Skip to content

HiteshRepo/ai-practice-projects

BranchesTags

Repository files navigation

AI Practice Projects

This repository contains a collection of AI-powered applications and tools, including both Go and Node.js projects, that leverage APIs such as OpenAI and Hugging Face for various practical use cases.

Projects

Hugging Face Inference

A Node.js project demonstrating the use of the Hugging Face Inference API for chat completion, sentiment classification, and translation tasks.

Key Features:

  • Chat completion using large language models
  • Sentiment and emotion classification
  • Multilingual translation
  • Simple CLI interface with task-based flags
  • Easy setup with Hugging Face API token

Motivational Speaker

A command-line tool that generates positive and encouraging responses to user inputs using a fine-tuned OpenAI model.

Key Features:

  • Uploads training data to OpenAI
  • Creates and manages a fine-tuning job for GPT-3.5-turbo
  • Uses the fine-tuned model to generate motivational responses
  • Provides uplifting and encouraging messages to help users overcome challenges

Stock Price Predictor

A command-line tool that generates concise stock performance reports and buy/hold recommendations based on recent price data.

Key Features:

  • Fetches stock price data from Polygon.io
  • Analyzes data using OpenAI's GPT-4o Mini model
  • Supports zero-shot and few-shot analysis approaches
  • Generates concise reports with buy/hold recommendations

Image Generator

A command-line tool that generates images from text descriptions using OpenAI's DALL-E models.

Key Features:

  • Connects to OpenAI's DALL-E 3 API
  • Generates high-quality images based on text prompts
  • Saves generated images as PNG files locally
  • Supports customizable image parameters (size, style)
  • Optional Studio Ghibli-style image generation

Film Fusion

A command-line tool that generates AI-powered art inspired by your favorite movies and a chosen art style, using OpenAI's GPT-4 and DALL-E 3.

Key Features:

  • Generates a creative, single-line prompt based on a movie name and art style
  • Uses OpenAI's GPT-4 for imaginative prompt generation
  • Creates vivid images with DALL-E 3 based on the generated prompt
  • Supports a variety of art styles (art deco, impressionism, cyberpunk, and more)
  • Simple command-line interface

Setup:

  1. Enter the project directory: 
    cd film-fusion
  2. Install dependencies (requires Go 1.24+): 
    go mod tidy
  3. Set your OpenAI API key in a .env file: 
    export OPEN_API_KEY=your-openai-api-key

Usage Example:

go run main.go -film-name="Inception" -art-style=cyberpunk

For more details and art style options, see the Film Fusion README.

Content Moderator

A command-line tool that checks if text content is safe or contains potentially harmful material using OpenAI's moderation API.

Key Features:

  • Analyzes text input using OpenAI's moderation API
  • Identifies specific categories of harmful content
  • Supports multiple input types: single-line text, multi-line text, URLs, and files
  • Provides clear feedback on content safety

Pollyglot

A command-line tool that translates any given content into a specified language using OpenAI's GPT-3.5-turbo model.

Key Features:

  • Translates text to any target language using OpenAI's GPT-3.5-turbo
  • Simple CLI interface with flags for content and target language
  • Secure API key management via environment variables
  • Easily extensible and written in idiomatic Go

Vector Embeddings

A Go project for generating, storing, and searching vector embeddings for text data (e.g., podcast descriptions, movie details) using OpenAI and Supabase. Supports semantic search, recommendations, and AI-powered applications requiring vector representations of text.

Key Features:

  • Generate embeddings for text data (podcasts, movies) using OpenAI's API.
  • Store embeddings and original text in Supabase tables with vector support.
  • Perform semantic search over stored documents or movies using vector similarity.
  • Chat-based Q&A over documents or movies using OpenAI GPT-4.
  • Written in Go with idiomatic project structure and environment-based configuration.

Setup:

  1. Clone the repository and enter the project directory:

    git clone <repo-url>
cd vector-embeddings

  2. Install dependencies (requires Go 1.24+):

    go mod tidy

  3. Copy sample.env to .env and fill in your OpenAI and Supabase credentials:

    cp sample.env .env
    • OPEN_API_KEY — Your OpenAI API key
    • SUPABASE_PROJECT_URL — Your Supabase project URL
    • SUPABASE_API_KEY — Your Supabase API key (service role or anon, with insert/search permissions)

  4. Prepare Supabase by creating the required tables using the SQL scripts in vector-embeddings/queries/:

    • create_documents_table.sql
    • create_movies_table.sql

Usage Examples:

  • Insert podcast embeddings: 
    go run main.go -action=insert-docs
  • Semantic search (podcasts): 
    go run main.go -action=search-docs -query="What can I listen to in half an hour?"
  • Semantic search + chat (podcasts): 
    go run main.go -action=search-n-chat-docs -query="An episode Elon Musk would enjoy"
  • Insert movie embeddings (chunked): 
    go run main.go -action=chunk-n-insert-movies
  • Semantic search + chat (movies): 
    go run main.go -action=query-movie -query="Which movie can I take my child to?" -matches=3

Command-Line Flags:

  • -action (required): One of insert-docs, search-docs, search-n-chat-docs, chunk-n-insert-movies, query-movie
  • -query (required for search/chat actions): Query string for semantic search
  • -matches (optional for query-movie): Number of top matches to return (default: 1)

Security Note:
Never commit your real .env file or share your actual API keys. Only share sample.env as a template.

Movie Chatbot

A command-line chatbot that recommends movies and answers movie-related questions using OpenAI's GPT-4 and Supabase for semantic search. The bot maintains conversation history and can answer follow-up questions based on previous interactions.

Key Features:

  • Movie recommendations based on user queries
  • Answers follow-up and context-based questions (e.g., "What is my name?", "Which movies have you recommended?")
  • Remembers conversation history for personalized responses
  • Uses OpenAI GPT-4 and Supabase for semantic search and context retrieval
  • Simple CLI interface

Pop Choice

A command-line tool that recommends movies based on user interests using OpenAI embeddings and Supabase as a vector database. Supports both single-user and multi-user modes for personalized or group recommendations.

Key Features:

  • AI-powered movie recommendations using OpenAI embeddings
  • Stores and queries movie embeddings with Supabase
  • Single-user and multi-user interactive CLI modes
  • Easy setup and environment-based configuration

reAct

A Go-based command-line tool that leverages OpenAI's API to generate personalized activity ideas based on your current location and weather. Supports multiple versions of suggestion logic (v1, v2, and v3), with v3 as the default, and is easily extensible.

Key Features:

  • AI-powered activity suggestions using OpenAI
  • Weather and location-aware recommendations
  • Switchable logic versions (v1, v2, v3) with v3 as the default
  • Simple CLI interface with flags for version and query
  • Environment-based configuration

Setup:

  1. Enter the project directory: 
    cd reAct
  2. Install dependencies (requires Go 1.24+): 
    go mod tidy
  3. Create a .env file and add your API keys: 
    OPEN_API_KEY=your-openai-api-key
WEATHER_STACK_API_KEY=your-weatherstack-api-key
    Or export them in your shell before running the CLI.

Security Note:
Never commit your real .env file or share your actual API keys. Only share .env as a template if needed.

Multimodality

A Go CLI tool for generating, editing, and analyzing images using OpenAI's API. Supports image generation from text prompts, image inpainting (completion), and image vision (analysis) tasks.

Key Features:

  • Image Generation from text prompts using OpenAI's API
  • Image Completion (inpainting) with original and masked images
  • Image Vision: analyze and answer questions about images
  • Simple CLI interface with action-based flags
  • Environment-based configuration

Setup:

  1. Enter the project directory: 
    cd multimodality
  2. Install dependencies (requires Go 1.24+): 
    go mod tidy
  3. Set your OpenAI API key in a .env file: 
    export OPEN_API_KEY=your-openai-api-key

Usage Examples:

  • Generate an image: 
    go run main.go -action=image-gen -image-desc="An astronaut riding a bicycle on the moon"
  • Complete (inpaint) an image: 
    go run main.go -action=image-complete -image-desc="Ancient Konark temple dedicated for Lord Surya (Sun) before it was destroyed." -image-path=./test-files/image.png -masked-image-path=./test-files/masked.png
  • Image vision (analyze image): 
    go run main.go -action=image-vision -query="What did the Ancient Konark temple dedicated for Lord Surya (Sun) look like before it was destroyed." -image-path=./test-files/image.png

For more details, see the Multimodality README.

Common Technologies

All projects are built with:

  • Go 1.24+ and/or Node.js 18+
  • API integration (OpenAI, Hugging Face)
  • Environment-based configuration
  • Command-line interfaces

Getting Started

Each project has its own README with detailed instructions for setup and usage. To get started with a specific project:

  1. Navigate to the project directory
  2. Follow the installation instructions in the project's README
  3. Configure the required API keys
  4. Run the application

Repository Structure

ai-practice-projects/
├── README.md                      # This file
├── .gitignore                     # Git ignore file
├── content-moderator/             # Content Moderator project
│   ├── README.md                  # Project documentation
│   ├── main.go                    # Main application code
│   ├── sample.env                 # Environment variables template
│   └── openai/                    # OpenAI client implementation
│       └── client.go
├── film-fusion/                   # Film Fusion project
│   ├── README.md                  # Project documentation
│   ├── main.go                    # Main application code
│   ├── go.mod                     # Go module definition
│   ├── go.sum                     # Go dependencies lockfile
│   ├── .env                       # Environment variables template
│   ├── loader/                    # Loading animation and progress display
│   ├── openai/                    # OpenAI client logic
│   └── utils/                     # Utility functions
├── hugging-face-inference/        # Hugging Face Inference (Node.js)
│   ├── README.md                  # Project documentation
│   ├── index.js                   # Main application code
│   ├── .env                       # Environment variables template
│   └── package.json               # Project metadata
├── motivational-speaker/          # Motivational Speaker project
│   ├── README.md                  # Project documentation
│   ├── main.go                    # Main application code
│   ├── finetunedata.jsonl         # Training data for fine-tuning
│   ├── sample.env                 # Environment variables template
│   └── openai/                    # OpenAI client implementation
│       └── client.go
├── stock-price-predictor/         # Stock Price Predictor project
│   ├── README.md                  # Project documentation
│   ├── main.go                    # Main application code
│   ├── sample.env                 # Environment variables template
│   ├── openai/                    # OpenAI client implementation
│   │   └── client.go
│   └── polygon/                   # Polygon.io client implementation
│       ├── client.go
│       └── models.go
├── image-generator/               # Image Generator project
│   ├── README.md                  # Project documentation
│   ├── main.go                    # Main application code
│   ├── sample.env                 # Environment variables template
│   └── openai/                    # OpenAI client implementation
│       └── client.go
├── content-moderator/             # Content Moderator project
│   ├── README.md                  # Project documentation
│   ├── main.go                    # Main application code
│   ├── sample.env                 # Environment variables template
│   └── openai/                    # OpenAI client implementation
│       └── client.go
├── pollyglot/                     # Pollyglot translation project
│   ├── README.md                  # Project documentation
│   ├── main.go                    # Main application code
│   ├── sample.env                 # Environment variables template
│   └── openai/                    # OpenAI client implementation
│       └── client.go
├── vector-embeddings/             # Vector Embeddings project
│   ├── README.md                  # Project documentation
│   ├── main.go                    # Main application code
│   ├── sample.env                 # Environment variables template
│   ├── openai/                    # OpenAI client implementation
│   ├── supabase/                  # Supabase client and DB operations
│   ├── constants/                 # Constants and sample data
│   ├── models/                    # Data models
│   └── queries/                   # SQL scripts for DB setup
├── movie-chatbot/                 # Movie Chatbot project
│   ├── README.md                  # Project documentation
│   ├── main.go                    # Main application code
│   ├── .env                       # Environment variables template
│   ├── go.mod                     # Go module definition
│   ├── go.sum                     # Go dependencies lockfile
│   ├── constants/                 # Project constants
│   ├── models/                    # Data models
│   ├── openai/                    # OpenAI client implementation
│   └── supabase/                  # Supabase client and vector search
├── multimodality/                 # Multimodality image generation & vision CLI
│   ├── README.md                  # Project documentation
│   ├── main.go                    # Main application code
│   ├── go.mod                     # Go module definition
│   ├── go.sum                     # Go dependencies lockfile
│   ├── .env                       # Environment variables template
│   ├── openai/                    # OpenAI client and image logic
│   └── test-files/                # Example/test images
├── pop-choice/                    # Pop Choice movie recommender
│   ├── README.md                  # Project documentation
│   ├── main.go                    # Main application code
│   ├── .env                       # Environment variables template
│   ├── go.mod                     # Go module definition
│   ├── go.sum                     # Go dependencies lockfile
│   ├── constants/                 # Project constants
│   ├── models/                    # Data models
│   ├── openai/                    # OpenAI client implementation
│   ├── supabase/                  # Supabase client and DB operations
│   └── queries/                   # SQL scripts for DB setup
├── reAct/                         # reAct activity suggestion CLI (Go + OpenAI)
│   ├── README.md                  # Project documentation
│   ├── main.go                    # Main application code
│   ├── .env                       # Environment variables template
│   ├── go.mod                     # Go module definition
│   ├── go.sum                     # Go dependencies lockfile
│   ├── constants/                 # Project constants
│   ├── models/                    # Data models
│   ├── openai/                    # OpenAI client implementation
│   ├── tools/                     # Utility tools
│   ├── utils/                     # Helper utilities
│   └── versions/                  # Versioned suggestion logic
└── prompts/                       # Additional prompt resources

Prerequisites

  • Go 1.24 or later
  • API keys for the services used by each project:
    • OpenAI - for all projects
    • Polygon.io - for Stock Price Predictor
    • OpenAI DALL-E access - for Image Generator

License

[TODO]

Contributing

[TODO]

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

0 stars

Watchers

1 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages