Day 25: CaptionCraft: Build an Image Captioning App with BLIP and Gradio

Duration: 1-Day Workshop
Series: From Code to Cognition
Level: Intermediate (Python & ML basics)

Why This Matters on Day 25

As we hit Day 25 of the From Code to Cognition series, we shift gears from language-centric architectures to multimodal intelligence. Vision-language models like BLIP aren’t just impressive—they represent a leap in making machines see and describe the world the way humans do. CaptionCraft embodies that shift: from token sequences to image semantics.

Today’s build goes beyond theory. You’ll leave with a working app, deeper intuition for BLIP’s pipeline, and an interface that’s workshop-ready and community-shareable.

Overview

In this hands-on workshop, you'll build CaptionCraft—an app that transforms images into meaningful captions using the BLIP vision-language model. You’ll gain practical experience with pretrained AI models, understand image-text alignment, and deploy your tool using Gradio for easy accessibility.

What You’ll Learn

How BLIP interprets images and generates descriptive text
Best practices for model loading, UI integration, and error handling
How to enhance your captioning app with multilingual support, batch processing, and style filtering

Part 1: Introduction to BLIP

BLIP (Bootstrapped Language Image Pretraining) is a state-of-the-art model developed by Salesforce that bridges the gap between vision and language. Pretrained on massive image-text datasets, it powers tasks like:

Image captioning
Visual question answering
Image-text retrieval

For CaptionCraft, we use BLIP’s image captioning ability to turn uploaded visuals into readable, relevant descriptions.

Part 2: Implementation Breakdown

Step 1: Setup Your Environment

Install required libraries:

pip install torch torchvision transformers gradio pillow

Project structure:

  # Project Structure: CaptionCraft

  CaptionCraft/

    ├── app/

    │   ├── captioning.py

    │   └── interface.py

    ├── assets/       # Sample images

    ├── run.py        # Launcher

    ├── requirements.txt

    └── README.md

Step 2: Build a Robust Caption Generator

File: app/captioning.py

  // Vision-language captioning using BLIP

  from transformers import BlipProcessor, BlipForConditionalGeneration

  from PIL import Image

  import torch

  import logging

  logging.basicConfig(level=logging.INFO)

  class CaptionGenerator:

    def __init__(self):

      self.device = "cuda" if torch.cuda.is_available() else "cpu"

      logging.info(f"Using device: {self.device}")

      self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")

      self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(self.device)

    def generate_caption(self, image: Image.Image) -> str:

      try:

        inputs = self.processor(images=image, return_tensors="pt").to(self.device)

        output = self.model.generate(**inputs)

        caption = self.processor.decode(output[0], skip_special_tokens=True)

        return caption

      except Exception as e:

        return f"Error generating caption: {str(e)}"

  # Optional singleton loader

  captioner = CaptionGenerator()

Step 3: Create the Gradio UI

File: app/interface.py

  // Gradio interface for BLIP-powered captioning

  import gradio as gr

  from app.captioning import captioner

  def build_interface():

    def safe_generate_caption(image):

      return captioner.generate_caption(image)

    demo = gr.Interface(

      fn=safe_generate_caption,

      inputs=gr.Image(type="pil", label="Upload an Image"),

      outputs=gr.Textbox(label="Generated Caption"),

      title="CaptionCraft",

      description="BLIP-powered image captioning tool"

    )

    return demo

  if __name__ == "__main__":

    build_interface().launch()

Step 4: Launch the App

File: run.py

  // Run entry point for CaptionCraft

  if __name__ == "__main__":

    app = build_interface()

    app.launch()

Extension Ideas

For learners finishing early or working on stretch goals:

Multilingual Captions: Integrate translation APIs for Hindi, Telugu, etc.
Caption Styles: Add options like poetic, humorous, factual toggles.
Batch Captioning: Accept multiple image uploads or folders.
Downloadable Captions: Allow users to export results.
Spinner Feedback: Add loaders during generation for better UX.
Attention Maps: Visualize BLIP’s focus regions (advanced).

README Tips

Include the following in your README.md:

Project purpose and motivation
Setup instructions and dependency list
Screenshot or demo GIF
Example usage or sample output
License and contributor credits
Learning outcomes for workshop participants

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