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# π 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.
---
## π 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
---
## βοΈ Setup & Dependencies
Install required libraries:
```bash
pip install transformers gradio torch torchvision pillow
```
---
## π 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(
"## πΌοΈ 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 π€ Transformers (`nlpconnect/vit-gpt2-image-captioning`) and π 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.
---
## π 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 |
---
## π 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.
|