ARC-ANGEL AI Web Application

Overview

ARC-ANGEL is a sophisticated Flask web application designed to harness the power of advanced AI and machine learning technologies. The application integrates with cutting-edge libraries such as OpenAI, Google Cloud Vision, and scikit-learn to offer a wide range of functionalities. These include:

• Object Detection: Identify and classify objects within images.
• Sentiment Analysis: Analyze and determine the sentiment expressed in text.
• Text Generation: Create text based on provided prompts.
• Anomaly Detection: Detect unusual patterns or outliers in data.
• Real-time Analytics: Analyze data in real-time to gain insights.
• Real-time Recommendation: Provide recommendations based on user input.

Endpoints

Object Detection

Endpoint: POST /object-detection
Description: Analyzes a base64 encoded image to detect objects.
Request:

• frameData (base64 encoded image)

Response:

• result (list of detected objects)

Sentiment Analysis

Endpoint: POST /sentiment-analysis
Description: Evaluates the sentiment of a given text.
Request:

• text (text for sentiment analysis)

Response:

• result (sentiment analysis result)

Text Generation

Endpoint: POST /text-generation
Description: Generates text based on the provided prompt.
Request:

• prompt (text prompt for generation)

Response:

• result (generated text)

Anomaly Detection

Endpoint: POST /anomaly-detection
Description: Identifies anomalies in the provided data.
Request:

• data (data for anomaly detection)

Response:

• result (anomaly detection result)

Real-time Analytics

Endpoint: POST /realtime-analytics
Description: Performs real-time analysis on the provided feature matrix.
Request:

• feature_matrix (feature matrix for analytics)

Response:

• result (real-time analytics result)

Real-time Recommendation

Endpoint: POST /realtime-recommendation
Description: Recommends items based on user input in real-time.
Request:

• user_input (user input for recommendation)

Response:

• result (recommended item)

Additional Components

Lambda Function

File: lambda.py
Description: Contains a Lambda function for sentiment analysis using TextBlob.
Event:

• text (text for sentiment analysis)

Response:

• sentiment (sentiment analysis result)

Dockerfile

Description: Includes a Dockerfile configured for running unit tests using Poetry.
Purpose: Automates the environment setup for testing the application in a consistent manner.

Machine Learning Models

• generate.py: A script that utilizes an LSTM model for generating text.
• IterablDataset.py: Defines a custom dataset class for IterablDataset, tailored for specific data requirements.
• PromptDataset.py: Implements a custom dataset class for PromptDataset, designed to handle prompt-based data efficiently.

Web Interface

File: index.html
Description: Provides a basic web interface for users to interact with the API, facilitating ease of access and usability.

Libraries and Frameworks

• OpenAI: Used for generating text based on given prompts.
• Google Cloud Vision: Utilized for object detection tasks within images.
• scikit-learn: Employed for various machine learning tasks, including anomaly detection.
• Flask: The core framework for building and managing the web application.
• Poetry: Manages dependencies and project setup.
• Docker: Facilitates containerization, ensuring consistent environments across different setups.

Code

The main application code is written in Python and utilizes Flask for endpoint management. Below is a brief overview of the core implementation:

from flask import Flask, request, jsonify
import os
import cv2
import numpy as np
from google.cloud import vision
from sklearn.ensemble import IsolationForest
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import CountVectorizer

app = Flask(__name__)

# Initialize clients
vision_client = vision.ImageAnnotatorClient()

# Define endpoints and logic

@app.route('/object-detection', methods=['POST'])
def object_detection():
    # Endpoint implementation for object detection
    pass

@app.route('/sentiment-analysis', methods=['POST'])
def sentiment_analysis():
    # Endpoint implementation for sentiment analysis
    pass

@app.route('/text-generation', methods=['POST'])
def text_generation():
    # Endpoint implementation for text generation
    pass

@app.route('/anomaly-detection', methods=['POST'])
def anomaly_detection():
    # Endpoint implementation for anomaly detection
    pass

@app.route('/realtime-analytics', methods=['POST'])
def realtime_analytics():
    # Endpoint implementation for real-time analytics
    pass

@app.route('/realtime-recommendation', methods=['POST'])
def realtime_recommendation():
    # Endpoint implementation for real-time recommendation
    pass

if __name__ == '__main__':
    app.run(host='0.0.0.0')

## Installation

To set up and run the ARC-ANGEL AI Web Application locally, follow these steps:

1. **Clone the Repository:**

   ```bash
   git clone https://github.com/KidusB9/ARC-ANGEL.git
   cd ARC-ANGEL

2. Create and Activate a Virtual Environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Dependencies:

   Ensure that Poetry is installed. Then run:

   poetry install

4. Set Up Environment Variables:

   Create a .env file in the root directory with necessary configurations.

5. Run the Application:

   python app.py

   The application will be accessible at http://localhost:5000.

Usage

Once the application is running, you can interact with it through the API endpoints listed above. For a web-based interface, navigate to index.html in your browser.

Contributing

Contributions are welcome! If you'd like to contribute to ARC-ANGEL, please follow these guidelines:

1. Fork the Repository: Click the "Fork" button at the top right of the repository page on GitHub.

2. Clone Your Fork:

   git clone https://github.com/KidusB9/ARC-ANGEL.git

3. Create a Branch:

   git checkout -b feature/your-feature

4. Make Your Changes: Implement the desired feature or fix.

5. Commit Your Changes:

   git add .
   git commit -m "Add new feature"

6. Push to Your Fork:

   git push origin feature/your-feature

7. Create a Pull Request: Open a pull request on the original repository and describe your changes.

Contact

For any questions, issues, or suggestions, please contact:

• Email: Kidus@kidusberhanu.com
• GitHub: KidusB9

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

Special thanks to the libraries and frameworks used in this project:

• OpenAI for text generation capabilities.
• Google Cloud Vision for advanced image analysis.
• scikit-learn for robust machine learning solutions.
• Flask for creating the web application framework.
• Poetry for seamless dependency management.
• Docker for consistent development environments.