Customized_AI_Kitchen

Intel Project

Overview

Customized_AI_Kitchen is a Flask-based web application that helps users identify ingredients in uploaded images and adjust recipe quantities based on the number of servings. The application uses a pre-trained TensorFlow model for image classification and processes recipes from a CSV dataset.

Download Our Custom Dataset

Custom Database

Dataset Overview

Our custom dataset consists of images categorized into 36 distinct classes. These classes represent various ingredients commonly used in cooking. The class names include:

• Asafoetida
• Brown rice
• Butter
• Cabbage
• Capsicum
• Cardamom powder
• Carom seeds
• Carrot
• Coriander leaves
• Curry leaves
• Dry red chili
• Eggplant
• Fenugreek leaves
• Fenugreek seeds
• Garlic
• Ginger
• Green chili
• Jaggery
• Lemon
• Mango
• Milk
• Mustard seeds
• Onion
• Raw mango
• Red chili powder
• Ridge gourd skin
• Rosemary
• Saffron strands
• Sugar
• Sunflower oil
• Tamarind
• Tea leaves
• Tomato
• Turmeric powder
• White lentils
• Yellow corn meal flour

The dataset is organized into three main directories:

1. Train: Contains images used for training the model.
2. Test: Contains images used for testing the model's performance.
3. Validation: Contains images used for validating the model during training to prevent overfitting.

This well-structured dataset ensures comprehensive coverage of each class, enabling robust training and accurate classification by the model.

Table of Contents

1. Installation
2. Setup
3. Running the Application
4. File Structure

Installation

Clone the Repository

git clone
cd Customized_AI_Kitchen

Uninstall Dependencies

• To prevent conflicts between libraries, uninstall the following packages:

pip uninstall -y Flask
pip uninstall -y pandas
pip uninstall -y numpy
pip uninstall -y tensorflow
pip uninstall -y Werkzeug
pip uninstall -y matplotlib
pip uninstall -y Flask-WTF

Install Dependencies

pip install -r requirements.txt

Setup

Download the Dataset and Model

1. Custom_Recipe_Dataset.csv:

1. Ensure you have Custom_Recipe_Dataset.csv.
2. Place it in the directory Customized_AI_Kitchen/.

2. Custom_Image_Classification_Trained_Model.h5:

1. Ensure you have Custom_Image_Classification_Trained_Model.h5.
2. Place it in the directory Customized_AI_Kitchen/.

Running the Application

Run the Flask Application

python AIKitchen.py

• Access the application at http://127.0.0.1:5000/.

File Structure

Customized_AI_Kitchen/
│
├── static/
│   ├── backgrounds/
│   │   ├── background1.jpg
│   │   ├── background2.jpg
│   │   ├── background3.jpg
│   │   ├── background4.jpg
│   │   └── background5.jpg
│   ├── uploaded_images/
│   └── styles.css
│
├── templates/
│   ├── action.html
│   ├── base.html
│   ├── display_images.html
│   ├── home.html
│   ├── index.html
│   ├── missing.html
│   └── recipe.html
│   └── upload.html
│
├── .gitattributes
├── .gitignore
├── AIKitchen.py
├── Custom_Image_Classification_Trained_Model.h5
├── Custom_Recipe_Dataset.csv
├── README.md
└── requirements.txt

Note

• Ensure that the library versions are accurately installed and that the processes are carefully followed. If there are any errors, please refer to the tutorial video.
• Ensure all file paths are correct.

Tutorial Video

• Please follow the video closely.
  [Intel Project Running Tutorial]