# 📖 Code Explanation: Image Caption Generator This document explains the **Image Caption Generator** app, which uses a ViT+GPT2 model to generate descriptive captions for uploaded images. --- ## 📝 Overview **Purpose** Upload an image and receive a concise, descriptive caption generated by a Vision Transformer (ViT) combined with GPT-2. **Tech Stack** - **Model**: `nlpconnect/vit-gpt2-image-captioning` (Vision Transformer + GPT-2) - **Precision**: `torch_dtype=torch.bfloat16` for reduced memory usage and faster inference on supported hardware - **Interface**: Gradio Blocks + Image + Textbox --- ## ⚙️ Setup & Dependencies Install required libraries: ```bash pip install transformers gradio torch torchvision pillow ``` --- ## 🔍 Detailed Block-by-Block Code Explanation ```python import torch import gradio as gr from transformers import pipeline # 1) Load the image-to-text pipeline captioner = pipeline( "image-to-text", model="nlpconnect/vit-gpt2-image-captioning", torch_dtype=torch.bfloat16 ) # 2) Caption generation function def generate_caption(image): outputs = captioner(image) return outputs[0]["generated_text"] # 3) Build Gradio interface with gr.Blocks(theme=gr.themes.Default()) as demo: gr.Markdown( "## 🖼️ Image Caption Generator " "Upload an image to generate a descriptive caption using ViT+GPT2." ) with gr.Row(): input_image = gr.Image(type="pil", label="Upload Image") caption_output = gr.Textbox(label="Generated Caption", lines=2) generate_btn = gr.Button("Generate Caption") generate_btn.click(fn=generate_caption, inputs=input_image, outputs=caption_output) gr.Markdown( "--- " "Built with 🤗 Transformers (`nlpconnect/vit-gpt2-image-captioning`) and 🚀 Gradio" ) demo.launch() ``` **Explanation:** 1. **Imports**: - `torch` for tensor operations and bfloat16 support. - `gradio` for the web interface. - `pipeline` from Transformers to load the image-captioning model. 2. **Pipeline Loading**: - `"image-to-text"` task uses a ViT encoder and GPT-2 decoder. - Loading with half-precision reduces memory use and speeds up inference. 3. **Caption Function**: - Accepts a PIL image, runs the pipeline, and returns the generated caption text. 4. **Gradio UI**: - Uses **Blocks** and **Row** to layout the uploader and output. - **Image** component accepts uploaded images. - **Textbox** displays the generated caption. - **Button** triggers caption generation when clicked. --- ## 🚀 Core Concepts | Concept | Why It Matters | |-----------------------------|---------------------------------------------------------------| | Vision Transformer (ViT) | Extracts visual features from images | | GPT-2 Decoder | Generates natural language text from visual features | | bfloat16 Precision | Lowers memory usage and speeds up inference on supported HW | | Gradio Blocks & Components | Simplifies web app creation without frontend coding | --- ## 🔄 Extensions & Alternatives - **Alternate Captioning Models**: - `Salesforce/blip-image-captioning-base` - `microsoft/git-base-coco` - **UI Enhancements**: - Allow batch upload of multiple images. - Display generated captions alongside thumbnails. - Add option to download captions as a text file. - **Advanced Features**: - Fine-tune the model on a custom image dataset for domain-specific descriptions. - Integrate with image galleries or social media platforms for auto-captioning.