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https://github.com/samsoumyajitd/food_ai
The AI Food Weather-Based Recommendation System provides personalized food and restaurant suggestions using AI. It uses GenAI and AI techniques like TF-IDF Vectorization, Cosine Similarity, and FuzzyWuzzy for tailored recommendations.
https://github.com/samsoumyajitd/food_ai
axios cosine-similarity flask flask-cors fuzzywuzzy generative-ai google-generativeai json nlp python-dotenv python3 reactjs scikit-learn sklearn tf-idf vanilla-css weather-api
Last synced: 6 days ago
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The AI Food Weather-Based Recommendation System provides personalized food and restaurant suggestions using AI. It uses GenAI and AI techniques like TF-IDF Vectorization, Cosine Similarity, and FuzzyWuzzy for tailored recommendations.
- Host: GitHub
- URL: https://github.com/samsoumyajitd/food_ai
- Owner: samSoumyajitD
- Created: 2025-01-21T06:56:06.000Z (10 days ago)
- Default Branch: master
- Last Pushed: 2025-01-22T06:24:30.000Z (9 days ago)
- Last Synced: 2025-01-22T07:27:56.556Z (9 days ago)
- Topics: axios, cosine-similarity, flask, flask-cors, fuzzywuzzy, generative-ai, google-generativeai, json, nlp, python-dotenv, python3, reactjs, scikit-learn, sklearn, tf-idf, vanilla-css, weather-api
- Language: Python
- Homepage:
- Size: 194 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: ReadMe.md
Awesome Lists containing this project
README
# AI Food Weather-Based Recommendation System
This project is a web-based application that provides food recommendations based on weather conditions, user preferences (vegetarian, non-vegetarian, or any), and cuisine type. Additionally, the app suggests popular restaurants known for specific food items in the user's locality. The backend is built with Flask and integrates AI-based recommendation logic, while the frontend is developed using React.
---
## Features
1. **Weather-Based Food Recommendations**: Fetches weather data for a given city and suggests food items suited to the current weather.
2. **User Preferences**: Allows users to specify dietary preferences (vegetarian, non-vegetarian, or any) and cuisine type.
3. **AI-Enhanced Recommendations**: Filters and refines recommendations using trained models and AI-based logic.
4. **Restaurant Suggestions**: Displays a list of well-known restaurants for the recommended food items in the user's locality.
5. **Responsive Frontend**: Provides a clean and interactive UI for users to input their preferences and view recommendations.
---
## Technologies Used
### Backend
- **Flask**: A lightweight Python web framework.
- **scikit-learn**: For AI-based filtering and recommendation logic.
- **Google Generative AI API**: To enhance food suggestions based on weather and user inputs.
- **OpenWeatherMap API**: For fetching real-time weather data.
- **Google Places API**: To find popular restaurants for the recommended food items in the user's locality.
- **Flask-CORS**: To handle cross-origin requests.
- **FuzzyWuzzy**: For fuzzy matching of inputs (e.g., city names, cuisine types).
### Frontend
- **React**: For building a responsive user interface.
- **Axios**: For making API calls to the Flask backend.
- **CSS**: Custom styles for a clean UI.
---
## Application Structure
```
project-root/
│
├── backend/
│ ├── ai_recommendations.py
│ ├── app.py
│ ├── config.py
│ ├── cuisines.json
│ ├── myFood.json
│ ├── recommendations.py
│ ├── requirements.txt
│ └── .env
│
├── frontend/
│ ├── public/
│ │ └── index.html
│ ├── src/
│ │ ├── components/
│ │ │ ├── FoodRecommendation.jsx
│ │ │ └── FoodRecom.css
│ │ ├── App.css
│ │ ├── App.js
│ │ ├── index.css
│ │ └── index.js
│ └── package.json
│
└── README.md
```
---
## Prerequisites
1. **Python 3.8+**
2. **Node.js 14+**
3. **npm or Yarn**
4. API Keys for:
- OpenWeatherMap API
- Google Generative AI API
- Google Places API
---
## Installation Guide
### Backend Setup
1. **Navigate to the `backend` directory**:
```
cd backend
python -m venv venv
venv\Scripts\activate
```
2. **Install Python dependencies**:
```
pip install -r requirements.txt
pip install --upgrade Flask Werkzeug
pip install flask-cors
pip install python-Levenshtein
```
3. **Set up the `.env` file**:
Create a `.env` file in the `backend` directory with the following keys:
```
WEATHER_API_KEY=
GENAI_API_KEY=
PLACES_API_KEY=
CORS_ORIGIN=http://localhost:3000
```
4. **Run the Flask application**:
```
python app.py
```
The backend will run on `http://127.0.0.1:5000`.
### Frontend Setup
1. **Navigate to the `frontend` directory**:
```
cd frontend
```
2. **Install dependencies**:
```
npm i
```
3. **Run the React application**:
```
npm start
```
The frontend will run on `http://localhost:3000`.
---
## Usage
1. Open the browser and navigate to `http://localhost:3000`.
2. Enter a city name and specify your preferences:
- Dietary preference (vegetarian, non-vegetarian, or any).
- Cuisine type (e.g., Indian, Continental, etc.).
3. Click on the "Get Recommendations" button.
4. View the list of recommended food items and popular restaurants in your locality for those items.
---
## Dependencies
### Backend
- `flask`
- `flask-cors`
- `requests`
- `scikit-learn`
- `google-generativeai`
- `google-places`
- `python-dotenv`
- `fuzzywuzzy`
Install all dependencies via:
```bash
pip install -r requirements.txt
```
### Frontend
- `react`
- `axios`
Install all dependencies via:
```bash
npm install
```
---
## Example Workflow
1. **Input**:
- City: `New York`
- Preference: `Veg`
- Cuisine Type: `Indian`
2. **Backend Process**:
- Fetch weather data for New York.
- Use the weather and user inputs to request food suggestions from the Generative AI API.
- Filter and refine results using the trained AI model.
- Use Google Places API to find popular restaurants for the recommended food items in New York.
3. **Output**:
- A list of vegetarian Indian food items suitable for the current weather in New York.
- Suggested restaurants in New York known for these food items.
---
## AI and NLP Techniques Employed
### 1. **TF-IDF Vectorization**
Transforms textual food data into numerical representations for efficient analysis.
### 2. **Cosine Similarity**
Measures similarity between user preferences and available food recommendations.
### 3. **Fuzzy String Matching**
Handles imperfect user inputs for better robustness.
---
## Contributing
Contributions are welcome! Feel free to open issues or submit pull requests.
---