NutriGenius - FastAPI Backend & ML Model

This repository contains the backend server and the machine learning model for NutriGenius, a smart recipe recommendation system. The backend is built with Python and FastAPI, and it serves personalized meal recommendations based on user-provided health data and preferences.

✨ Features

• FastAPI Framework: A modern, high-performance web framework for building APIs with Python.
• Content-Based Filtering Model: A custom-trained machine learning model that recommends recipes based on ingredient similarity, diet, course, and user goals.
• Dynamic Image Fetching: Uses DuckDuckGo Search to dynamically find relevant images for each recommended recipe, without needing API keys.
• CORS Enabled: Properly configured to accept requests from any frontend origin.
• Ready for Deployment: Includes a Procfile and requirements.txt for easy deployment on platforms like Render.

🛠️ Tech Stack

• Language: Python
• Web Framework: FastAPI
• ML & Data Processing: Pandas, Scikit-learn
• Image Search: duckduckgo-search
• Server: Uvicorn

🚀 Getting Started

Follow these instructions to get the backend server running on your local machine.

Prerequisites

• Python (v3.9 or later)
• pip and venv

Installation

1. Clone the repository:

   git clone [https://github.com/your-username/nutrigenius-backend.git](https://github.com/your-username/nutrigenius-backend.git)
   cd nutrigenius-backend

2. Create and activate a virtual environment:

   # For Windows
   python -m venv venv
   .\venv\Scripts\activate
   
   # For macOS/Linux
   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

Running the Server

With your virtual environment activated, run the following command to start the local development server:

uvicorn main:app --reload

The API will be available at http://127.0.0.1:8000. You can access the interactive API documentation (powered by Swagger UI) at http://127.0.0.1:8000/docs.

📖 API Endpoints

1. Recommend Recipes

• Endpoint: POST /recommend
• Description: Takes user data and returns a list of personalized recipe recommendations.
• Request Body:

  {
    "height_cm": 175,
    "weight_kg": 70,
    "desired_ingredients": "paneer, onion",
    "user_allergies": "nuts",
    "meal_type": "Dinner",
    "weight_goal": "Lose",
    "max_cooking_time": 60,
    "diet_preference": "Vegetarian"
  }

• Success Response (200 OK): An array of recipe objects.

2. Get Recipe Procedure

• Endpoint: GET /procedure/{recipe_id}
• Description: Retrieves the step-by-step cooking instructions for a specific recipe ID.
• Success Response (200 OK):

  {
    "steps": ["Step 1...", "Step 2...", "Step 3..."]
  }

🚢 Deployment

This project is configured for easy deployment on Render.

1. Create requirements.txt: If you've added new packages, update the file:

   pip freeze > requirements.txt

2. Create Procfile: Ensure a Procfile exists in the root directory with the following content:

   web: uvicorn main:app --host 0.0.0.0 --port $PORT
   

3. Push to GitHub: Commit and push your latest changes to a GitHub repository.

4. Deploy on Render:

   • Create a new Web Service on Render and connect your GitHub repository.
   • Set the Runtime to Python 3.
   • Set the Build Command to pip install -r requirements.txt.
   • Set the Start Command to uvicorn main:app --host 0.0.0.0 --port $PORT.
   • Add an environment variable with PYTHON_VERSION as the key and 3.11.9 as the value.
   • Click Create Web Service. Render will build and deploy your API.