Overview:
1) Python Programming: Leveraging the versatility and robustness of Python, we've built a solid foundation for our speech recognizer and assistant, ensuring flexibility and scalability.
2) OPENAI API Integration: Empowering our assistant with the capabilities of the OPENAI API enables it to comprehend, process, and respond to queries across a spectrum of languages and topics.
3) Google Recognizer for Voice-to-Text: By utilizing Google's advanced speech recognition technology, we achieve accurate and efficient transcription of spoken words into text, forming the basis for seamless interaction.
4) Streamlit for Deployment: Deploying our solution using Streamlit provides an intuitive and user-friendly interface, making interaction effortless and accessible to users across diverse platforms.

Advantages:
Multilingual Mastery: Breaks language barriers, catering globally.
AI-Powered Precision: Learns, adapts, and delivers tailored responses.
Efficiency Booster: Swift voice interaction, enhancing productivity.

Market Demand:
The market demands seamless communication solutions that transcend language barriers and facilitate efficient interaction. Our Multilingual Speech Recognizer & AI Assistant addresses this demand by offering a versatile, intelligent, and accessible platform.

Conclusion:
In the dynamic landscape of communication technology, our Multilingual Speech Recognizer & AI Assistant stands as a testament to innovation and progress. With its multilingual competence, AI-powered assistance, and user-friendly deployment, it heralds a new era of effortless communication and interaction, catering to the evolving needs of a diverse global audience.

Custom GPTs 48-hours Hackathon

My creation, a Multilingual Speech Recognizer & AI Assistant, embodies innovation at its core, utilizing cutting-edge tools and technologies to redefine the way we communicate and interact with information.

HassanAI

OpenAI is an AI research and deployment company

OpenAI

GPT-3.5 stands for Generative Pre-trained Transformer and is fine-tuned version of GPT-3. It is an autoregressive language model that uses deep learning to produce human-like text. There are many models in GPT-3.5 family as gpt-3.5-turbo or gpt-3.5-turbo.

GPT-3.5

ChatGPT is a large language model trained by OpenAI to generate human-like text in a conversational style. It is a variant of the GPT-3 model, which was specifically designed to be used to generate text in response to user input. The ChatGPT model has been trained on a vast amount of text data, including conversations and other types of human-generated text, which allows it to generate text that is similar in style and content to human conversation. ChatGPT can be used to generate responses to questions, code, make suggestions, or provide information in a conversational manner, and it is able to do so in a way that is often indistinguishable from human-generated text. The initial model has been trained using Reinforcement Learning from Human Feedback (RLHF), using methods similar to InstructGPT, but with slight differences in the data collection setup. The model is trained using supervised fine-tuning, where human AI trainers provided conversations in which they played both sides—the user and an AI assistant. The trainers would have had access to model-written suggestions to help them compose their responses.

ChatGPT

Empower your projects with customizable language models, prompt precision, and extensive tool integration through OpenGPTs by LangChain.

OpenGPTs

Large language models (LLMs) have transformed the way we approach machine learning and artificial intelligence. These models are incredibly powerful, but their true potential lies in their ability to work in harmony with other sources of data and computation. This library has been developed to assist developers in building applications that combine LLMs with other technologies, allowing them to create truly intelligent and innovative products.

Langchain

Explore the groundbreaking GPT-4 Vision, empowering AI to understand and process images, revolutionizing capabilities across diverse domains.

GPT-4 Vision

LangChain

A Gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA. It provides a user-friendly interface to interact with these models and generate text, with features such as model switching, notebook mode, chat mode, and more.

Text Generation Web UI

Multilingual Speech Recognizer and AI Assistant

Dive2Code is an AI-assisted platform designed to facilitate the exploration of GitHub repositories. Dive2Code is an innovative AI-assisted platform tailor-made for streamlining the exploration of GitHub repositories. By leveraging the power of AutoGPT combined with the extensive reach of the GitHub API, Dive2Code provides an intuitive visual representation of the multifaceted components of a repository, encompassing classes, modules, and files. Instead of the traditional tedious manual searching, users can now interact effortlessly through a user-friendly chat interface to dive deep into specific elements of a repository. Beyond mere exploration, the principal value of Dive2Code lies in its transformative capacity. It significantly boosts developer efficiency, offering an optimized glance at codebase structures. Moreover, when organizations look to bring new developers on board, the onboarding process often presents challenges. Familiarizing oneself with a new codebase can be daunting. Dive2Code simplifies this, ensuring that new team members can quickly get up to speed, fostering a more collaborative and efficient development environment.

Utilizing AutoGPT and GitHub API, the tool provides a visual representation of repository components, including classes, modules, and files. 

Users can interact via a chat interface to access specific elements without manual search. The primary value lies in improving developer efficiency, optimizing codebase structures, and simplifying new developer onboarding for organizations.

AutoGPT Arena Hacks

Dive2Code uses AutoGPT to simplify GitHub repository navigation. It visually maps repository elements and allows chat-based interaction for targeted access. The tool enhances developer efficiency and facilitates easier developer onboarding

Power Rangers

Auto-GPT is an experimental open-source application that showcases the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM 'thoughts' to autonomously achieve whatever goal you set.

Auto-GPT

Chroma is an AI-native open-source embedding database. The fastest way to build Python or JavaScript LLM apps with memory

Chroma

Dive2Code

While coding, programmers rely heavily on documentation, and the process of switching windows every time you search for something could be obnoxious at times, especially if the device has only one display. We thought about a solution of speeding the process of developing by creating a code helper VSCode extension. Llama2 GPT CodePilot is aiming at helping software developers in building code or debugging their software by prompting the gpt making it coding convenient for developers with only one display. It uses a large language model, CodeLlama-7B-Instruct-GPTQ, takes input from the user, and generates a relevant response based on the text given. It is published on the VSCode extension marketplace under the name of "Llama2-GPT-CodePilot" and it is written in TypeScript

Llama 2 Hackathon with Clarifai

A VSCode code helper extension powered by CodeLlama-7B-Instruct-GPTQ model of Llama2 provided by Clarifai 

New CV and NLP

Llama2 GPT CodePilot VSCode Extension

Many ML researchers are unhappy with their development process.

Coding from scratch is laborious since the process for developing and testing new models is largely the same each time but no-code and low-code platforms do not provide enough granularity to tweak models, loss functions, and training processes.

Most ML researchers experiment in Jupyter notebooks. They are quick, composable, and easy to present.However, even with the help of LLMs:
- Copy-pasting code between web-interfaces and notebooks is slow
- Errors in generated code are difficult to detect and fix
- Writing the appropriate prompt to generate correct boilerplate code is still repetitive

Our solution takes existing data and a natural language prompt and uses it to build a model that is compatible with the shape and types of the data. It also uses recursive API calls to fix any errors in the generated code by passing them back to the LLM. In the future, this product could be extended to generate code for the full build, train, test, and measure cycle so that researchers can ask for a set of models to be tested, tweak the generated code as needed, and rapidly evaluate the best model for their needs.

Google Vertex AI Hackathon

Faster ML Development, Powered by PaLM. 
AutoML Jupyter is more customisable than no-code platforms yet faster than manual coding. It's a VSCode-LLM integration that recursively generates boilerplate code for ML model development and testing.

AutoML

Discover PaLM 2, Google's groundbreaking large language model, excelling in complex reasoning, multilingual translation, and coding expertise. Experience the next-generation AI technology that is setting new standards in the field of artificial intelligence.

PaLM

Discover Alphabet Inc.'s gem: Google LLC! A global multi-tech marvel offering internet-focused services – search engines, online ads, cloud prowess, software savvy, and hardware innovation.

Google

Discover Codey, the AI-powered coding assistant developed by Google AI, designed to transform software development with its advanced code generation capabilities across various programming languages.

Codey

AutoML Jupyter

We are presenting a tool for static code analysis, based on a large language model by Google.

It can assist you with code review and improvement. AI can greatly enhance code quality and save developer's time.
Remember, fixing and improving the code is probably the most time-consuming part of the job.

Conventional static code analysis tools are mostly rule-based. They can assist in:
* checking style;
* checking types;
* finding basic security issues.

In contrast, an AI-based code analysis tool can:
* find potential bugs, based on syntax and semantics;
* suggest code optimisations;
* promote best coding practices.

We believe this project can also find its place in the field of products for developer efficiency.
With rapidly improving large language models, organisations and individuals are willing to boost their productivity, and pay for subscriptions or pay-as-you-go services.

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