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@article{181084,
        author = {Samiya Mahveen and Nazneen Muskaan and Shaheen Begum},
        title = {Smart Weather Voice Assistant – A Voice-Integrated Weather Forecasting Application},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {1},
        pages = {3357-3359},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181084},
        abstract = {Weather is a critical factor in daily decision
making, especially for vulnerable communities 
including the differently-abled. Existing weather apps 
predominantly rely on graphical user interfaces, often 
creating accessibility barriers. This paper presents a 
Smart Weather Voice Assistant—an inclusive and real
time voice-integrated system that empowers users 
through spoken weather and air quality updates. Built 
using Python and Streamlit, and powered by APIs such 
as 
OpenWeatherMap, OpenCage Geocoder, and 
AirVisual, the system integrates Google Text-to-Speech 
(gTTS) for converting weather data into natural speech. 
By enabling IP-based geolocation, screen-reader
friendly UI, and low-resource deployment, this assistant 
bridges the accessibility gap and supports proactive 
environmental awareness.},
        keywords = {Voice Assistant, Weather Forecasting,  Accessibility, Streamlit, gTTS, Real-time APIs,  Differently-abled Users, Air Quality, IP Geolocation.},
        month = {},
        }