Weather Forecast Application

Description:

This project builds a robust and interactive web application that leverages the strengths of Streamlit, FastAPI, and a built-in machine learning model to accurately predict weather variables for a specified time frame.

Key Features:

• Integrated Machine Learning: Includes a trained weather prediction model within the project, eliminating the need for external resources.
• Streamlined Data Flow: FastAPI provides a RESTful API framework to efficiently manage data flow and model interactions.
• Intuitive Interface: Streamlit's user-friendly interface allows users to input weather variables and time frame parameters effortlessly.
• Clear Visualizations: Streamlit's versatile plotting capabilities create informative visualizations of weather variables across time.

Getting Started:

1. Prerequisites:
   • Basic understanding of Python programming.
   • Familiarity with Streamlit and FastAPI concepts.
2. Installation:
   • Clone the repository: git clone https://github.com/adejumoridwan/Weather-Forecast-Application.git
   • Navigate to the project directory: cd weather-forecast-application
   • Install dependencies: pip install -r requirements.txt
3. Run the application:
   • Start the API py backend/main.py
   • Start the Streamlit app: streamlit run frontend/main.py