🧠 Depression Analysis API

A FastAPI-based web service that leverages Machine Learning and Deep Learning models to analyze and detect depression levels from user inputs.

🚀 Overview

This project implements a RESTful API using FastAPI to assess depression levels based on user-provided text. It integrates various ML/DL models to classify and predict depression severity.

🧩 Key Features

• FastAPI Backend: High-performance web framework for building APIs.
• Machine Learning Integration: Utilizes models trained to detect depression indicators.
• Deep Learning Models: Incorporates advanced neural networks for accurate predictions.
• Scalability: Designed to handle multiple requests efficiently.

🧠 Technologies Used

• FastAPI: Modern, fast (high-performance) web framework for building APIs with Python 3.6+.
• TensorFlow / PyTorch: Deep learning frameworks for model development.
• Scikit-learn: Machine learning library for model training and evaluation.
• Pandas / NumPy: Data manipulation and analysis tools.
• Uvicorn: ASGI server for serving FastAPI applications.

🛠️ Setup & Installation

Clone the Repository

git clone https://github.com/Kushan2k/depression-analysis-api-using-fastapi-python.git
cd depression-analysis-api-using-fastapi-python

Install Dependancies

pip install -r requirements.txt

Run the Application

uvicorn main:app --reload

The API will be accessible at http://127.0.0.1:8000

Project Structure

depression-analysis-api-using-fastapi-python/
├── data/                   # Dataset and preprocessing scripts
├── models/                 # Trained model files
├── routers/                # API route definitions
├── main.py                 # FastAPI application entry point
├── requirements.txt        # Python dependencies
├── README.md               # Project documentation
└── .gitignore              # Git ignore file

Model Details

• Model Type: Support Vector Classifier (SVC)
• Training Data: Custom dataset of text samples labeled with depression levels.
• Performance: Achieves high accuracy in classifying depression severity.

Contributing

Contributions are welcome! Please fork the repository, create a new branch, and submit a pull request with your proposed changes.