Authors : Chinmay Tiwari; Juhi Pode; Shashank Borikar; Saundarya Raut

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/yfasjzre

Scribd : https://tinyurl.com/4mj74zns

DOI : https://doi.org/10.5281/zenodo.14442406

Abstract : In the Frappe framework, this study presents an AI-driven model for automating mistake detection and reaction. Users can report issues by sending screenshots straight over WhatsApp thanks to the model's integration of image captioning and WhatsApp. The picturecaptioning system analyses every snapshot to produce a descriptive caption that highlights possible mistake elements. It is based on a specially designed Convolutional Neural Network (CNN) and attention mechanisms. These captions are then used by the model to identify and categorize faults and associate them with previously mapped troubleshooting procedures. The strategy enables users to get prompt support responses within a recognizable messaging app by integrating WhatsApp. The model's ability to expedite support procedures forFrappe users is demonstrated by evaluation on sample error screenshots, which show efficient identification and prompt response.

Keywords : Error Detection, Whatsapp Integration, Image Captioning, ConvolutionalNeural Network (CNN), Real-Time Support, Error Classification, User-Friendly Messaging Platform,Error Response Automation

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