pip install -U webscout

python -m webscout --help

import json
import asyncio
from webscout import VNEngine
from webscout import TempMail

async def main():
    vn = VNEngine()
    countries = vn.get_online_countries()
    if countries:
        country = countries[0]['country']
        numbers = vn.get_country_numbers(country)
        if numbers:
            number = numbers[0]['full_number']
            inbox = vn.get_number_inbox(country, number)
            
            # Serialize inbox data to JSON string
            json_data = json.dumps(inbox, ensure_ascii=False, indent=4)
            
            # Print with UTF-8 encoding
            print(json_data)
    
    async with TempMail() as client:
        domains = await client.get_domains()
        print("Available Domains:", domains)
        email_response = await client.create_email(alias="testuser")
        print("Created Email:", email_response)
        messages = await client.get_messages(email_response.email)
        print("Messages:", messages)
        await client.delete_email(email_response.email, email_response.token)
        print("Email Deleted")

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

from webscout import YepSearch

# Initialize YepSearch
yep = YepSearch(
    timeout=20,  # Optional: Set custom timeout
    proxies=None,  # Optional: Use proxies
    verify=True   # Optional: SSL verification
)

# Text Search
text_results = yep.text(
    keywords="artificial intelligence",
    region="all",           # Optional: Region for results
    safesearch="moderate",  # Optional: "on", "moderate", "off"
    max_results=10          # Optional: Limit number of results
)
print(text_results)

# Image Search
image_results = yep.images(
    keywords="nature photography",
    region="all",
    safesearch="moderate",
    max_results=10
)
print(image_results)


# Suggestions
suggestions = yep.suggestions("hist")
print(suggestions)

from webscout import GoogleSearch

# Initialize GoogleSearch
google = GoogleSearch(
    timeout=10,  # Optional: Set custom timeout
    proxies=None,  # Optional: Use proxies
    verify=True   # Optional: SSL verification
)

# Text Search
text_results = google.text(
    keywords="artificial intelligence",
    region="us",           # Optional: Region for results
    safesearch="moderate",  # Optional: "on", "moderate", "off"
    max_results=10          # Optional: Limit number of results
)
for result in text_results:
    print(f"Title: {result.title}")
    print(f"URL: {result.url}")
    print(f"Description: {result.description}")
    print("---")

# News Search
news_results = google.news(
    keywords="technology trends",
    region="us",
    safesearch="moderate",
    max_results=5
)
for result in news_results:
    print(f"Title: {result.title}")
    print(f"URL: {result.url}")
    print(f"Description: {result.description}")
    print("---")

# Get search suggestions
suggestions = google.suggestions("how to")
print(suggestions)

# Legacy usage is still supported
from webscout import search
results = search("Python programming", num_results=5)
for url in results:
    print(url)

from webscout import WEBS

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

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()

from webscout import WEBS

# Text search for 'live free or die' using DuckDuckGo.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)

from webscout import WEBS

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

from webscout import WEBS

# Image search for the keyword 'butterfly' using DuckDuckGo.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)

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)

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)

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)

from webscout import WEBS

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

from webscout import WEBS

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

from webscout import WEBS

# Get weather information for a location using DuckDuckGo.com
with WEBS() as webs:
    weather_data = webs.weather("New York")
    print(weather_data)

from webscout import model
from rich import print

all_models = model.llm.list()
print("Available models:")
print(all_models)

from webscout import model
from rich import print

summary = model.llm.summary()
print("Summary of models:")
print(summary)

from webscout import model
from rich import print

provider_name = "PerplexityLabs"  # Example provider
available_models = model.llm.get(provider_name)

if isinstance(available_models, list):
    print(f"Available models for {provider_name}: {', '.join(available_models)}")
else:
    print(f"Available models for {provider_name}: {available_models}")

from webscout import model
from rich import print

all_voices = model.tts.list()
print("Available TTS voices:")
print(all_voices)

from webscout import model
from rich import print

summary = model.tts.summary()
print("Summary of TTS voices:")
print(summary)

from webscout import model
from rich import print

provider_name = "ElevenlabsTTS"  # Example provider
available_voices = model.tts.get(provider_name)

if isinstance(available_voices, list):
    print(f"Available voices for {provider_name}: {', '.join(available_voices)}")
elif isinstance(available_voices, dict):
    print(f"Available voices for {provider_name}:")
    for voice_name, voice_id in available_voices.items():
        print(f"  - {voice_name}: {voice_id}")
else:
    print(f"Available voices for {provider_name}: {available_voices}")

from webscout import WEBS as w
R = w().chat("Who are you", model='gpt-4o-mini') # mixtral-8x7b, llama-3.1-70b, claude-3-haiku, gpt-4o-mini
print(R)

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)

from webscout import Phindv2

# Create an instance of the PHIND class
ph = Phindv2()

# Define a prompt to send to the AI
prompt = ""

# 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)

import webscout
from webscout import GEMINI
from rich import print
COOKIE_FILE = "cookies.json"

# Optional: Provide proxy details if needed
PROXIES = {}

# 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("websearch about HelpingAI and who is its developer")
print(response)

from webscout import YEPCHAT
ai = YEPCHAT()
response = ai.chat(input(">>> "))
for chunk in response:
    print(chunk, end="", flush=True)

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
)


# Define a prompt to send to the AI
prompt = "Tell me about india"
# Use the 'chat' method to send the prompt and receive a response
r = ai.chat(prompt)
print(r)

from webscout import Meta
from rich import print
# **For unauthenticated usage**
meta_ai = Meta()

# Simple text prompt
response = meta_ai.chat("What is the capital of France?")
print(response)

# Streaming response
for chunk in meta_ai.chat("Tell me a story about a cat."):
    print(chunk, end="", flush=True)

# **For authenticated usage (including image generation)**
fb_email = "[email protected]"
fb_password = "qwertfdsa"
meta_ai = Meta(fb_email=fb_email, fb_password=fb_password)

# Text prompt with web search
response = meta_ai.ask("what is currently happning in bangladesh in aug 2024")
print(response["message"]) # Access the text message
print("Sources:", response["sources"]) # Access sources (if any)

# Image generation
response = meta_ai.ask("Create an image of a cat wearing a hat.") 
print(response["message"]) # Print the text message from the response
for media in response["media"]:
    print(media["url"])  # Access image URLs

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)

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)

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)

from webscout import DeepInfra

ai = DeepInfra(
    is_conversation=True,
    model= "Qwen/Qwen2-72B-Instruct",
    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)

from webscout import GROQ
ai = GROQ(api_key="")
response = ai.chat("What is the meaning of life?")
print(response)
#----------------------TOOL CALL------------------
from webscout import GROQ  # Adjust import based on your project structure
from webscout import WEBS
import json

# Initialize the GROQ client
client = GROQ(api_key="")
MODEL = 'llama3-groq-70b-8192-tool-use-preview'

# Function to evaluate a mathematical expression
def calculate(expression):
    """Evaluate a mathematical expression"""
    try:
        result = eval(expression)
        return json.dumps({"result": result})
    except Exception as e:
        return json.dumps({"error": str(e)})

# Function to perform a text search using DuckDuckGo.com
def search(query):
    """Perform a text search using DuckDuckGo.com"""
    try:
        results = WEBS().text(query, max_results=5)
        return json.dumps({"results": results})
    except Exception as e:
        return json.dumps({"error": str(e)})

# Add the functions to the provider
client.add_function("calculate", calculate)
client.add_function("search", search)

# Define the tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "calculate",
            "description": "Evaluate a mathematical expression",
            "parameters": {
                "type": "object",
                "properties": {
                    "expression": {
                        "type": "string",
                        "description": "The mathematical expression to evaluate",
                    }
                },
                "required": ["expression"],
            },
        }
    },
    {
        "type": "function",
        "function": {
            "name": "search",
            "description": "Perform a text search using DuckDuckGo.com and Yep.com",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "The search query to execute",
                    }
                },
                "required": ["query"],
            },
        }
    }
]


user_prompt_calculate = "What is 25 * 4 + 10?"
response_calculate = client.chat(user_prompt_calculate, tools=tools)
print(response_calculate)

user_prompt_search = "Find information on HelpingAI and who is its developer"
response_search = client.chat(user_prompt_search, tools=tools)
print(response_search)

from webscout import LLAMA

llama = LLAMA()

r = llama.chat("What is the meaning of life?")
print(r)

from webscout import AndiSearch
a = AndiSearch()
print(a.chat("HelpingAI-9B"))

from webscout.LLM import LLM, VLM

# Chat with text
llm = LLM("meta-llama/Meta-Llama-3-70B-Instruct")
response = llm.chat([{"role": "user", "content": "What's good?"}])

# Chat with images
vlm = VLM("cogvlm-grounding-generalist")
response = vlm.chat([{
    "role": "user",
    "content": [
        {"type": "image", "image_url": "cool_pic.jpg"},
        {"type": "text", "text": "What's in this image?"}
    ]
}])

from webscout.Extra.gguf import ModelConverter
"""
Valid quantization methods:
"q2_k", "q3_k_l", "q3_k_m", "q3_k_s", 
"q4_0", "q4_1", "q4_k_m", "q4_k_s", 
"q5_0", "q5_1", "q5_k_m", "q5_k_s", 
"q6_k", "q8_0"
"""
# Create a converter instance
converter = ModelConverter(
    model_id="prithivMLmods/QWQ-500M",
    quantization_methods="q2_k"
)

# Run the conversion
converter.convert()