Webscout
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
Stars: 203
WebScout is a versatile tool that allows users to search for anything using Google, DuckDuckGo, and phind.com. It contains AI models, can transcribe YouTube videos, generate temporary email and phone numbers, has TTS support, webai (terminal GPT and open interpreter), and offline LLMs. It also supports features like weather forecasting, YT video downloading, temp mail and number generation, text-to-speech, advanced web searches, and more.
README:
Webscout is the all-in-one search and AI toolkit you need.
Discover insights with Yep.com, DuckDuckGo, and Phind; access cutting-edge AI models; transcribe YouTube videos; generate temporary emails and phone numbers; perform text-to-speech conversions; and much more!
- Comprehensive Search: Leverage Google, DuckDuckGo for diverse search results.
- AI Powerhouse: Access and interact with various AI models, including OpenAI, Cohere, and more.
- YouTube Toolkit: Advanced YouTube video and transcript management with multi-language support, versatile downloading, and intelligent data extraction
- GitAPI: Powerful GitHub data extraction toolkit for seamless repository and user information retrieval, featuring commit tracking, issue management, and comprehensive user analytics - all without authentication requirements for public data
- Tempmail & Temp Number: Generate temporary email addresses and phone numbers for enhanced privacy.
- Text-to-Speech (TTS): Convert text into natural-sounding speech using multiple AI-powered providers like ElevenLabs, StreamElements, and Voicepods.
- GGUF Conversion & Quantization: Convert and quantize Hugging Face models to GGUF format.
- SwiftCLI: A powerful and elegant CLI framework that makes it easy to create beautiful command-line interfaces.
- LitPrinter: Provides beautiful, styled console output with rich formatting and colors
- LitLogger: Simplifies logging with customizable formats and color schemes
- LitAgent: Powerful and modern user agent generator that keeps your requests fresh and undetectable
- Text-to-Image: Generate high-quality images using a wide range of AI art providers
- Scout: Advanced web parsing and crawling library with intelligent HTML/XML parsing, web crawling, and Markdown conversion
- Awesome Prompts (Act): A curated collection of system prompts designed to transform Webscout into specialized personas, enhancing its ability to assist with specific tasks. Simply prefix your request with the act name or index number to leverage these tailored capabilities.
- Weather Tool kit Webscout provides tools to retrieve weather information.
- AIsearch AI Search Providers offer powerful and flexible AI-powered search Search Engine
pip install -U webscoutpython -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. |
| python -m webscout weather -k Text | CLI function to get weather information for a location using Webscout. |
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 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)The WEBS and AsyncWEBS classes are used to retrieve search results from DuckDuckGo.com.
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:
Example - WEBS:
from webscout import WEBS
R = WEBS().text("python programming", max_results=5)
print(R)Example - AsyncWEBS:
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()Important Note: The WEBS and AsyncWEBS classes should always be used as a context manager (with statement). This ensures proper resource management and cleanup, as the context manager will automatically handle opening and closing the HTTP client connection.
Exceptions:
-
WebscoutE: Raised when there is a generic exception during the API request.
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)Retrieve a comprehensive list of all supported LLMs.
from webscout import model
from rich import print
all_models = model.llm.list()
print("Available models:")
print(all_models)Obtain a summary of the available LLMs, including provider details.
from webscout import model
from rich import print
summary = model.llm.summary()
print("Summary of models:")
print(summary)Filter and display LLMs available from a specific provider.
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}")Retrieve a comprehensive list of all supported TTS voices.
from webscout import model
from rich import print
all_voices = model.tts.list()
print("Available TTS voices:")
print(all_voices)Obtain a summary of the available TTS voices, including provider details.
from webscout import model
from rich import print
summary = model.tts.summary()
print("Summary of TTS voices:")
print(summary)Filter and display TTS voices available from a specific provider.
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)Using phindv2:
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 URLsfrom 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"))
LLAMA, C4ai, Venice, Copilot, HuggingFaceChat, TwoAI, HeckAI, AllenAI, PerplexityLabs, AkashGPT, DeepSeek, WiseCat, IBMGranite, QwenLM, ChatGPTGratis, TextPollinationsAI, GliderAI, Cohere, REKA, GROQ, AsyncGROQ, OPENAI, AsyncOPENAI, KOBOLDAI, AsyncKOBOLDAI, BLACKBOXAI, PhindSearch, GEMINI, DeepInfra, AI4Chat, Phindv2, OLLAMA, AndiSearch, PIZZAGPT, Sambanova, DARKAI, KOALA, Meta, AskMyAI, PiAI, Julius, YouChat, YEPCHAT, Cloudflare, TurboSeek, Editee, TeachAnything, AI21, Chatify, X0GPT, Cerebras, Lepton, GEMINIAPI, Cleeai, Elmo, Free2GPT, GPTWeb, Netwrck, LlamaTutor, PromptRefine, TutorAI, ChatGPTES, Bagoodex, AIMathGPT, GaurishCerebras, GeminiPro, LLMChat, Talkai, Llama3Mitril, Marcus, TypeGPT, Netwrck, MultiChatAI, JadveOpenAI, ChatGLM, NousHermes, FreeAIChat, ElectronHub, GithubChat, Flowith, SonusAI, UncovrAI, LabyrinthAI, WebSim, LambdaChat, ChatGPTClone
Code is similar to other providers.
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?"}
]
}])Webscout provides tools to convert and quantize Hugging Face models into the GGUF format for use with offline LLMs.
Example:
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()Command Line Usage:
-
GGUF Conversion:
python -m webscout.Extra.gguf convert -m "prithivMLmods/QWQ-500M" -q "q2_k"
Note:
- Replace
"your_username"and"your_hf_token"with your actual Hugging Face credentials. - The
model_pathinautollamais the Hugging Face model ID, andgguf_fileis the GGUF file ID.
Contributions are welcome! If you'd like to contribute to Webscout, please follow these steps:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Make your changes and commit them with descriptive messages.
- Push your branch to your forked repository.
- Submit a pull request to the main repository.
- All the amazing developers who have contributed to the project!
- The open-source community for their support and inspiration.
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LLMVoX
LLMVoX is a lightweight 30M-parameter, LLM-agnostic, autoregressive streaming Text-to-Speech (TTS) system designed to convert text outputs from Large Language Models into high-fidelity streaming speech with low latency. It achieves significantly lower Word Error Rate compared to speech-enabled LLMs while operating at comparable latency and speech quality. Key features include being lightweight & fast with only 30M parameters, LLM-agnostic for easy integration with existing models, multi-queue streaming for continuous speech generation, and multilingual support for easy adaptation to new languages.
mcp-framework
MCP-Framework is a TypeScript framework for building Model Context Protocol (MCP) servers with automatic directory-based discovery for tools, resources, and prompts. It provides powerful abstractions, simple server setup, and a CLI for rapid development and project scaffolding.
llmproxy
llmproxy is a reverse proxy for LLM API based on Cloudflare Worker, supporting platforms like OpenAI, Gemini, and Groq. The interface is compatible with the OpenAI API specification and can be directly accessed using the OpenAI SDK. It provides a convenient way to interact with various AI platforms through a unified API endpoint, enabling seamless integration and usage in different applications.
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WebScout is a versatile tool that allows users to search for anything using Google, DuckDuckGo, and phind.com. It contains AI models, can transcribe YouTube videos, generate temporary email and phone numbers, has TTS support, webai (terminal GPT and open interpreter), and offline LLMs. It also supports features like weather forecasting, YT video downloading, temp mail and number generation, text-to-speech, advanced web searches, and more.
KrillinAI
KrillinAI is a video subtitle translation and dubbing tool based on AI large models, featuring speech recognition, intelligent sentence segmentation, professional translation, and one-click deployment of the entire process. It provides a one-stop workflow from video downloading to the final product, empowering cross-language cultural communication with AI. The tool supports multiple languages for input and translation, integrates features like automatic dependency installation, video downloading from platforms like YouTube and Bilibili, high-speed subtitle recognition, intelligent subtitle segmentation and alignment, custom vocabulary replacement, professional-level translation engine, and diverse external service selection for speech and large model services.
DownEdit
DownEdit is a fast and powerful program for downloading and editing videos from platforms like TikTok, Douyin, and Kuaishou. It allows users to effortlessly grab videos, make bulk edits, and utilize advanced AI features for generating videos, images, and sounds in bulk. The tool offers features like video, photo, and sound editing, downloading videos without watermarks, bulk AI generation, and AI editing for content enhancement.
sktime
sktime is a Python library for time series analysis that provides a unified interface for various time series learning tasks such as classification, regression, clustering, annotation, and forecasting. It offers time series algorithms and tools compatible with scikit-learn for building, tuning, and validating time series models. sktime aims to enhance the interoperability and usability of the time series analysis ecosystem by empowering users to apply algorithms across different tasks and providing interfaces to related libraries like scikit-learn, statsmodels, tsfresh, PyOD, and fbprophet.
NeoPass
NeoPass is a free Chrome extension designed for students taking tests on exam portals like Iamneo and Wildlife Ecology NPTEL. It provides features such as NPTEL integration, NeoExamShield bypass, AI chatbot with stealth mode, AI search answers/code, MCQ solving, tab switching bypass, pasting when restricted, and remote logout. Users can install the extension by following simple steps and use shortcuts for quick access to features. The tool is intended for educational purposes only and promotes academic integrity.
tafrigh
Tafrigh is a tool for transcribing visual and audio content into text using advanced artificial intelligence techniques provided by OpenAI and wit.ai. It allows direct downloading of content from platforms like YouTube, Facebook, Twitter, and SoundCloud, and provides various output formats such as txt, srt, vtt, csv, tsv, and json. Users can install Tafrigh via pip or by cloning the GitHub repository and using Poetry. The tool supports features like skipping transcription if output exists, specifying playlist items, setting download retries, using different Whisper models, and utilizing wit.ai for transcription. Tafrigh can be used via command line or programmatically, and Docker images are available for easy usage.
subtitler
Subtitles by fframes is a free, local, on-device AI video transcription tool with a user-friendly GUI. It allows users to transcribe video content, edit transcribed cues, style the subtitles, and render them directly onto the video. The tool provides a convenient way to create accurate subtitles for videos without the need for an internet connection.
AI-Youtube-Shorts-Generator
AI Youtube Shorts Generator is a Python tool that utilizes GPT-4 and Whisper to generate engaging YouTube shorts from long-form videos. It downloads videos, transcribes them, extracts highlights, detects speakers, and crops content vertically for shorts. The tool requires Python 3.7 or higher, FFmpeg, and OpenCV. Users can contribute to the project under the MIT License.
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LLMStack
LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.
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Daily Chinese ancient poetry and AI-generated images powered by Bing DALL-E-3. GitHub Action triggers the process automatically. Poetry is provided by Today's Poem API. The website is built with Astro.
exif-photo-blog
EXIF Photo Blog is a full-stack photo blog application built with Next.js, Vercel, and Postgres. It features built-in authentication, photo upload with EXIF extraction, photo organization by tag, infinite scroll, light/dark mode, automatic OG image generation, a CMD-K menu with photo search, experimental support for AI-generated descriptions, and support for Fujifilm simulations. The application is easy to deploy to Vercel with just a few clicks and can be customized with a variety of environment variables.
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SillyTavern is a user interface you can install on your computer (and Android phones) that allows you to interact with text generation AIs and chat/roleplay with characters you or the community create. SillyTavern is a fork of TavernAI 1.2.8 which is under more active development and has added many major features. At this point, they can be thought of as completely independent programs.
Twitter-Insight-LLM
This project enables you to fetch liked tweets from Twitter (using Selenium), save it to JSON and Excel files, and perform initial data analysis and image captions. This is part of the initial steps for a larger personal project involving Large Language Models (LLMs).
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Aila Desktop Application is a powerful tool that integrates multiple leading AI models into a single desktop application. It allows users to interact with various AI models simultaneously, providing diverse responses and insights to their inquiries. With its user-friendly interface and customizable features, Aila empowers users to engage with AI seamlessly and efficiently. Whether you're a researcher, student, or professional, Aila can enhance your AI interactions and streamline your workflow.
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obs-localvocal
LocalVocal is a live-streaming AI assistant plugin for OBS that allows you to transcribe audio speech into text and perform various language processing functions on the text using AI / LLMs (Large Language Models). It's privacy-first, with all data staying on your machine, and requires no GPU, cloud costs, network, or downtime.