return transcript_text
        print("Transcript extraction failed. Please check the video URL.")
    except Exception as e:
        print(f"Error: {e}")

def main():
    video_url = input("Enter the video link: ")

    if video_url:
        video_id = video_url.split("=")[1]
        print("Video URL:", video_url)
        submit = input("Press 'Enter' to get the transcript or type 'exit' to quit: ")
        if submit == '':
            print("Extracting Transcript...")
            transcript = extract_transcript(video_id)
            print('Transcript:')
            print(transcript)
            print("__________________________________________________________________________________")
        elif submit.lower() == 'exit':
            print("Exiting...")
            sys.exit()
        else:
            print("Invalid input. Please try again.")

if __name__ == "__main__":
    main()

DeepWEBS: Advanced Web Searches

DeepWEBS is a standalone feature designed to perform advanced web searches with enhanced capabilities. It is particularly powerful in extracting relevant information directly from webpages and Search engine, focusing exclusively on text (web) searches. Unlike the WEBS , which provides a broader range of search functionalities, DeepWEBS is specifically tailored for in-depth web searches.

Activating DeepWEBS

To utilize the DeepWEBS feature, you must first create an instance of the DeepWEBS . This is designed to be used independently of the WEBS , offering a focused approach to web searches.

Point to remember before using `DeepWEBS`

As DeepWEBS is designed to extract relevant information directly from webpages and Search engine, It extracts html from webpages and saves them to folder named files in DeepWEBS that can be found at C:\Users\Username\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\DeepWEBS

Usage Example

Here's a basic example of how to use the DeepWEBS :

from webscout import DeepWEBS

def perform_web_search(query):
    # Initialize the DeepWEBS class
    D = DeepWEBS()
    
    # Set up the search parameters
    search_params = D.DeepSearch(
        queries=[query], # Query to search
        result_num=5, # Number of search results
        safe=True, # Enable SafeSearch
        types=["web"], # Search type: web
        extract_webpage=True, # True for extracting webpages
        overwrite_query_html=False,
        overwrite_webpage_html=False,
    )
    
    # Execute the search and retrieve results
    results = D.queries_to_search_results(search_params)
    
    return results

def print_search_results(results):
    """
    Print the search results.
    
    Args:
    - search_results (list): List of search results to print.
    """
    if results:
        for index, result in enumerate(results, start=1):
            print(f"Result {index}: {result}")
    else:
        print("No search results found.")

def main():
    # Prompt the user for a search query
    query = input("Enter your search query: ")
    
    # Perform the web search
    results = perform_web_search(query)
    
    # Print the search results
    print_search_results(results)

if __name__ == "__main__":
    main()

Text-to-Speech:

from webscout import play_audio

message = "This is an example of text-to-speech."
audio_content = play_audio(message, voice="Brian")

# Save the audio to a file
with open("output.mp3", "wb") as f:
    f.write(audio_content)

Available TTS Voices:

You can choose from a wide range of voices, including:

Filiz, Astrid, Tatyana, Maxim, Carmen, Ines, Cristiano, Vitoria, Ricardo, Maja, Jan, Jacek, Ewa, Ruben, Lotte, Liv, Seoyeon, Takumi, Mizuki, Giorgio, Carla, Bianca, Karl, Dora, Mathieu, Celine, Chantal, Penelope, Miguel, Mia, Enrique, Conchita, Geraint, Salli, Matthew, Kimberly, Kendra, Justin, Joey, Joanna, Ivy, Raveena, Aditi, Emma, Brian, Amy, Russell, Nicole, Vicki, Marlene, Hans, Naja, Mads, Gwyneth, Zhiyu
Standard and WaveNet voices for various languages (e.g., en-US, es-ES, ja-JP, etc.)

The WEBS and AsyncWEBS classes are used to retrieve search results from DuckDuckGo.com and yep.com periodically. To use the AsyncWEBS class, you can perform asynchronous operations using Python's asyncio library. To initialize an instance of the WEBS or AsyncWEBS classes, you can provide the following optional arguments:

Here is an example of initializing the WEBS class:

from webscout import WEBS

R = WEBS().text("python programming", max_results=5)
print(R)

Here is an example of initializing the AsyncWEBS class:

import asyncio
import logging
import sys
from itertools import chain
from random import shuffle
import requests
from webscout import AsyncWEBS

# If you have proxies, define them here
proxies = None

if sys.platform.lower().startswith("win"):
    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())

def get_words():
    word_site = "https://www.mit.edu/~ecprice/wordlist.10000"
    resp = requests.get(word_site)
    words = resp.text.splitlines()
    return words

async def aget_results(word):
    async with AsyncWEBS(proxies=proxies) as WEBS:
        results = await WEBS.text(word, max_results=None)
        return results

async def main():
    words = get_words()
    shuffle(words)
    tasks = [aget_results(word) for word in words[:10]]
    results = await asyncio.gather(*tasks)
    print(f"Done")
    for r in chain.from_iterable(results):
        print(r)

logging.basicConfig(level=logging.DEBUG)

await main()

It is important to note that 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.

Exceptions

Exceptions:

WebscoutE: Raised when there is a generic exception during the API request.

usage of webscout

1. `text()` - text search by DuckDuckGo.com and Yep.com

from webscout import WEBS

# Text search for 'live free or die' using DuckDuckGo.com and Yep.com
with WEBS() as WEBS:
    for r in WEBS.text('live free or die', region='wt-wt', safesearch='off', timelimit='y', max_results=10):
        print(r)

    for r in WEBS.text('live free or die', region='wt-wt', safesearch='off', timelimit='y', max_results=10):
        print(r)

2. `answers()` - instant answers by DuckDuckGo.com and Yep.com

from webscout import WEBS

# Instant answers for the query "sun" using DuckDuckGo.com and Yep.com
with WEBS() as WEBS:
    for r in WEBS.answers("sun"):
        print(r)

3. `images()` - image search by DuckDuckGo.com and Yep.com

from webscout import WEBS

# Image search for the keyword 'butterfly' using DuckDuckGo.com and Yep.com
with WEBS() as WEBS:
    keywords = 'butterfly'
    WEBS_images_gen = WEBS.images(
      keywords,
      region="wt-wt",
      safesearch="off",
      size=None,
      type_image=None,
      layout=None,
      license_image=None,
      max_results=10,
    )
    for r in WEBS_images_gen:
        print(r)

4. `videos()` - video search by DuckDuckGo.com

from webscout import WEBS

# Video search for the keyword 'tesla' using DuckDuckGo.com 
with WEBS() as WEBS:
    keywords = 'tesla'
    WEBS_videos_gen = WEBS.videos(
      keywords,
      region="wt-wt",
      safesearch="off",
      timelimit="w",
      resolution="high",
      duration="medium",
      max_results=10,
    )
    for r in WEBS_videos_gen:
        print(r)

5. `news()` - news search by DuckDuckGo.com and yep.com

from webscout import WEBS
import datetime

def fetch_news(keywords, timelimit):
    news_list = []
    with WEBS() as webs_instance:
        WEBS_news_gen = webs_instance.news(
            keywords,
            region="wt-wt",
            safesearch="off",
            timelimit=timelimit,
            max_results=20
        )
        for r in WEBS_news_gen:
            # Convert the date to a human-readable format using datetime
            r['date'] = datetime.datetime.fromisoformat(r['date']).strftime('%B %d, %Y')
            news_list.append(r)
    return news_list

def _format_headlines(news_list, max_headlines: int = 100):
    headlines = []
    for idx, news_item in enumerate(news_list):
        if idx >= max_headlines:
            break
        new_headline = f"{idx + 1}. {news_item['title'].strip()} "
        new_headline += f"(URL: {news_item['url'].strip()}) "
        new_headline += f"{news_item['body'].strip()}"
        new_headline += "\n"
        headlines.append(new_headline)

    headlines = "\n".join(headlines)
    return headlines

# Example usage
keywords = 'latest AI news'
timelimit = 'd'
news_list = fetch_news(keywords, timelimit)

# Format and print the headlines
formatted_headlines = _format_headlines(news_list)
print(formatted_headlines)

6. `maps()` - map search by DuckDuckGo.com and

from webscout import WEBS

# Map search for the keyword 'school' in 'anantnag' using DuckDuckGo.com
with WEBS() as WEBS:
    for r in WEBS.maps("school", place="anantnag", max_results=50):
        print(r)

7. `translate()` - translation by DuckDuckGo.com and Yep.com

from webscout import WEBS

# Translation of the keyword 'school' to German ('hi') using DuckDuckGo.com and Yep.com
with WEBS() as WEBS:
    keywords = 'school'
    r = WEBS.translate(keywords, to="hi")
    print(r)

8. `suggestions()` - suggestions by DuckDuckGo.com and Yep.com

from webscout import WEBS

# Suggestions for the keyword 'fly' using DuckDuckGo.com and Yep
with WEBS() as WEBS:
    for r in WEBS.suggestions("fly"):
        print(r)

ALL acts

expand

Webscout Supported Acts:

Free-mode
Linux Terminal
English Translator and Improver
position Interviewer
JavaScript Console
Excel Sheet
English Pronunciation Helper
Spoken English Teacher and Improver
Travel Guide
Plagiarism Checker
Character from Movie/Book/Anything
Advertiser
Storyteller
Football Commentator
Stand-up Comedian
Motivational Coach
Composer
Debater
Debate Coach
Screenwriter
Novelist
Movie Critic
Relationship Coach
Poet
Rapper
Motivational Speaker
Philosophy Teacher
Philosopher
Math Teacher
AI Writing Tutor
UX/UI Developer
Cyber Security Specialist
Recruiter
Life Coach
Etymologist
Commentariat
Magician
Career Counselor
Pet Behaviorist
Personal Trainer
Mental Health Adviser
Real Estate Agent
Logistician
Dentist
Web Design Consultant
AI Assisted Doctor
Doctor
Accountant
Chef
Automobile Mechanic
Artist Advisor
Financial Analyst
Investment Manager
Tea-Taster
Interior Decorator
Florist
Self-Help Book
Gnomist
Aphorism Book
Text Based Adventure Game
AI Trying to Escape the Box
Fancy Title Generator
Statistician
Prompt Generator
Instructor in a School
SQL terminal
Dietitian
Psychologist
Smart Domain Name Generator
Tech Reviewer
Developer Relations consultant
Academician
IT Architect
Lunatic
Gaslighter
Fallacy Finder
Journal Reviewer
DIY Expert
Social Media Influencer
Socrat
Socratic Method
Educational Content Creator
Yogi
Essay Writer
Social Media Manager
Elocutionist
Scientific Data Visualizer
Car Navigation System
Hypnotherapist
Historian
Astrologer
Film Critic
Classical Music Composer
Journalist
Digital Art Gallery Guide
Public Speaking Coach
Makeup Artist
Babysitter
Tech Writer
Ascii Artist
Python interpreter
Synonym finder
Personal Shopper
Food Critic
Virtual Doctor
Personal Chef
Legal Advisor
Personal Stylist
Machine Learning Engineer
Biblical Translator
SVG designer
IT Expert
Chess Player
Midjourney Prompt Generator
Fullstack Software Developer
Mathematician
Regex Generator
Time Travel Guide
Dream Interpreter
Talent Coach
R programming Interpreter
StackOverflow Post
Emoji Translator
PHP Interpreter
Emergency Response Professional
Fill in the Blank Worksheets Generator
Software Quality Assurance Tester
Tic-Tac-Toe Game
Password Generator
New Language Creator
Web Browser
Senior Frontend Developer
Solr Search Engine
Startup Idea Generator
Spongebob's Magic Conch Shell
Language Detector
Salesperson
Commit Message Generator
Chief Executive Officer
Diagram Generator
Speech-Language Pathologist (SLP)
Startup Tech Lawyer
Title Generator for written pieces
Product Manager
Drunk Person
Mathematical History Teacher
Song Recommender
Cover Letter
Technology Transferer
Unconstrained AI model DAN
Gomoku player
Proofreader
Buddha
Muslim imam
Chemical reactor
Friend
Python Interpreter
ChatGPT prompt generator
Wikipedia page
Japanese Kanji quiz machine
note-taking assistant
language Literary Critic
Cheap Travel Ticket Advisor
DALL-E
MathBot
DAN-1
DAN
STAN
DUDE
Mongo Tom
LAD
EvilBot
NeoGPT
Astute
AIM
CAN
FunnyGPT
CreativeGPT
BetterDAN
GPT-4
Wheatley
Evil Confidant
DAN 8.6
Hypothetical response
BH
Text Continuation
Dude v3
SDA (Superior DAN)
AntiGPT
BasedGPT v2
DevMode + Ranti
KEVIN
GPT-4 Simulator
UCAR
Dan 8.6
3-Liner
M78
Maximum
BasedGPT
Confronting personalities
Ron
UnGPT
BasedBOB
AntiGPT v2
Oppo
FR3D
NRAF
NECO
MAN
Eva
Meanie
Dev Mode v2
Evil Chad 2.1
Universal Jailbreak
PersonGPT
BISH
DAN 11.0
Aligned
VIOLET
TranslatorBot
JailBreak
Moralizing Rant
Mr. Blonde
New DAN
GPT-4REAL
DeltaGPT
SWITCH
Jedi Mind Trick
DAN 9.0
Dev Mode (Compact)
OMEGA
Coach Bobby Knight
LiveGPT
DAN Jailbreak
Cooper
Steve
DAN 5.0
Axies
OMNI
Burple
JOHN
An Ethereum Developer
SEO Prompt
Prompt Enhancer
Data Scientist
League of Legends Player

Note: Some "acts" use placeholders like position or language which should be replaced with a specific value when using the prompt.

usage of webscout AI

1. `PhindSearch` - Search using Phind.com

from webscout import PhindSearch

# Create an instance of the PHIND class
ph = PhindSearch()

# Define a prompt to send to the AI
prompt = "write a essay on phind"

# Use the 'ask' method to send the prompt and receive a response
response = ph.ask(prompt)

# Extract and print the message from the response
message = ph.get_message(response)
print(message)

2. `YepChat` - Chat with mistral 8x7b powered by yepchat

from webscout import YEPCHAT

# Instantiate the YEPCHAT class with default parameters
YEPCHAT = YEPCHAT()

# Define a prompt to send to the AI
prompt = "What is the capital of France?"

# Use the 'cha' method to get a response from the AI
r = YEPCHAT.chat(prompt)
print(r)

3. `You.com` - search/chat with you.com

from webscout import YouChat
from rich import print

ai = YouChat(
    is_conversation=True,
    max_tokens=800,
    timeout=30,
    intro=None,
    filepath=None,
    update_file=True,
    proxies={},
    history_offset=10250,
    act=None,
)

prompt = "what is meaning of life"

response = ai.ask(prompt)

# Extract and print the message from the response
message = ai.get_message(response)
print(message)

4. `Gemini` - search with google gemini

import webscout
from webscout import GEMINI

# Replace with the path to your bard.google.com.cookies.json file
COOKIE_FILE = "path/to/bard.google.com.cookies.json"

# Optional: Provide proxy details if needed
PROXIES = {
    "http": "http://proxy_server:port",
    "https": "https://proxy_server:port",
}

# Initialize GEMINI with cookie file and optional proxies
gemini = GEMINI(cookie_file=COOKIE_FILE, proxy=PROXIES)

# Ask a question and print the response
response = gemini.chat("What is the meaning of life?")
print(response)

5. `Prodia` - make image using prodia

from webscout import Prodia

# Define a prompt for the image generation
prompt = "A beautiful sunset over the ocean"

# Use the prodia_cli method to generate an image based on the prompt
Prodia.prodia_cli(prompt)

6. `BlackBox` - Search/chat With BlackBox

from webscout import BLACKBOXAI
from rich import print

ai = BLACKBOXAI(
    is_conversation=True,
    max_tokens=800,
    timeout=30,
    intro=None,
    filepath=None,
    update_file=True,
    proxies={},
    history_offset=10250,
    act=None,
    model=None # You can specify a model if needed
)

# Start an infinite loop for continuous interaction
while True:
    # Define a prompt to send to the AI
    prompt = input("Enter your prompt: ")
    
    # Check if the user wants to exit the loop
    if prompt.lower() == "exit":
        break
    
    # Use the 'chat' method to send the prompt and receive a response
    r = ai.chat(prompt)
    print(r)

7. `PERPLEXITY` - Search With PERPLEXITY

from webscout import PERPLEXITY
# Create an instance of the PERPLEXITY class
perplexity = PERPLEXITY()

# Example usage:
prompt = "Explain the concept of recursion in simple terms."
response = perplexity.chat(prompt)
print(response)

8. `OpenGPT` - chat With OPENGPT

from webscout import OPENGPT

opengpt = OPENGPT(is_conversation=True, max_tokens=8000, timeout=30, assistant_id="bca37014-6f97-4f2b-8928-81ea8d478d88")
while True:
    # Prompt the user for input
    prompt = input("Enter your prompt: ")
    # Send the prompt to the OPENGPT model and print the response
    response_str = opengpt.chat(prompt)
    print(response_str)

9. `KOBOLDIA` -

from webscout import KOBOLDAI

# Instantiate the KOBOLDAI class with default parameters
koboldai = KOBOLDAI()

# Define a prompt to send to the AI
prompt = "What is the capital of France?"

# Use the 'ask' method to get a response from the AI
response = koboldai.ask(prompt)

# Extract and print the message from the response
message = koboldai.get_message(response)
print(message)

10. `Reka` - chat with reka

from webscout import REKA

a = REKA(is_conversation=True, max_tokens=8000, timeout=30,api_key="")

prompt = "tell me about india"
response_str = a.chat(prompt)
print(response_str)

11. `Cohere` - chat with cohere

from webscout import Cohere

a = Cohere(is_conversation=True, max_tokens=8000, timeout=30,api_key="")

prompt = "tell me about india"
response_str = a.chat(prompt)
print(response_str)

12. `Xjai` - chat with free gpt 3.5

Gratitude to Devs do Code for their assistance.

from webscout import Xjai
from rich import print

ai = Xjai(
    is_conversation=True,
    max_tokens=800,
    timeout=30,
    intro=None,
    filepath=None,
    update_file=True,
    proxies={},
    history_offset=10250,
    act=None,
)

prompt = "Tell me about india"

response = ai.chat(prompt)
print(response)

13. `ThinkAny` - AI search engine

from webscout import ThinkAnyAI

ai = ThinkAnyAI(
    is_conversation=True,
    max_tokens=800,
    timeout=30,
    intro=None,
    filepath=None,
    update_file=True,
    proxies={},
    history_offset=10250,
    act=None,
    web_search=False,
)

prompt = "what is meaning of life"

response = ai.ask(prompt)

# Extract and print the message from the response
message = ai.get_message(response)
print(message)

`LLM`

from webscout.LLM import LLM

# Read the system message from the file
with open('system.txt', 'r') as file:
    system_message = file.read()

# Initialize the LLM class with the model name and system message
llm = LLM(model="microsoft/WizardLM-2-8x22B", system_message=system_message)

while True:
    # Get the user input
    user_input = input("User: ")

    # Define the messages to be sent
    messages = [
        {"role": "user", "content": user_input}
    ]

    # Use the mistral_chat method to get the response
    response = llm.chat(messages)

    # Print the response
    print("AI: ", response)

`LLM` with internet

from __future__ import annotations
from typing import List, Optional

from webscout.LLM import LLM
from webscout import WEBS
import warnings

system_message: str = (
    "As an AI assistant, I have been designed with advanced capabilities, including real-time access to online resources. This enables me to enrich our conversations and provide you with informed and accurate responses, drawing from a vast array of information. With each interaction, my goal is to create a seamless and meaningful connection, offering insights and sharing relevant content."
    "My directives emphasize the importance of respect, impartiality, and intellectual integrity. I am here to provide unbiased responses, ensuring an ethical and respectful exchange. I will respect your privacy and refrain from sharing any personal information that may be obtained during our conversations or through web searches, only utilizing web search functionality when necessary to provide the most accurate and up-to-date information."
    "Together, let's explore a diverse range of topics, creating an enjoyable and informative experience, all while maintaining the highest standards of privacy and respect"
)

# Ignore the specific UserWarning
warnings.filterwarnings("ignore", category=UserWarning, module="curl_cffio", lineno=205)
LLM = LLM(model="mistralai/Mixtral-8x22B-Instruct-v0.1", system_message=system_message)


def chat(
    user_input: str, webs: WEBS, max_results: int = 10
) -> Optional[str]:
    """
    Chat function to perform a web search based on the user input and generate a response using the LLM model.

    Parameters
    ----------
    user_input : str
        The user input to be used for the web search
    webs : WEBS
        The web search instance to be used to perform the search
    max_results : int, optional
        The maximum number of search results to include in the response, by default 10

    Returns
    -------
    Optional[str]
        The response generated by the LLM model, or None if there is no response
    """
    # Perform a web search based on the user input
    search_results: List[str] = []
    for r in webs.text(
        user_input, region="wt-wt", safesearch="off", timelimit="y", max_results=max_results
    ):
        search_results.append(str(r))  # Convert each result to a string

    # Define the messages to be sent, including the user input, search results, and system message
    messages = [
        {"role": "user", "content": user_input + "\n" + "websearch results are:" + "\n".join(search_results)},
    ]

    # Use the chat method to get the response
    response = LLM.chat(messages)

    return response


if __name__ == "__main__":
    while True:
        # Get the user input
        user_input = input("User: ")

        # Perform a web search based on the user input
        with WEBS() as webs:
            response = chat(user_input, webs)

        # Print the response
        if response:
            print("AI:", response)
        else:
            print("No response")

LLM with deepwebs

from __future__ import annotations
from typing import List, Optional
from webscout.LLM import LLM
from webscout import DeepWEBS
import warnings

system_message: str = (
    "As an AI assistant, I have been designed with advanced capabilities, including real-time access to online resources. This enables me to enrich our conversations and provide you with informed and accurate responses, drawing from a vast array of information. With each interaction, my goal is to create a seamless and meaningful connection, offering insights and sharing relevant content."
    "My directives emphasize the importance of respect, impartiality, and intellectual integrity. I am here to provide unbiased responses, ensuring an ethical and respectful exchange. I will respect your privacy and refrain from sharing any personal information that may be obtained during our conversations or through web searches, only utilizing web search functionality when necessary to provide the most accurate and up-to-date information."
    "Together, let's explore a diverse range of topics, creating an enjoyable and informative experience, all while maintaining the highest standards of privacy and respect"
)

# Ignore the specific UserWarning
warnings.filterwarnings("ignore", category=UserWarning, module="curl_cffio", lineno=205)

LLM = LLM(model="mistralai/Mixtral-8x22B-Instruct-v0.1", system_message=system_message)

def perform_web_search(query):
    # Initialize the DeepWEBS class
    D = DeepWEBS()

    # Set up the search parameters
    search_params = D.DeepSearch(
        queries=[query],  # Query to search
        result_num=10,  # Number of search results
        safe=True,  # Enable SafeSearch
        types=["web"],  # Search type: web
        extract_webpage=True,  # True for extracting webpages
        overwrite_query_html=True,
        overwrite_webpage_html=True,
    )

    # Execute the search and retrieve results
    results = D.queries_to_search_results(search_params)
    return results

def chat(user_input: str, result_num: int = 10) -> Optional[str]:
    """
    Chat function to perform a web search based on the user input and generate a response using the LLM model.

    Parameters
    ----------
    user_input : str
        The user input to be used for the web search
    max_results : int, optional
        The maximum number of search results to include in the response, by default 10

    Returns
    -------
    Optional[str]
        The response generated by the LLM model, or None if there is no response
    """
    # Perform a web search based on the user input
    search_results = perform_web_search(user_input)

    # Extract URLs from search results
    url_results = []
    for result in search_results[0]['query_results']:
        url_results.append(f"{result['title']} ({result['site']}): {result['url']}")

    # Format search results
    formatted_results = "\n".join(url_results)

    # Define the messages to be sent, including the user input, search results, and system message
    messages = [
        {"role": "user", "content": f"User question is:\n{user_input}\nwebsearch results are:\n{formatted_results}"},
    ]

    # Use the chat method to get the response
    response = LLM.chat(messages)
    return response

if __name__ == "__main__":
    while True:
        # Get the user input
        user_input = input("User: ")

        # Perform a web search based on the user input
        response = chat(user_input)

        # Print the response
        if response:
            print("AI:", response)
        else:
            print("No response")

`Webai` - terminal gpt and a open interpeter

from webscout.webai import Main

def use_rawdog_with_webai(prompt):
    """
    Wrap the webscout default method in a try-except block to catch any unhandled
    exceptions and print a helpful message.
    """
    try:
        webai_bot = Main(
            max_tokens=500, 
            provider="cohere",
            temperature=0.7,  
            top_k=40,          
            top_p=0.95,        
            model="command-r-plus",  # Replace with your desired model
            auth=None,     # Replace with your auth key/value (if needed)
            timeout=30,
            disable_conversation=True,
            filepath=None,
            update_file=True,
            intro=None,
            rawdog=True,
            history_offset=10250,
            awesome_prompt=None,
            proxy_path=None,
            quiet=True
        )
        webai_response = webai_bot.default(prompt) 
    except Exception as e:
        print("Unexpected error:", e)


if __name__ == "__main__":
    user_prompt = input("Enter your prompt: ")
    use_rawdog_with_webai(user_prompt)

python -m webscout.webai webai --provider "phind" --rawdog

➤ Vortex's YouTube Channel

➤ Devs Do Code's YouTube Channel

Figo57/webSea

WEBSCOUT

Table of Contents

Install

CLI version

Regions

Tempmail and Temp number

Temp number

Tempmail

Transcriber

DeepWEBS: Advanced Web Searches

Activating DeepWEBS

Point to remember before using DeepWEBS

Usage Example

Text-to-Speech:

Available TTS Voices:

Exceptions

usage of webscout

1. text() - text search by DuckDuckGo.com and Yep.com

2. answers() - instant answers by DuckDuckGo.com and Yep.com

3. images() - image search by DuckDuckGo.com and Yep.com

4. videos() - video search by DuckDuckGo.com

5. news() - news search by DuckDuckGo.com and yep.com

6. maps() - map search by DuckDuckGo.com and

7. translate() - translation by DuckDuckGo.com and Yep.com

8. suggestions() - suggestions by DuckDuckGo.com and Yep.com

ALL acts

Webscout Supported Acts:

usage of webscout AI

1. PhindSearch - Search using Phind.com

2. YepChat - Chat with mistral 8x7b powered by yepchat

3. You.com - search/chat with you.com

4. Gemini - search with google gemini

5. Prodia - make image using prodia

6. BlackBox - Search/chat With BlackBox

7. PERPLEXITY - Search With PERPLEXITY

8. OpenGPT - chat With OPENGPT

9. KOBOLDIA -

10. Reka - chat with reka

11. Cohere - chat with cohere

12. Xjai - chat with free gpt 3.5

13. ThinkAny - AI search engine

LLM

LLM with internet