Sherlyn Olalo
A conversational AI weather assistant built with Flask and spaCy (with rapidfuzz for fuzzy matching), integrated with the OpenWeather API to deliver real-time forecasts through a lightweight chat interface.
Weather forecasting tools often overwhelm users with data or require navigating clunky interfaces. People needed a natural way to ask weather questions and get clear, actionable answers — whether planning a trip, deciding what to wear, or checking conditions for outdoor activities. Existing solutions forced users into rigid search patterns instead of conversational queries.
Skyler Chatbot bridges this gap with a conversational interface that understands natural language weather queries. The assistant processes user questions, fetches real-time data from OpenWeather API, and responds with clear, personalized insights.
Tech stack: Flask serves the app and routes the chat, spaCy + rapidfuzz handle the natural-language layer, OpenWeather feeds the live data, and HTML/CSS/JS keeps the UI light.
Natural-language parsing — spaCy extracts intent and entities (locations, time windows) from messy user phrasing; rapidfuzz catches typos and city-name variants so "Cebu Cty" still resolves to Cebu City
Conversational design — Users ask questions naturally ("Will it rain tomorrow?" or "What's the warmest time today?") instead of navigating menus
Real-time data integration — Direct OpenWeather API connection ensures current, accurate forecasts
Clean chat interface — A lightweight HTML/CSS/JS frontend keeps the tool accessible and snappy
This project taught me the power of natural language interfaces — even simple conversational flows make tools dramatically more usable. I learned to think about API integration as part of the UX, not just a technical detail. If I revisited it, I'd add location detection and multi-day forecast summaries for better context.