README.md

metadata

title: Weather Wear
emoji: 🧥
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.35.0
python_version: '3.10'
app_file: app.py
pinned: false
short_description: AI meets your wardrobe. Weather decides the rest

Weather Wear

Weather Wear is a weather-based outfit recommendation web app that helps users decide what to wear according to the current weather condition of a selected location.

☀️Weather Wear

Weather Wear is a weather-based outfit recommendation web app that helps users decide what to wear according to the current weather condition of a selected location. The app fetches live weather data using a free weather API, displays weather details such as condition and temperature, and recommends suitable outfits from a CSV dataset based on the weather and selected style.

Features

• Live Weather Fetching: Gets real-time weather data for a user-entered location using a free weather API.
• Weather-Based Outfit Suggestions: Recommends clothes according to weather conditions such as sunny, rainy, cloudy, snowy, foggy, or stormy.
• Style Selection: Allows users to choose outfit styles such as casual, formal, sporty, party, or traditional.
• CSV-Based Dataset: Uses an outfit recommendation dataset stored in CSV format.
• Weather Data Storage: Saves fetched weather data into a CSV file using pandas.
• Interactive Dashboard: Built with Streamlit for a simple and user-friendly interface.
• Temperature Display: Shows the current temperature and weather details to the user.

---

Tech Stack

The project is built using the following technologies:

• Python: Core programming language used for the application logic.
• Streamlit: Used to build the interactive web application.
• Pandas: Used for reading the outfit dataset, transforming weather data, and saving weather API data into CSV format.
• Requests: Used to fetch data from the weather API.
• Open-Meteo API: A free weather API used to get live weather details without requiring an API key.

---

Dataset Format

The outfit recommendation dataset should be named:

outfit_recommendations.csv

The CSV file should contain columns similar to:

temp_min_c,temp_max_c,weather_condition,style,top,bottom,footwear,accessory,outerwear,notes
20,35,sunny,Casual,T-shirt,Jeans,Sneakers,Sunglasses,None,Light clothes are suitable for sunny weather
15,28,rainy,Formal,Shirt,Trousers,Formal shoes,Umbrella,Waterproof blazer,Carry rain protection
18,30,cloudy,Casual,Hoodie,Jeans,Sneakers,Watch,Light jacket,Comfortable outfit for cloudy weather

Main required columns:

• weather_condition
• style
• top
• bottom
• footwear
• accessory
• outerwear
• notes

---

Getting Started

Prerequisites

Make sure Python 3.8 or above is installed on your system.

Check your Python version:

python --version

---

Installation

1. Clone the repository:

git clone https://code.swecha.org/Dan/weather-wear.git
cd weather-wear

2. Create a virtual environment:

python -m venv venv

3. Activate the virtual environment:

On Windows:

venv\Scripts\activate

On macOS/Linux:

source venv/bin/activate

4. Install the required dependencies:

pip install streamlit pandas requests

5. Add the dataset:

Place your dataset file in the project folder:

outfit_recommendations.csv

---

Usage

Run the Streamlit app using:

streamlit run app.py

After running the command, open the local app URL in your browser:

http://localhost:8501

---

How It Works

1. The user enters a location in the sidebar.
2. The app fetches the location coordinates using the Open-Meteo Geocoding API.
3. The app fetches current weather data using the Open-Meteo Weather API.
4. The weather data is transformed using pandas and saved into a CSV file.
5. The app reads the outfit recommendation dataset.
6. Based on the current weather condition and selected style, the app displays a suitable outfit suggestion.

---

Example Outfit Recommendation

For a rainy day with casual style, the app may suggest:

Top: Hoodie
Bottom: Jeans
Footwear: Waterproof shoes
Accessory: Umbrella
Outerwear: Raincoat
Notes: Suitable for rainy weather. Carry rain protection.

---

Project Structure

weather-wear/
│
├── app.py
├── outfit_recommendations.csv
├── weather_data.csv
├── README.md
└── requirements.txt

---

Requirements File

You can create a requirements.txt file with:

streamlit
pandas
requests

Then install all dependencies using:

pip install -r requirements.txt

---

Roadmap

• Add more outfit styles.
• Add outfit images for better visual recommendations.
• Add support for multiple outfit suggestions.
• Improve weather condition matching.
• Add user profile-based preferences.
• Add seasonal recommendations.

---

Contributing

Contributions are welcome.

1. Fork the project.
2. Create a new feature branch:

git checkout -b feature/new-feature

3. Commit your changes:

git commit -m "Add new feature"

4. Push to your branch:

git push origin feature/new-feature

5. Open a Merge Request.

---

Author

• Dan - Initial work and development

---

License

This project is licensed under the MIT License.

---

Project Status

Active - The project is currently under development. ```