Metadata-Version: 2.1
Name: webscout
Version: 8.2
Summary: Search for anything using Google, DuckDuckGo, phind.com, Contains AI models, can transcribe yt videos, temporary email and phone number generation, has TTS support, webai (terminal gpt and open interpreter) and offline LLMs and more
Author: OEvortex
Author-email: helpingai5@gmail.com
License: HelpingAI
Project-URL: Source, https://github.com/OE-LUCIFER/Webscout
Project-URL: Tracker, https://github.com/OE-LUCIFER/Webscout/issues
Project-URL: YouTube, https://youtube.com/@OEvortex
Keywords: search,ai,chatbot,llm,language-model,gpt,openai,gemini,claude,llama,search-engine,text-to-speech,tts,text-to-image,tti,weather,youtube,toolkit,utilities,web-search,duckduckgo,google,yep
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Science/Research
Classifier: License :: Other/Proprietary License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Internet :: WWW/HTTP :: Indexing/Search
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Communications
Classifier: Topic :: Utilities
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: Local
License-File: LICENSE.md

<div align="center">
  <a href="https://github.com/OEvortex/Webscout">
    <img src="https://img.shields.io/badge/WebScout-Ultimate%20Toolkit-blue?style=for-the-badge&logo=python&logoColor=white" alt="WebScout Logo">
  </a>

  <h1>Webscout</h1>

  <p><strong>Your All-in-One Python Toolkit for Web Search, AI Interaction, Digital Utilities, and More</strong></p>

  <p>
    Access diverse search engines, cutting-edge AI models, temporary communication tools, media utilities, developer helpers, and powerful CLI interfaces – all through one unified library.
  </p>

  <!-- Badges -->
  <p>
    <a href="https://pypi.org/project/webscout/"><img src="https://img.shields.io/pypi/v/webscout.svg?style=flat-square&logo=pypi&label=PyPI" alt="PyPI Version"></a>
    <a href="https://pepy.tech/project/webscout"><img src="https://static.pepy.tech/badge/webscout/month?style=flat-square" alt="Monthly Downloads"></a>
    <a href="https://pepy.tech/project/webscout"><img src="https://static.pepy.tech/badge/webscout?style=flat-square" alt="Total Downloads"></a>
    <a href="#"><img src="https://img.shields.io/pypi/pyversions/webscout?style=flat-square&logo=python" alt="Python Version"></a>
  </p>
</div>

> [!IMPORTANT]
> Webscout supports three types of compatibility:
> - **Native Compatibility:** Webscout's own native API for maximum flexibility
> - **OpenAI Compatibility:** Use providers with OpenAI-compatible interfaces
> - **Local LLM Compatibility:** Run local models with [Inferno](webscout/Local/README.md), an OpenAI-compatible server
>
> Choose the approach that best fits your needs! For OpenAI compatibility, check the [OpenAI Providers README](webscout/Provider/OPENAI/README.md).

> [!NOTE]
> Webscout supports over 90 AI providers including: LLAMA, C4ai, Venice, Copilot, HuggingFaceChat, PerplexityLabs, DeepSeek, WiseCat, GROQ, OPENAI, GEMINI, DeepInfra, Meta, YEPCHAT, TypeGPT, ChatGPTClone, ExaAI, Claude, Anthropic, Cloudflare, AI21, Cerebras, and many more. All providers follow similar usage patterns with consistent interfaces.

<div align="center">
  <!-- Social/Support Links -->
  <p>
    <a href="https://t.me/PyscoutAI"><img alt="Telegram Group" src="https://img.shields.io/badge/Telegram%20Group-2CA5E0?style=for-the-badge&logo=telegram&logoColor=white"></a>
    <a href="https://t.me/ANONYMOUS_56788"><img alt="Developer Telegram" src="https://img.shields.io/badge/Developer%20Contact-2CA5E0?style=for-the-badge&logo=telegram&logoColor=white"></a>
    <a href="https://youtube.com/@OEvortex"><img alt="YouTube" src="https://img.shields.io/badge/YouTube-FF0000?style=for-the-badge&logo=youtube&logoColor=white"></a>
    <a href="https://www.linkedin.com/in/oe-vortex-29a407265/"><img alt="LinkedIn" src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white"></a>
    <a href="https://www.instagram.com/oevortex/"><img alt="Instagram" src="https://img.shields.io/badge/Instagram-E4405F?style=for-the-badge&logo=instagram&logoColor=white"></a>
    <a href="https://buymeacoffee.com/oevortex"><img alt="Buy Me A Coffee" src="https://img.shields.io/badge/Buy%20Me%20A%20Coffee-FFDD00?style=for-the-badge&logo=buymeacoffee&logoColor=black"></a>
  </p>
</div>


## 🚀 Features

### Search & AI
* **Comprehensive Search:** Leverage Google, DuckDuckGo, and Yep for diverse search results
* **AI Powerhouse:** Access and interact with various AI models through three compatibility options:
  * **Native API:** Use Webscout's native interfaces for providers like OpenAI, Cohere, Gemini, and many more
  * **[OpenAI-Compatible Providers](webscout/Provider/OPENAI/README.md):** Seamlessly integrate with various AI providers using standardized OpenAI-compatible interfaces
  * **[Local LLMs with Inferno](webscout/Local/README.md):** Run local models with an OpenAI-compatible server
* **[AI Search](webscout/Provider/AISEARCH/README.md):** AI-powered search engines with advanced capabilities

### Media & Content Tools
* **[YouTube Toolkit](webscout/Extra/YTToolkit/README.md):** Advanced YouTube video and transcript management with multi-language support
* **[Text-to-Speech (TTS)](webscout/Provider/TTS/README.md):** Convert text into natural-sounding speech using multiple AI-powered providers
* **[Text-to-Image](webscout/Provider/TTI/README.md):** Generate high-quality images using a wide range of AI art providers
* **[Weather Tools](webscout/Extra/weather.md):** Retrieve detailed weather information for any location

### Developer Tools
* **[GitAPI](webscout/Extra/GitToolkit/gitapi):** Powerful GitHub data extraction toolkit without authentication requirements for public data
* **[SwiftCLI](webscout/swiftcli/Readme.md):** A powerful and elegant CLI framework for beautiful command-line interfaces
* **[LitPrinter](webscout/litprinter/Readme.md):** Styled console output with rich formatting and colors
* **[LitLogger](webscout/litlogger/Readme.md):** Simplified logging with customizable formats and color schemes
* **[LitAgent](webscout/litagent/Readme.md):** Modern user agent generator that keeps your requests undetectable
* **[Scout](webscout/scout/README.md):** Advanced web parsing and crawling library with intelligent HTML/XML parsing
* **[Inferno](webscout/Local/README.md):** Run local LLMs with an OpenAI-compatible API and interactive CLI
* **GGUF Conversion:** Convert and quantize Hugging Face models to GGUF format

### Privacy & Utilities
* **[Tempmail](webscout/Extra/tempmail/README.md) & Temp Number:** Generate temporary email addresses and phone numbers
* **[Awesome Prompts](webscout/Extra/Act.md):** Curated collection of system prompts for specialized AI personas

## ⚙️ Installation

Install Webscout using pip:

```bash
pip install -U webscout
```

## 🖥️ Command Line Interface

Webscout provides a powerful command-line interface for quick access to its features:

```bash
python -m webscout --help
```

### Web Search Commands

| Command | Description |
|---------|-------------|
| `python -m webscout answers -k "query"` | Perform an answers search |
| `python -m webscout chat` | Start an interactive AI chat session |
| `python -m webscout images -k "query"` | Search for images |
| `python -m webscout maps -k "query"` | Perform a maps search |
| `python -m webscout news -k "query"` | Search for news articles |
| `python -m webscout suggestions -k "query"` | Get search suggestions |
| `python -m webscout text -k "query"` | Perform a text search |
| `python -m webscout translate -k "text"` | Translate text |
| `python -m webscout version` | Display the current version |
| `python -m webscout videos -k "query"` | Search for videos |
| `python -m webscout weather -l "location"` | Get weather information |

### Inferno LLM Commands

Inferno provides commands for managing and using local LLMs:

```bash
python -m inferno --help
```

| Command | Description |
|---------|-------------|
| `python -m inferno pull <model>` | Download a model from Hugging Face |
| `python -m inferno list` | List downloaded models |
| `python -m inferno serve <model>` | Start a model server with OpenAI-compatible API |
| `python -m inferno run <model>` | Chat with a model interactively |
| `python -m inferno remove <model>` | Remove a downloaded model |
| `python -m inferno version` | Show version information |

> [!NOTE]
> Hardware requirements for running models:
> - Around 2 GB of RAM for 1B models
> - Around 4 GB of RAM for 3B models
> - At least 8 GB of RAM for 7B models
> - 16 GB of RAM for 13B models
> - 32 GB of RAM for 33B models
> - GPU acceleration is recommended for better performance



## 🔍 Search Engines

Webscout provides multiple search engine interfaces for diverse search capabilities.

### YepSearch - Yep.com Interface

```python
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
)

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

# Get search suggestions
suggestions = yep.suggestions("hist")
```

### GoogleSearch - Google Interface

```python
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}")

# News Search
news_results = google.news(
    keywords="technology trends",
    region="us",
    safesearch="moderate",
    max_results=5
)

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

# Legacy usage is still supported
from webscout import search
results = search("Python programming", num_results=5)
```

## 🦆 DuckDuckGo Search with WEBS and AsyncWEBS

Webscout provides powerful interfaces to DuckDuckGo's search capabilities through the `WEBS` and `AsyncWEBS` classes.

### Synchronous Usage with WEBS

```python
from webscout import WEBS

# Use as a context manager for proper resource management
with WEBS() as webs:
    # Simple text search
    results = webs.text("python programming", max_results=5)
    for result in results:
        print(f"Title: {result['title']}\nURL: {result['url']}")
```

### Asynchronous Usage with AsyncWEBS

```python
import asyncio
from webscout import AsyncWEBS

async def search_multiple_terms(search_terms):
    async with AsyncWEBS() as webs:
        # Create tasks for each search term
        tasks = [webs.text(term, max_results=5) for term in search_terms]
        # Run all searches concurrently
        results = await asyncio.gather(*tasks)
        return results

async def main():
    terms = ["python", "javascript", "machine learning"]
    all_results = await search_multiple_terms(terms)

    # Process results
    for i, term_results in enumerate(all_results):
        print(f"Results for '{terms[i]}':\n")
        for result in term_results:
            print(f"- {result['title']}")
        print("\n")

# Run the async function
asyncio.run(main())
```

> [!NOTE]
> Always use these classes with a context manager (`with` statement) to ensure proper resource management and cleanup.

## 💻 WEBS API Reference

The WEBS class provides comprehensive access to DuckDuckGo's search capabilities through a clean, intuitive API.

### Available Search Methods

| Method | Description | Example |
|--------|-------------|--------|
| `text()` | General web search | `webs.text('python programming')` |
| `answers()` | Instant answers | `webs.answers('population of france')` |
| `images()` | Image search | `webs.images('nature photography')` |
| `videos()` | Video search | `webs.videos('documentary')` |
| `news()` | News articles | `webs.news('technology')` |
| `maps()` | Location search | `webs.maps('restaurants', place='new york')` |
| `translate()` | Text translation | `webs.translate('hello', to='es')` |
| `suggestions()` | Search suggestions | `webs.suggestions('how to')` |
| `weather()` | Weather information | `webs.weather('london')` |

### Example: Text Search

```python
from webscout import WEBS

with WEBS() as webs:
    results = webs.text(
        'artificial intelligence',
        region='wt-wt',        # Optional: Region for results
        safesearch='off',      # Optional: 'on', 'moderate', 'off'
        timelimit='y',         # Optional: Time limit ('d'=day, 'w'=week, 'm'=month, 'y'=year)
        max_results=10         # Optional: Limit number of results
    )

    for result in results:
        print(f"Title: {result['title']}")
        print(f"URL: {result['url']}")
        print(f"Description: {result['body']}\n")
```

### Example: News Search with Formatting

```python
from webscout import WEBS
import datetime

def fetch_formatted_news(keywords, timelimit='d', max_results=20):
    """Fetch and format news articles"""
    with WEBS() as webs:
        # Get news results
        news_results = webs.news(
            keywords,
            region="wt-wt",
            safesearch="off",
            timelimit=timelimit,  # 'd'=day, 'w'=week, 'm'=month
            max_results=max_results
        )

        # Format the results
        formatted_news = []
        for i, item in enumerate(news_results, 1):
            # Format the date
            date = datetime.datetime.fromisoformat(item['date']).strftime('%B %d, %Y')

            # Create formatted entry
            entry = f"{i}. {item['title']}\n"
            entry += f"   Published: {date}\n"
            entry += f"   {item['body']}\n"
            entry += f"   URL: {item['url']}\n"

            formatted_news.append(entry)

        return formatted_news

# Example usage
news = fetch_formatted_news('artificial intelligence', timelimit='w', max_results=5)
print('\n'.join(news))
```

### Example: Weather Information

```python
from webscout import WEBS

with WEBS() as webs:
    # Get weather for a location
    weather = webs.weather("New York")

    # Access weather data
    if weather:
        print(f"Location: {weather.get('location', 'Unknown')}")
        print(f"Temperature: {weather.get('temperature', 'N/A')}")
        print(f"Conditions: {weather.get('condition', 'N/A')}")
```

## 🤖 AI Models and Voices

Webscout provides easy access to a wide range of AI models and voice options.

### LLM Models

Access and manage Large Language Models with Webscout's model utilities.

```python
from webscout import model
from rich import print

# List all available LLM models
all_models = model.llm.list()
print(f"Total available models: {len(all_models)}")

# Get a summary of models by provider
summary = model.llm.summary()
print("Models by provider:")
for provider, count in summary.items():
    print(f"  {provider}: {count} models")

# Get models for a specific provider
provider_name = "PerplexityLabs"
available_models = model.llm.get(provider_name)
print(f"\n{provider_name} models:")
if isinstance(available_models, list):
    for i, model_name in enumerate(available_models, 1):
        print(f"  {i}. {model_name}")
else:
    print(f"  {available_models}")
```

### TTS Voices

Access and manage Text-to-Speech voices across multiple providers.

```python
from webscout import model
from rich import print

# List all available TTS voices
all_voices = model.tts.list()
print(f"Total available voices: {len(all_voices)}")

# Get a summary of voices by provider
summary = model.tts.summary()
print("\nVoices by provider:")
for provider, count in summary.items():
    print(f"  {provider}: {count} voices")

# Get voices for a specific provider
provider_name = "ElevenlabsTTS"
available_voices = model.tts.get(provider_name)
print(f"\n{provider_name} voices:")
if isinstance(available_voices, dict):
    for voice_name, voice_id in list(available_voices.items())[:5]:  # Show first 5 voices
        print(f"  - {voice_name}: {voice_id}")
    if len(available_voices) > 5:
        print(f"  ... and {len(available_voices) - 5} more")
```

## 💬 AI Chat Providers

Webscout offers a comprehensive collection of AI chat providers, giving you access to various language models through a consistent interface.

### Popular AI Providers

| Provider | Description | Key Features |
|----------|-------------|-------------|
| `OPENAI` | OpenAI's models | GPT-3.5, GPT-4, tool calling |
| `GEMINI` | Google's Gemini models | Web search capabilities |
| `Meta` | Meta's AI assistant | Image generation, web search |
| `GROQ` | Fast inference platform | High-speed inference, tool calling |
| `LLAMA` | Meta's Llama models | Open weights models |
| `DeepInfra` | Various open models | Multiple model options |
| `Cohere` | Cohere's language models | Command models |
| `PerplexityLabs` | Perplexity AI | Web search integration |
| `Anthropic` | Claude models | Long context windows |
| `YEPCHAT` | Yep.com's AI | Streaming responses |
| `ChatGPTClone` | ChatGPT-like interface | Multiple model options |
| `TypeGPT` | TypeChat models | Code generation focus |

### Example: Using Duckchat

```python
from webscout import WEBS

# Initialize and use Duckchat
with WEBS() as webs:
    response = webs.chat(
        "Explain quantum computing in simple terms",
        model='gpt-4o-mini'  # Options: mixtral-8x7b, llama-3.1-70b, claude-3-haiku, etc.
    )
    print(response)
```

### Example: Using Meta AI

```python
from webscout import Meta

# For basic usage (no authentication required)
meta_ai = Meta()

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

# For authenticated usage with web search and image generation
meta_ai = Meta(fb_email="your_email@example.com", fb_password="your_password")

# Text prompt with web search
response = meta_ai.ask("What are the latest developments in quantum computing?")
print(response["message"])
print("Sources:", response["sources"])

# Image generation
response = meta_ai.ask("Create an image of a futuristic city")
for media in response.get("media", []):
    print(media["url"])
```

### Example: GROQ with Tool Calling

```python
from webscout import GROQ, WEBS
import json

# Initialize GROQ client
client = GROQ(api_key="your_api_key")

# Define helper functions
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)})

def search(query):
    """Perform a web search"""
    try:
        results = WEBS().text(query, max_results=3)
        return json.dumps({"results": results})
    except Exception as e:
        return json.dumps({"error": str(e)})

# Register functions with GROQ
client.add_function("calculate", calculate)
client.add_function("search", search)

# Define tool specifications
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 web search",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "The search query"
                    }
                },
                "required": ["query"]
            }
        }
    }
]

# Use the tools
response = client.chat("What is 25 * 4 + 10?", tools=tools)
print(response)

response = client.chat("Find information about quantum computing", tools=tools)
print(response)
```

## 👨‍💻 Advanced AI Interfaces

### Direct Model Access with LLM and VLM

Webscout provides direct interfaces to language and vision-language models through the `LLM` and `VLM` classes.

```python
from webscout.LLM import LLM, VLM

# Text-only model interaction
llm = LLM("meta-llama/Meta-Llama-3-70B-Instruct")
response = llm.chat([
    {"role": "user", "content": "Explain the concept of neural networks"}
])
print(response)

# Vision-language model interaction
vlm = VLM("cogvlm-grounding-generalist")
response = vlm.chat([
    {
        "role": "user",
        "content": [
            {"type": "image", "image_url": "path/to/image.jpg"},
            {"type": "text", "text": "Describe what you see in this image"}
        ]
    }
])
print(response)
```

### GGUF Model Conversion

Webscout provides tools to convert and quantize Hugging Face models into the GGUF format for offline use.

```python
from webscout.Extra.gguf import ModelConverter

# Create a converter instance
converter = ModelConverter(
    model_id="mistralai/Mistral-7B-Instruct-v0.2",  # Hugging Face model ID
    quantization_methods="q4_k_m"                  # Quantization method
)

# Run the conversion
converter.convert()
```

#### Available Quantization Methods

| Method | Description |
|--------|-------------|
| `fp16` | 16-bit floating point - maximum accuracy, largest size |
| `q2_k` | 2-bit quantization (smallest size, lowest accuracy) |
| `q3_k_l` | 3-bit quantization (large) - balanced for size/accuracy |
| `q3_k_m` | 3-bit quantization (medium) - good balance for most use cases |
| `q3_k_s` | 3-bit quantization (small) - optimized for speed |
| `q4_0` | 4-bit quantization (version 0) - standard 4-bit compression |
| `q4_1` | 4-bit quantization (version 1) - improved accuracy over q4_0 |
| `q4_k_m` | 4-bit quantization (medium) - balanced for most models |
| `q4_k_s` | 4-bit quantization (small) - optimized for speed |
| `q5_0` | 5-bit quantization (version 0) - high accuracy, larger size |
| `q5_1` | 5-bit quantization (version 1) - improved accuracy over q5_0 |
| `q5_k_m` | 5-bit quantization (medium) - best balance for quality/size |
| `q5_k_s` | 5-bit quantization (small) - optimized for speed |
| `q6_k` | 6-bit quantization - highest accuracy, largest size |
| `q8_0` | 8-bit quantization - maximum accuracy, largest size |

#### Command Line Usage

```bash
python -m webscout.Extra.gguf convert -m "mistralai/Mistral-7B-Instruct-v0.2" -q "q4_k_m"
```

<div align="center">
  <p>
    <a href="https://youtube.com/@OEvortex">▶️ Vortex's YouTube Channel</a> |
    <a href="https://t.me/ANONYMOUS_56788">📢 Anonymous Coder's Telegram</a>
  </p>
</div>

## 🤝 Contributing

Contributions are welcome! If you'd like to contribute to Webscout, please follow these steps:

1. Fork the repository
2. Create a new branch for your feature or bug fix
3. Make your changes and commit them with descriptive messages
4. Push your branch to your forked repository
5. Submit a pull request to the main repository

## 🙏 Acknowledgments

* All the amazing developers who have contributed to the project
* The open-source community for their support and inspiration

---

<div align="center">
  <p>Made with ❤️ by the Webscout team</p>
</div>
