neuralite 0.0.2

A streamlined Python framework for rapidly building and deploying machine learning-based AIs.

Neuralite: A Streamlined Python Framework for Rapidly Building and Deploying Machine Learning-Based Conversational Agents 💡

Build, deploy, and scale your AI projects with Neuralite! Whether you're a seasoned AI developer or just starting out, Neuralite offers a simple and efficient way to implement AI features into your software.

Table of Contents

1. Getting Started

2. Basic Chatbot

3. Compiling Your Model

4. Loading a Compiled Model

5. Sample Datasets

   • Conversational Dataset

   • Medical Advice Dataset

   • Educational Dataset

Getting Started

To get started, install Neuralite with pip:

pip install Neuralite

Basic Chatbot

Creating a basic chatbot is simple:

from neuralite.interpreter import ai # Imports framework



assistant = ai('dataset.json') # Loads dataset



cycle_end = False # Starts the AI cycle



while not cycle_end: # Main cycle

    message = input("Enter a message: ") # Prompts user with message

    if message.lower() == "stop": # Exit with 'stop'

        cycle_end = True # Ends cycle

    else:

        print(assistant.process_input(message)) # Prints the response

Compiling Your Model 📦

To make your dataset unreadable, load faster, and to package it into a neat file.

from neuralite.interpreter import ai # Imports framework



assistant = ai('dataset.json') # Loads dataset



assistant.compile('model.nlite') # Packages dataset to 'model.nlite'

Loading a Compiled Model 🔓

To load a precompiled model.

from neuralite.interpreter import ai # Imports framework



assistant = ai.compiled('model.nlite') # Loads the compiled model



cycle_end = False # Starts the AI cycle



while not cycle_end: # Main cycle

    message = input("Enter a message: ") # Prompts user with message

    if message.lower() == "stop": # Exit with 'stop'

        cycle_end = True # Ends cycle

    else:

        print(assistant.process_input(message)) # Prints the response

Sample Datasets

Here are some sample datasets for different use-cases.

Conversational Dataset

{

  "greeting": ["Hi", "Hello", "Hey there"],

  "greeting_responses": ["Hello!", "Hi, how can I help?", "Hey! What's up?"],

  "farewell": ["Goodbye", "See you", "Ciao"],

  "farewell_responses": ["Goodbye!", "See you soon!", "Take care!"],

  "how_are_you": ["How are you?", "How's it going?", "What's up?"],

  "how_are_you_responses": ["I'm good, thank you!", "I'm doing well. How about you?", "Not much, what about you?"],

  "compliment": ["You're great!", "You're amazing!", "You're awesome!"],

  "compliment_responses": ["Thank you! You're pretty awesome too.", "Thanks! You're great as well!", "Thanks! That means a lot."],

  "name": ["What's your name?", "Who are you?", "Tell me your name."],

  "name_responses": ["I'm Neuralite, your AI assistant.", "Call me Neuralite.", "I'm Neuralite! Nice to meet you."],

  "jokes": ["Tell me a joke.", "Do you know any jokes?", "Make me laugh."],

  "jokes_responses": ["Why did the scarecrow win an award? Because he was outstanding in his field!", "I told my computer I needed a break. Now it won't stop sending me Kit-Kats.", "Why did the math book look sad? Because it had too many problems."]

}

Medical Advice Dataset

{

  "general_health": ["How can I stay healthy?", "Tell me some health tips"],

  "general_health_responses": ["Eat a balanced diet, exercise regularly, and get enough sleep.", "Maintaining a healthy lifestyle is essential for long-term health."],

  "first_aid": ["What should I do for a cut?", "How do I treat burns?"],

  "first_aid_responses": ["For minor cuts, clean with water and apply antiseptic.", "For minor burns, cool the area under cold running water and apply a burn ointment."],

  "headache": ["How to treat a headache?", "What's good for migraines?"],

  "headache_responses": ["For mild headaches, over-the-counter pain relievers and rest are recommended.", "For migraines, consult a healthcare provider for prescription medications."],

  "allergies": ["How to manage allergies?", "Tell me about antihistamines"],

  "allergies_responses": ["Antihistamines can help relieve allergy symptoms.", "For chronic allergies, consider lifestyle changes and consult a healthcare provider."],

  "exercise": ["What are the benefits of exercise?", "Tell me about aerobic exercises"],

  "exercise_responses": ["Exercise improves mental and physical health.", "Aerobic exercises like running and swimming are good for cardiovascular health."]

}

Conversational Dataset

{

  "general_science": ["Tell me about science", "What is science?"],

  "general_science_responses": ["Science is the study of the natural world.", "Science helps us understand how things work."],

  "literature": ["Who wrote 'Romeo and Juliet'?", "Tell me about 'Moby Dick'"],

  "literature_responses": ["'Romeo and Juliet' was written by William Shakespeare.", "'Moby Dick' is a novel by Herman Melville about Captain Ahab's obsession with a white whale."],

  "history": ["Tell me about the French Revolution", "Who was Martin Luther King Jr.?"],

  "history_responses": ["The French Revolution was a period of social and political upheaval in France from 1789 to 1799.", "Martin Luther King Jr. was an American civil rights leader who fought for racial equality."],

  "chemistry": ["What is the periodic table?", "Tell me about chemical bonds"],

  "chemistry_responses": ["The periodic table organizes chemical elements based on their properties.", "Chemical bonds are forces that hold atoms together in a molecule."],

  "biology": ["What is DNA?", "Tell me about evolution"],

  "biology_responses": ["DNA is the genetic material that carries information about an organism.", "Evolution is the process of change in species over generations."]

}