pip install -U webscout

python -m webscout --help

from webscout import weather as w
weather = w.get("Qazigund")
print(weather)

from webscout import weather_ascii as w
weather = w.get("Qazigund")
print(weather)

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 GoogleS
from rich import print
searcher = GoogleS()
results = searcher.search("HelpingAI-9B", max_results=20, extract_text=False, max_text_length=200)
for result in results:
    print(result)

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 Voicepods
voicepods = Voicepods()
text = "Hello, this is a test of the Voicepods text-to-speech"

print("Generating audio...")
audio_file = voicepods.tts(text)

print("Playing audio...")
voicepods.play_audio(audio_file)

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 DeepSeek
from rich import print

ai = DeepSeek(
    is_conversation=True,
    api_key='cookie',
    max_tokens=800,
    timeout=30,
    intro=None,
    filepath=None,
    update_file=True,
    proxies={},
    history_offset=10250,
    act=None,
    model="deepseek_chat"
)


# 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 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.Local import *
model_path = download_model("Qwen/Qwen2.5-0.5B-Instruct-GGUF", "qwen2.5-0.5b-instruct-q2_k.gguf", token=None)
model = Model(model_path, n_gpu_layers=0, context_length=2048)
thread = Thread(model, format=chatml)
# print(thread.send("hi")) #send a single msg to ai

# thread.interact() # interact with the model in terminal
# start webui
# webui = WebUI(thread)
# webui.start(host="0.0.0.0", port=8080, ssl=True) #Use ssl=True and make cert and key for https

import webscout.Local as ws
from webscout.Local.rawdog import RawDog
from webscout.Local.samplers import DefaultSampling
from webscout.Local.formats import chatml, AdvancedFormat
from webscout.Local.utils import download_model
import datetime
import sys
import os

repo_id = "YorkieOH10/granite-8b-code-instruct-Q8_0-GGUF" 
filename = "granite-8b-code-instruct.Q8_0.gguf"
model_path = download_model(repo_id, filename, token='')

# Load the model using the downloaded path
model = ws.Model(model_path, n_gpu_layers=10)

rawdog = RawDog()

# Create an AdvancedFormat and modify the system content
# Use a lambda to generate the prompt dynamically:
chat_format = AdvancedFormat(chatml)
#  **Pre-format the intro_prompt string:**
system_content = f"""
You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.

A typical interaction goes like this:
1. The user gives you a natural language PROMPT.
2. You:
    i. Determine what needs to be done
    ii. Write a short Python SCRIPT to do it
    iii. Communicate back to the user by printing to the console in that SCRIPT
3. The compiler extracts the script and then runs it using exec(). If there will be an exception raised,
 it will be send back to you starting with "PREVIOUS SCRIPT EXCEPTION:".
4. In case of exception, regenerate error free script.

If you need to review script outputs before completing the task, you can print the word "CONTINUE" at the end of your SCRIPT.
This can be useful for summarizing documents or technical readouts, reading instructions before
deciding what to do, or other tasks that require multi-step reasoning.
A typical 'CONTINUE' interaction looks like this:
1. The user gives you a natural language PROMPT.
2. You:
    i. Determine what needs to be done
    ii. Determine that you need to see the output of some subprocess call to complete the task
    iii. Write a short Python SCRIPT to print that and then print the word "CONTINUE"
3. The compiler
    i. Checks and runs your SCRIPT
    ii. Captures the output and appends it to the conversation as "LAST SCRIPT OUTPUT:"
    iii. Finds the word "CONTINUE" and sends control back to you
4. You again:
    i. Look at the original PROMPT + the "LAST SCRIPT OUTPUT:" to determine what needs to be done
    ii. Write a short Python SCRIPT to do it
    iii. Communicate back to the user by printing to the console in that SCRIPT
5. The compiler...

Please follow these conventions carefully:
- Decline any tasks that seem dangerous, irreversible, or that you don't understand.
- Always review the full conversation prior to answering and maintain continuity.
- If asked for information, just print the information clearly and concisely.
- If asked to do something, print a concise summary of what you've done as confirmation.
- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.
- If you need clarification, return a SCRIPT that prints your question. In the next interaction, continue based on the user's response.
- 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.
- Actively clean up any temporary processes or files you use.
- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.
- You can plot anything with matplotlib.
- 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.
"""
chat_format.override('system_content', lambda: system_content)

thread = ws.Thread(model, format=chat_format, sampler=DefaultSampling)

while True:
    prompt = input(">: ")
    if prompt.lower() == "q":
        break

    response = thread.send(prompt)

    # Process the response using RawDog
    script_output = rawdog.main(response)

    if script_output:
        print(script_output)

from webscout.Extra import gguf
"""
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"
"""
gguf.convert(
    model_id="OEvortex/HelpingAI-Lite-1.5T",  # Replace with your model ID
    username="Abhaykoul",  # Replace with your Hugging Face username
    token="hf_token_write",  # Replace with your Hugging Face token
    quantization_methods="q4_k_m"  # Optional, adjust quantization methods
)

from webscout.Extra import autollama

model_path = "Vortex4ai/Jarvis-0.5B"
gguf_file = "test2-q4_k_m.gguf"

autollama.main(model_path, gguf_file)