Chapter 14: Case Studies – Meme Generator, Cartoonizer, Chatbot¶
You're in the final act — time to bring everything together. Chapter 14 is a deep dive into real AI/ML projects you've built or can build, showing how each one connects the dots between models, deployment, APIs, and scalability. These case studies serve as templates for future production-ready tools.
Case Study 1: AI Meme Generator¶
"Give me a picture. I’ll give you a laugh."
Objective: Generate witty meme captions based on user input (text/image). Uses GPT for captions.
Stack Breakdown¶
| Layer | Tool |
|---|---|
| Frontend | React (Vercel) |
| Backend | FastAPI (Railway) |
| AI Model/API | OpenAI gpt-3.5-turbo |
| Hosting | Vercel (UI), Railway (API) |
Backend API Flow¶
- Receive prompt from frontend
- Query OpenAI with:
messages = [
{"role": "system", "content": "You are a witty meme caption generator."},
{"role": "user", "content": input.prompt}
]
- Return text output
Cool Add-ons¶
- Limit API calls per session (cooldown)
- Generate meme template + overlay text with Pillow
- Save memes to user account (e.g., Supabase)
- Export as PNG or share link
Case Study 2: Photo Cartoonizer¶
"Convert any selfie into anime-style or cartoon art."
Objective: Transform user-uploaded image into a cartoon using AI image-to-image models.
Stack Breakdown¶
| Layer | Tool |
|---|---|
| Frontend | Gradio or React (Hugging Face / Vercel) |
| Backend | FastAPI or pure Gradio |
| AI Model/API | Replicate API – cartoonify, U-GAT-IT |
| Hosting | Hugging Face Spaces (demo), Replicate |
Image Inference Flow¶
- User uploads image
- Backend calls Replicate with:
replicate.run(
"tstramer/cartoonify:latest",
input={"image": open(image_path, "rb")}
)
- Display output URL/image in frontend
Cool Add-ons¶
- Compare original vs cartoon (split view)
- Add filters (sepia, comic, black & white)
- Export to social media templates (Instagram post, story)
Case Study 3: Swift Chat AI¶
"A chatbot that remembers your vibes and chats naturally."
Objective: Create a simple chatbot UI that talks like a buddy, mentor, or assistant.
Stack Breakdown¶
| Layer | Tool |
|---|---|
| Frontend | React + Chat Bubbles (Vercel) |
| Backend | FastAPI |
| AI Model/API | OpenAI GPT-3.5 or Claude (Anthropic) |
| Hosting | Railway (API) + Vercel (UI) |
Chatbot Flow¶
- Frontend sends message
- Backend builds conversation context
- Sends to GPT:
messages = [{"role": "system", "content": "You are an empathetic mentor..."}]
- Returns chatbot response → updates UI
Cool Add-ons¶
- Memory: persist chat history per user
- Mood: toggle between funny, formal, or technical tone
- Voice: use text-to-speech (TTS) to read replies
- Auth: login with Google + per-user chat logs
Common Threads in All Projects¶
| Element | Importance |
|---|---|
.env for secrets |
Security for API keys |
.gitignore |
Avoid leaking local files & venv |
| Deployment CI/CD | Fast shipping via GitHub + Railway |
| Logs + limits | Control cost and debug issues |
| Modular folder structure | Enables multi-feature expansion |
Project Packaging & Showcasing¶
Every project should include:
- ✅
README.md(with badges + demo link + screenshots) - ✅
requirements.txt+.env.example - ✅ Clean folder structure (
backend/,frontend/) - ✅ GitHub project board for task breakdown
Next-Level Ideas (Pick One to Expand)¶
| Project Idea | Description |
|---|---|
| AI Sound Bender | Add music filters using AI models (DDSP, etc.) |
| FaceSwap Video Tool | Swap faces using lightweight face-mesh models |
| AutoSlogan Generator | GPT-based product tagline/slogan creator |
| Anime Frame Restorer | Use ESRGAN + restoration pipeline for upscaling |
Each of these can use your current stack + one new model/API!
Chapter Summary¶
- You now have 3 full project blueprints: Meme Generator, Cartoonizer, Chatbot
- You understand how to use OpenAI, Replicate, and Hugging Face in real apps
- You’ve unlocked ideas to refine, publish, and scale AI tools with flair 🚀