AI-Powered Image Captioning and Content Generation: Design, Implementation, and Evaluation

Abstract: Image captioning models describe what a photograph shows, but people who publish images online usually need that description reshaped for a particular language, platform, and tone. This paper presents the design, implementation, and evaluation of a web application that bridges this gap. A BLIP captioning model fine-tuned on Flickr8k produces a base caption; a large language model served through the Groq API rewrites it for a selected platform, language (English, Hindi, or Marathi), and writing style; and a Pillow-based renderer places the result on the image. A Streamlit interface, a FastAPI backend, and a SQLite history store complete the system. On 1,000 Flickr8k test images the captioner obtained BLEU 0.2999, METEOR 0.5697, and CIDEr 0.5191. Ten-run timing tests gave a mean of 7.35 s for caption generation and 23.0 s end to end. Limitations of the evidence are discussed.

Keywords: Image captioning; BLIP; vision-language models; large language models; multilingual content generation; Flickr8k