The detection and response to homophobicand transphobic comments are important challengesin Natural Language Processing. In thispaper, we focus on the detection of span forhomophobic and transphobic comments (Task1) and generation of counter narratives for abusivecomments (Task 2) for the LT-EDI @ ACL2026 shared task. Harmful comments madeonline against the LGBTQ+ community havecreated a hostile environment for users. In thispaper, we have used the transformer model forthe detection of span for homophobic and transphobiccomments and generation of counternarratives. In this task, the detection of the spanof comments containing homophobic and transphobicwords and the generation of counter narrativesfor abusive comments have been doneusing the transformer model. The results showthe efficiency of the transformer model in thedetection of the span of comments and generationof counter narratives. This paper emphasizesthe efficiency of the transformer model increating a safe environment for users.