Adaptive Travel Itinerary Planning with AI Destinations and Weather Aware Adjustments

Abstract

 The proposed system is implemented as a Python-based Streamlit application that integrates multiple functional modules, including user authentication, weather data retrieval, destination selection, and itinerary generation. The system uses JSON-based local storage to manage user credentials, feedback records, and country metadata. Real-time weather data is fetched using an external weather API module, which processes and returns temperature and condition details for selected locations. The application dynamically generates travel itineraries based on user inputs, including destination, duration, and preferences, while incorporating weather conditions into its planning logic. The architecture follows a modular design consisting of input handling, API interaction, data processing, and output rendering components. The implementation demonstrates effective integration of API-driven data retrieval, session-based state management, and structured data handling using Python. The system highlights practical considerations, such as plaintext credential storage, dependency management, and basic feedback logging, while providing a functional prototype for adaptive travel planning.