AIPowered_Study_Assistant_App

This repository contains both the backend API and frontend application for the AI-Powered Study Assistant. The backend provides text summarization capabilities through a REST API, while the frontend offers a user-friendly interface for interacting with these features.

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)
• Android Studio (for frontend development)
• JDK 11 or higher

Kotlin Frontend

1. Open the project in Android Studio.
2. Sync the project with Gradle files.
3. Run the application on an emulator or a physical device.

Running the Application

1. Open the project in Android Studio.
2. Click on the "Run" button to launch the application on an emulator or a physical device.

Frontend Structure

Frontend/app/src/main/java/com/musketeers_and_me/ai_powered_study_assistant_app/
├── assets/                 # Raw asset files bundled with the app (e.g., text, fonts, templates)
├── Courses/                # Screens and logic for creating, viewing, and managing courses
├── DatabaseProvider/       # Database access and operations (e.g., Firebase integration)
├── GroupStudy/             # Features for group study sessions and collaborative tools
├── LectureAndNotes/        # Management of lectures, notes, and image uploads
├── Models/                 # Data models and classes representing app entities
├── Opening_Registration/   # User onboarding, login, and registration flows
├── OuterStructure/         # App-wide structural components and navigation logic
├── QuizCenter/             # Quiz management, participation, and results display
├── QuizStructure/          # Definitions and logic for quiz formats and questions
├── Services/               # Background services and app-wide service logic
├── SmartDigest/            # AI-powered summarization and smart content extraction
├── Utils/                  # Utility classes and helper functions for common tasks
├── AuthService.kt          # Authentication service logic
├── MainActivity.kt         # Main entry point and navigation for the app
└── MyApplication.kt        # Application class for global app configuration

Backend _ ML

This repository contains the backend API for the AI-Powered Study Assistant application. The backend provides text summarization capabilities through a REST API.

Installation

1. Clone the repository:

cd ML_Backend

2. Create and activate a virtual environment:

# Windows
python -m venv venv
.\venv\Scripts\activate

# Linux/MacOS
python -m venv venv
source venv/bin/activate

3. Install the required dependencies:

Running the Application

1. Start the Flask server:

python app.py

The server will start running at http://127.0.0.1:5000

API Endpoints

---

📄 Text Summarization API

• URL: /summarize
• Method: POST
• Content-Type: multipart/form-data

✅ Request Parameters (form-data):

Key	Type	Description
text	string	The text you want to summarize
context (optional)	string	Additional context if needed

✅ Success Response:

{
    "summary": "Summarized text here"
}

❌ Error Response:

{
    "error": "Error message here"
}

---

📄 Key Points API

• URL: /key_points
• Method: POST
• Content-Type: multipart/form-data

✅ Request Parameters (form-data):

Key	Type	Description
text	string	The text from which you want to generate key points
context (optional)	string	Additional context if needed

---

📄 Concept List API

• URL: /concept_list
• Method: POST
• Content-Type: multipart/form-data

✅ Request Parameters (form-data):

Key	Type	Description
text	string	The text from which you want to generate concept list
context (optional)	string	Additional context if needed

---

📄 Quiz Generation API

• URL: /quiz
• Method: POST
• Content-Type: multipart/form-data

✅ Request Parameters (form-data):

Key	Type	Description
text	string	The text from which you want to generate the quiz
context (optional)	string	Additional context if needed
question_count	int	Total number of questions to be generated for the quiz

---

🚀 Testing the API

You can test the API using tools like Postman:

1. Open Postman

2. Create a new POST request to: http://127.0.0.1:5000/summarize

3. Under the Headers tab, Postman will automatically set Content-Type when using form-data.

4. Go to the Body tab:

   • Select form-data
   • Add a key text, set its type to Text, and provide the input you want summarized
   • (Optional) Add a context key if needed

5. Click Send to receive the summarized output.

---

Common Issues

1. If you get a "Method Not Allowed" error:

   • Make sure you're using POST method, not GET
   • Check that the URL is correct

2. If you get an "Unsupported Media Type" error:

   • Ensure the Content-Type header is set to application/json
   • Make sure you're sending raw JSON data, not form-data

ML Backend Structure

ML_Backend/
├── app.py              # Main Flask application
├── requirements.txt    # Python dependencies
└── text_summarizer/    # Text summarization module
    ├── __init__.py
    └── summarizer.py   # Summarization implementation

Backend _ STT

The Backend is hosted on Glitch and thus no setup is required

Installation

1. Clone the repository:

cd STT_Backend

2. Create and activate a virtual environment:

# Windows
python -m venv venv2
.\venv2\Scripts\activate

# Linux/MacOS
python -m venv venv2
source venv2/bin/activate

3. Install the required dependencies:

Running the Application

The server is running at http://127.0.0.1:5000

API Endpoints

---

📄 Uploading Audio API

• URL: /upload-audio
• Method: POST
• Content-Type: multipart/form-data

✅ Request Parameters (form-data):

Key	Type	Description
audio	File	The audio file you need to send

---

📄 List Audios API

• URL: /list_audios
• Method: GET
• Content-Type: multipart/form-data

---

📄 Speech to text API

• URL: /speech-to-text-url
• Method: POST
• Content-Type: multipart/form-data

✅ Request Parameters (form-data):

Key	Type	Description
data	string	The data to be returned
audio-url	string	The url of the audio

---

STT Backend Structure

STT_Backend/
├── api.py              # Main Flask application and functionality
├── requirements.txt    # Python dependencies
├── start.sh            # Starting the server