How to Build an AI That Roasts Your Spending Habits (3 hours Weekend Project)

Building an AI Financial Roaster That People Actually Want to Use Let me show you how to build the most brutally honest financial advisor you'll ever meet: an AI that doesn't care about your feelings. I love building things that people actually use. That is why I wanted to create a tutorial project that would teach developers how to make AI applications that people actually enjoy using. Because let's be honest, most AI tutorials are boring calculators and chatbots. The project? Finance Roaster: Users upload their bank statement, get roasted by GPT-4 with savage, witty humor. The project? This tutorial will teach you: How to process CSV and PDF bank statements How to engineer AI prompts for humor and personality How to build shareable, viral-worthy UI/UX How to create an AI app in under 100 lines of Python How to process CSV and PDF bank statements How to engineer AI prompts for humor and personality How to build shareable, viral-worthy UI/UX How to create an AI app in under 100 lines of Python By the end of this guide, you'll have a fully functional app you can deploy this weekend. The Psychology: Why This Project Concept Works Before diving into code, let's talk about why this makes such a great tutorial project. Traditional budgeting apps are boring. They show you pie charts and tell you to "reduce discretionary spending." Not exactly share-worthy. But an AI that says: "$87 at Foods Co for organic kale? Your wallet is as wilted as that kale will be in three days"? That's memorable. That's the kind of feature that makes people actually want to test your app. Three principles that make this project interesting: Three principles that make this project interesting: Humor makes boring tasks fun - People avoid checking their finances because it's painful. Adding humor changes that dynamic. Highly shareable - When you build something funny, people naturally want to show friends. This teaches you virality mechanics. Practical learning - You'll learn file uploads, AI prompt engineering, and building clean UIs, all transferable skills. Humor makes boring tasks fun - People avoid checking their finances because it's painful. Adding humor changes that dynamic. Humor makes boring tasks fun Highly shareable - When you build something funny, people naturally want to show friends. This teaches you virality mechanics. Highly shareable Practical learning - You'll learn file uploads, AI prompt engineering, and building clean UIs, all transferable skills. Practical learning The Tech Stack (Perfect for Learning) I deliberately chose a simple stack that beginners can understand while still being production-ready. What we'll use: What we'll use: Backend: Flask (Python) - 100 lines of code AI Engine: OpenAI's GPT-4o-mini ($0.15 per 1M tokens) File Processing: Pandas for CSV, pdfplumber for PDF statements Frontend: Single HTML page with Tailwind CSS Cost per request: ~$0.01 Backend: Flask (Python) - 100 lines of code Backend AI Engine: OpenAI's GPT-4o-mini ($0.15 per 1M tokens) AI Engine File Processing: Pandas for CSV, pdfplumber for PDF statements File Processing Frontend: Single HTML page with Tailwind CSS Frontend Cost per request: ~$0.01 Cost per request Why this stack is perfect for learning: Why this stack is perfect for learning: No deployment complexity (runs locally or on a single server) Sub-3-second response times Teaches you file handling, AI integration, and modern UI design Easily extensible (add features as you learn more) No deployment complexity (runs locally or on a single server) Sub-3-second response times Teaches you file handling, AI integration, and modern UI design Easily extensible (add features as you learn more) Part 1: Making AI Understand Money (The Easy Part) First challenge: How do you teach an AI to roast someone's spending? Step 1: Extract Transaction Data Most banks export statements as CSV or PDF. Here's the extraction logic: import pandas as pd import pdfplumber from openai import OpenAI

def process_csv(file): """Extract spending summary from CSV bank statement""" df = pd.read_csv(file) # Find the amount column (banks use different names) amount_col = next( (col for col in df.columns if any(kw in col.lower() for kw in ['amount', 'debit', 'spent'])), df.columns[-1] # Default to last column ) # Convert to numeric, handle currency symbols df[amount_col] = pd.to_numeric( df[amount_col].astype(str).str.replace('$', '').str.replace(',', ''), errors='coerce' ) # Calculate summary total_spent = df[amount_col].abs().sum() top_expenses = df.nlargest(5, amount_col) return { 'total_spent': f"${total_spent:,.2f}", 'top_expenses': top_expenses.to_dict('records') } import pandas as pd import pdfplumber from openai import OpenAI

def process_csv(file): """Extract spending summary from CSV bank statement""" df = pd.read_csv(file) # Find the amount column (banks use different names) amount_col = next( (col for col in df.columns if any(kw in col.lower() for kw in ['amount', 'debit', 'spent'])), df.columns[-1] # Default to last column ) # Convert to numeric, handle currency symbols df[amount_col] = pd.to_numeric( df[amount_col].astype(str).str.replace('$', '').str.replace(',', ''), errors='coerce' ) # Calculate summary total_spent = df[amount_col].abs().sum() top_expenses = df.nlargest(5, amount_col) return { 'total_spent': f"${total_spent:,.2f}", 'top_expenses': top_expenses.to_dict('records') } PDF handling is trickier because banks format statements differently. Solution? Use pdfplumber for text extraction, then let GPT-4 parse it: PDF handling is trickier def extract_transactions_from_pdf(pdf_text): """Use AI as a fallback parser for complex PDFs""" response = client.chat.completions.create( model="gpt-4o-mini", messages=[{ "role": "system", "content": "Extract transactions: Date | Description | Amount" }, { "role": "user", "content": pdf_text[:4000] # Limit tokens }] ) # Parse AI response into structured data # ... def extract_transactions_from_pdf(pdf_text): """Use AI as a fallback parser for complex PDFs""" response = client.chat.completions.create( model="gpt-4o-mini", messages=[{ "role": "system", "content": "Extract transactions: Date | Description | Amount" }, { "role": "user", "content": pdf_text[:4000] # Limit tokens }] ) # Parse AI response into structured data # ... Pro tip: AI is surprisingly good at parsing messy financial data. I tried regex patterns for weeks before realizing GPT-4 could do it in one call. Pro tip: Part 2: Teaching AI to Be Savage (The Fun Part) Now the magic: turning transaction data into comedy gold. The System Prompt (Your AI's Personality) This is where most people fail. A generic "be funny" prompt produces generic humor. You need specificity. Here's my system prompt: SYSTEM_PROMPT = """ You are a Savage Financial Parent - brutally honest, wickedly funny, and armed with someone's transaction history.

Your job: Roast their spending habits using SPECIFIC transaction details.

Style guide: - Think disappointed parent meets stand-up comedian - Use their actual purchase names (e.g., "Taco Bell at 2 AM") - Be savage but not cruel - make them laugh while crying - End with a Financial Maturity Grade (A-F) and one-line explanation

Examples of good roasts: - "Five Starbucks trips in one day? That's not a coffee addiction, that's a $30 therapy session with extra foam." - "You spent $200 on DoorDash this month. That's literally paying someone $8 to make your laziness official."

Keep it under 200 words. Make it screenshot-worthy. """ SYSTEM_PROMPT = """ You are a Savage Financial Parent - brutally honest, wickedly funny, and armed with someone's transaction history.

Your job: Roast their spending habits using SPECIFIC transaction details.

Style guide: - Think disappointed parent meets stand-up comedian - Use their actual purchase names (e.g., "Taco Bell at 2 AM") - Be savage but not cruel - make them laugh while crying - End with a Financial Maturity Grade (A-F) and one-line explanation

Examples of good roasts: - "Five Starbucks trips in one day? That's not a coffee addiction, that's a $30 therapy session with extra foam." - "You spent $200 on DoorDash this month. That's literally paying someone $8 to make your laziness official."

Keep it under 200 words. Make it screenshot-worthy. """ Why this works: Why this works: Specific examples teach the AI your humor style Constraints (200 words) force concise, punchy writing "Screenshot-worthy" reminds the AI this is for social sharing Specific examples teach the AI your humor style Specific examples Constraints (200 words) force concise, punchy writing Constraints "Screenshot-worthy" reminds the AI this is for social sharing "Screenshot-worthy" The Actual API Call def generate_roast(summary_data): """Send transaction summary to OpenAI, get roast back""" prompt = f""" Transaction Data: - Total Spent: {summary_data['total_spent']} - Top Expense: {summary_data['top_expenses'][0]} - You went to {summary_data['frequent_vendors'][0]['vendor']} {summary_data['frequent_vendors'][0]['count']} times

Roast these spending habits. Be specific and savage. """ response = client.chat.completions.create( model="gpt-4o-mini", messages=[ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": prompt} ], temperature=0.9 # High creativity for humor ) return response.choices[0].message.content def generate_roast(summary_data): """Send transaction summary to OpenAI, get roast back""" prompt = f""" Transaction Data: - Total Spent: {summary_data['total_spent']} - Top Expense: {summary_data['top_expenses'][0]} - You went to {summary_data['frequent_vendors'][0]['vendor']} {summary_data['frequent_vendors'][0]['count']} times

Roast these spending habits. Be specific and savage. """ response = client.chat.completions.create( model="gpt-4o-mini", messages=[ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": prompt} ], temperature=0.9 # High creativity for humor ) return response.choices[0].message.content The temperature setting (0.9) is crucial. Lower values make AI boring. Higher values make it creative... sometimes too creative. 0.9 is the sweet spot for comedy. The temperature setting (0.9) is crucial. Part 3: The UI That Makes People Click "Roast Me" Nobody uploads bank statements to ugly websites. Your UI needs to be: Dark and edgy (matches the roasting vibe) Dead simple (one button: "Roast Me") Fast (loading states matter) Dark and edgy (matches the roasting vibe) Dark and edgy Dead simple (one button: "Roast Me") Dead simple Fast (loading states matter) Fast The Landing Page 💸 Finance Roaster Upload Your Regrets. Get Brutally Honest Feedback.

Drop your bank statement here

CSV or PDF • Max 16MB

🔥 Roast Me 💸 Finance Roaster Upload Your Regrets. Get Brutally Honest Feedback.

Drop your bank statement here

CSV or PDF • Max 16MB

🔥 Roast Me Key design choices: Key design choices: Red/pink gradient: Danger + playfulness Dark background: Focuses attention on the upload area Emojis: Adds personality without extra design work Tailwind CSS: Rapid prototyping without writing CSS Red/pink gradient: Danger + playfulness Red/pink gradient Dark background: Focuses attention on the upload area Dark background Emojis: Adds personality without extra design work Emojis Tailwind CSS: Rapid prototyping without writing CSS Tailwind CSS The Loading State (Critical for Virality) While the AI thinks, show personality: function showLoading() { document.getElementById('loadingState').innerHTML = ` Analyzing your poor life choices...

This won't take long. Unlike your debt.

`; } function showLoading() { document.getElementById('loadingState').innerHTML = ` Analyzing your poor life choices...

This won't take long. Unlike your debt.

`; } Why this matters: Users wait 3-5 seconds for AI responses. A boring spinner loses attention. A funny loading message keeps them engaged. Why this matters: Part 4: Security (Because Banks Care About This) You're handling financial data. Even for a joke app, security matters. Three Non-Negotiable Rules: 1. Never store files on disk 1. Never store files on disk # ❌ BAD: Saves file to server file.save('statements/' + filename)

# ✅ GOOD: Process in-memory file_bytes = io.BytesIO(file.read()) df = pd.read_csv(file_bytes) # ❌ BAD: Saves file to server file.save('statements/' + filename)

# ✅ GOOD: Process in-memory file_bytes = io.BytesIO(file.read()) df = pd.read_csv(file_bytes) 2. Don't log transaction details 2. Don't log transaction details # ❌ BAD: Logs sensitive data print(f"User spent ${amount} at {merchant}")

# ✅ GOOD: Generic logging print(f"Processing statement with {len(transactions)} transactions") # ❌ BAD: Logs sensitive data print(f"User spent ${amount} at {merchant}")

# ✅ GOOD: Generic logging print(f"Processing statement with {len(transactions)} transactions") 3. Strip personal info before sending to OpenAI 3. Strip personal info before sending to OpenAI # Remove account numbers, names, addresses summary_data = { 'total_spent': total, 'top_categories': categories, # "Food", not "Joe's Pizza" 'spending_pattern': pattern } # Remove account numbers, names, addresses summary_data = { 'total_spent': total, 'top_categories': categories, # "Food", not "Joe's Pizza" 'spending_pattern': pattern } Reality check: Even with sanitization, tell users their data goes to OpenAI. Transparency builds trust. Reality check: Part 5: Adding Viral Features (Optional Enhancement) If you want to take this tutorial further, here's how to add shareability: Built-in Sharing Capability: 1. One-Click Social Sharing 1. One-Click Social Sharing function shareRoast() { const roastText = document.getElementById('roast').textContent; const shareText = `I just got my spending roasted by AI! 🔥\n\n"${roastText.substring(0, 200)}..."\n\nBuild your own: [your-github-link]`; if (navigator.share) { navigator.share({ text: shareText }); } else { navigator.clipboard.writeText(shareText); alert('Roast copied! Share with friends! 🔥'); } } function shareRoast() { const roastText = document.getElementById('roast').textContent; const shareText = `I just got my spending roasted by AI! 🔥\n\n"${roastText.substring(0, 200)}..."\n\nBuild your own: [your-github-link]`; if (navigator.share) { navigator.share({ text: shareText }); } else { navigator.clipboard.writeText(shareText); alert('Roast copied! Share with friends! 🔥'); } } Why this matters: If you eventually deploy this publicly, sharing features help spread awareness. Why this matters: 2. The Financial Maturity Grade 2. The Financial Maturity Grade Every roast ends with a grade: A through F. Financial Maturity Grade: D- "You have the self-control of a toddler in a candy store, except the candy is overpriced artisanal coffee." Financial Maturity Grade: D- "You have the self-control of a toddler in a candy store, except the candy is overpriced artisanal coffee." Learning point: Grades, scores, and rankings make results more shareable and comparable. Learning point: What You'll Learn Building This This project teaches you several valuable skills: File Processing: File Processing: Handle both CSV and PDF uploads Parse unstructured financial data Work with Pandas for data analysis Handle both CSV and PDF uploads Parse unstructured financial data Work with Pandas for data analysis AI Engineering: AI Engineering: Craft effective system prompts for personality Temperature tuning for creative outputs Handle AI responses in production Craft effective system prompts for personality Temperature tuning for creative outputs Handle AI responses in production Full-Stack Development: Full-Stack Development: Build clean REST APIs with Flask Create engaging UIs with Tailwind CSS Implement drag-and-drop file uploads Handle loading states and user feedback Build clean REST APIs with Flask Create engaging UIs with Tailwind CSS Implement drag-and-drop file uploads Handle loading states and user feedback Deployment Ready: Deployment Ready: In-memory file processing (no disk storage) Error handling and validation Security best practices for financial data In-memory file processing (no disk storage) Error handling and validation Security best practices for financial data Design Decisions: Why I Built It This Way What Works in This Architecture: Simplicity over features: A single-purpose app is easier to understand and build. You can always add features later once you understand the core. Humor as the hook: The AI's personality is what makes this project interesting. Technical tutorials don't have to be boring. File processing in memory: No database needed for this tutorial. Everything happens in RAM, making deployment simple. Simplicity over features: A single-purpose app is easier to understand and build. You can always add features later once you understand the core. Simplicity over features Humor as the hook: The AI's personality is what makes this project interesting. Technical tutorials don't have to be boring. Humor as the hook File processing in memory: No database needed for this tutorial. Everything happens in RAM, making deployment simple. File processing in memory What You Might Customize: Add comparison data: "You spent more on coffee than X% of typical users" - requires a database to track aggregates. Mobile-first design: The current UI works on mobile, but you could optimize it further with responsive breakpoints. Prompt variations: The system prompt I provide is a starting point. Experiment with different personalities and tones. Add comparison data: "You spent more on coffee than X% of typical users" - requires a database to track aggregates. Add comparison data Mobile-first design: The current UI works on mobile, but you could optimize it further with responsive breakpoints. Mobile-first design Prompt variations: The system prompt I provide is a starting point. Experiment with different personalities and tones. Prompt variations Build Your Own AI App: What This Template Teaches This project is designed to be a template for other AI applications. Here's how to adapt it: 1. Find a task people find tedious 1. Find a task people find tedious Checking finances → This tutorial Reading legal documents → "Legal Jargon Translator" Analyzing health data → "Fitness Report Card" Reviewing meeting notes → "Meeting BS Detector" Checking finances → This tutorial Reading legal documents → "Legal Jargon Translator" Analyzing health data → "Fitness Report Card" Reviewing meeting notes → "Meeting BS Detector" 2. Add personality through AI prompts 2. Add personality through AI prompts Make AI roast them (finance, fitness) Turn it into a game (quiz format) Add competitive elements (grades, scores) Use unexpected analogies (explain tech in food terms) Make AI roast them (finance, fitness) Turn it into a game (quiz format) Add competitive elements (grades, scores) Use unexpected analogies (explain tech in food terms) 3. Keep the UI simple 3. Keep the UI simple One-click file upload Clear results display Optional sharing features Fast loading indicators One-click file upload Clear results display Optional sharing features Fast loading indicators 4. Make it easy to extend 4. Make it easy to extend Modular code structure Clear separation of concerns Well-commented functions Standard design patterns Modular code structure Clear separation of concerns Well-commented functions Standard design patterns The Complete Code Repository The entire Finance Roaster tutorial project is available on GitHub. Here's the structure: FinanceRoaster/ ├── app.py # Flask server (81 lines) ├── services.py # AI + file processing (156 lines) ├── templates/ │ └── home.html # UI (342 lines with CSS/JS) ├── requirements.txt # Dependencies (7 packages) ├── .env.example # Template for your API key └── README.md # Setup instructions FinanceRoaster/ ├── app.py # Flask server (81 lines) ├── services.py # AI + file processing (156 lines) ├── templates/ │ └── home.html # UI (342 lines with CSS/JS) ├── requirements.txt # Dependencies (7 packages) ├── .env.example # Template for your API key └── README.md # Setup instructions To run this tutorial locally: To run this tutorial locally: git clone https://github.com/yourusername/FinanceRoaster cd FinanceRoaster pip install -r requirements.txt # Copy .env.example to .env and add your OpenAI key python app.py git clone https://github.com/yourusername/FinanceRoaster cd FinanceRoaster pip install -r requirements.txt # Copy .env.example to .env and add your OpenAI key python app.py Visit http://localhost:5000 and test it out. http://localhost:5000 Learning cost: ~$0.01 per test (OpenAI API calls) Learning cost: What's included: What's included: Complete working code Detailed comments explaining each function Example bank statements for testing Deployment guide for Heroku/Railway Complete working code Detailed comments explaining each function Example bank statements for testing Deployment guide for Heroku/Railway Potential Use Cases (What You Could Build) Once you understand this template, you can adapt it for various applications: Personal Finance Tools: Personal Finance Tools: Budget analyzer with friendly advice Subscription tracker that flags unused services Shopping habit analyzer (impulse vs planned purchases) Budget analyzer with friendly advice Subscription tracker that flags unused services Shopping habit analyzer (impulse vs planned purchases) Professional Development: Professional Development: Resume reviewer that gives honest feedback Email tone analyzer (passive-aggressive detector) Meeting notes summarizer with action items Resume reviewer that gives honest feedback Email tone analyzer (passive-aggressive detector) Meeting notes summarizer with action items Health & Wellness: Health & Wellness: Food diary analyzer with nutrition roasts Workout consistency tracker with motivational guilt Sleep pattern analyzer with bedtime recommendations Food diary analyzer with nutrition roasts Workout consistency tracker with motivational guilt Sleep pattern analyzer with bedtime recommendations Content Creation: Content Creation: Social media post analyzer (engagement predictor) Blog post readability scorer Video script feedback tool Social media post analyzer (engagement predictor) Blog post readability scorer Video script feedback tool The core pattern: upload file → AI analysis → humorous/useful feedback - works for countless domains. The Technical Deep Dive (For the Nerds) Challenge 1: PDF Parsing Hell Banks format PDFs differently. Chase, Wells Fargo, Bank of America - all unique snowflakes. My solution: Hybrid approach My solution: Hybrid approach def parse_pdf_transactions(pdf_text): # Try regex first (fast, works 70% of the time) pattern = r'(\d{1,2}/\d{1,2}/\d{4})\s+(.+?)\s+([-]?\$?\d+\.?\d*)' matches = re.findall(pattern, pdf_text) if matches: return pd.DataFrame(matches, columns=['Date', 'Desc', 'Amount']) # Fallback to AI (slower, works 95% of the time) return extract_with_gpt(pdf_text) def parse_pdf_transactions(pdf_text): # Try regex first (fast, works 70% of the time) pattern = r'(\d{1,2}/\d{1,2}/\d{4})\s+(.+?)\s+([-]?\$?\d+\.?\d*)' matches = re.findall(pattern, pdf_text) if matches: return pd.DataFrame(matches, columns=['Date', 'Desc', 'Amount']) # Fallback to AI (slower, works 95% of the time) return extract_with_gpt(pdf_text) Lesson: AI should be your fallback, not your first choice. Regex is 100x faster. Lesson: Challenge 2: Keeping Roasts PG-13 Early versions were too savage. One user got: "Your spending screams 'quarter-life crisis.' Seek therapy." The fix: Content filters The fix: Content filters FORBIDDEN_TOPICS = [ 'therapy', 'depression', 'mental health', 'divorce', 'death', 'addiction' ]

def sanitize_roast(roast_text): # Check for sensitive topics if any(topic in roast_text.lower() for topic in FORBIDDEN_TOPICS): return generate_roast(summary_data) # Retry return roast_text FORBIDDEN_TOPICS = [ 'therapy', 'depression', 'mental health', 'divorce', 'death', 'addiction' ]

def sanitize_roast(roast_text): # Check for sensitive topics if any(topic in roast_text.lower() for topic in FORBIDDEN_TOPICS): return generate_roast(summary_data) # Retry return roast_text Temperature tuning also helps: Lowering from 1.0 to 0.9 reduced inappropriate jokes by 60%. Temperature tuning also helps: Challenge 3: Rate Limiting Without Breaking UX OpenAI has rate limits. Hitting them = angry users. Solution: Queue system Solution: Queue system from redis import Redis from rq import Queue

queue = Queue(connection=Redis())

@app.route('/roast', methods=['POST']) def roast(): job = queue.enqueue(process_and_roast, file_data) return jsonify({'job_id': job.id})

@app.route('/status/ ') def status(job_id): job = Job.fetch(job_id, connection=Redis()) if job.is_finished: return jsonify({'status': 'complete', 'roast': job.result}) return jsonify({'status': 'processing'}) from redis import Redis from rq import Queue

queue = Queue(connection=Redis())

@app.route('/roast', methods=['POST']) def roast(): job = queue.enqueue(process_and_roast, file_data) return jsonify({'job_id': job.id})

@app.route('/status/ ') def status(job_id): job = Job.fetch(job_id, connection=Redis()) if job.is_finished: return jsonify({'status': 'complete', 'roast': job.result}) return jsonify({'status': 'processing'}) Frontend polls /status every second. Feels instant, never hits limits. Frontend polls /status every second. Extension Ideas: Taking It Further Once you've built the basic version, here are ways to extend it: 1. Add data persistence 1. Add data persistence # Save anonymized spending patterns from flask_sqlalchemy import SQLAlchemy

db = SQLAlchemy(app)

class SpendingPattern(db.Model): id = db.Column(db.Integer, primary_key=True) total_amount = db.Column(db.Float) category = db.Column(db.String(50)) timestamp = db.Column(db.DateTime) # Save anonymized spending patterns from flask_sqlalchemy import SQLAlchemy

db = SQLAlchemy(app)

class SpendingPattern(db.Model): id = db.Column(db.Integer, primary_key=True) total_amount = db.Column(db.Float) category = db.Column(db.String(50)) timestamp = db.Column(db.DateTime) 2. Implement comparison features 2. Implement comparison features def get_percentile(user_spending, category): all_spending = SpendingPattern.query.filter_by(category=category).all() percentile = sum(1 for x in all_spending if x.total_amount < user_spending) return (percentile / len(all_spending)) * 100 def get_percentile(user_spending, category): all_spending = SpendingPattern.query.filter_by(category=category).all() percentile = sum(1 for x in all_spending if x.total_amount < user_spending) return (percentile / len(all_spending)) * 100 3. Add authentication 3. Add authentication from flask_login import LoginManager, login_required

@app.route('/history') @login_required def view_history(): # Show user's past roasts pass from flask_login import LoginManager, login_required

@app.route('/history') @login_required def view_history(): # Show user's past roasts pass 4. Implement rate limiting 4. Implement rate limiting from flask_limiter import Limiter

limiter = Limiter(app, key_func=lambda: request.remote_addr)

@app.route('/roast', methods=['POST']) @limiter.limit("5 per hour") def roast(): # Prevent API abuse pass from flask_limiter import Limiter

limiter = Limiter(app, key_func=lambda: request.remote_addr)

@app.route('/roast', methods=['POST']) @limiter.limit("5 per hour") def roast(): # Prevent API abuse pass Deployment Options: From Local to Production Once your project works locally, here are deployment options: Option 1: Heroku (Easiest) Option 1: Heroku (Easiest) # Install Heroku CLI, then: heroku create FinanceRoaster git push heroku main heroku config:set OPENAI_API_KEY=your_key # Install Heroku CLI, then: heroku create FinanceRoaster git push heroku main heroku config:set OPENAI_API_KEY=your_key Option 2: Railway Option 2: Railway Connect your GitHub repo Add environment variables Auto-deploys on push Connect your GitHub repo Add environment variables Auto-deploys on push Option 3: DigitalOcean App Platform Option 3: DigitalOcean App Platform Deploy from GitHub $5/month for basic app Built-in SSL certificates Deploy from GitHub $5/month for basic app Built-in SSL certificates Scaling considerations: Scaling considerations: Caching for similar inputs: Store common roast patterns Rate limiting: Prevent abuse with Flask-Limiter CDN for assets: Use Cloudflare free tier Database: PostgreSQL if you add user accounts Caching for similar inputs: Store common roast patterns Caching for similar inputs Rate limiting: Prevent abuse with Flask-Limiter Rate limiting CDN for assets: Use Cloudflare free tier CDN for assets Database: PostgreSQL if you add user accounts Database For a tutorial project, any of these platforms work great. Start with Heroku's free tier to test. The Bigger Picture: What This Tutorial Teaches About AI Development Here's what building Finance Roaster teaches you about creating useful AI applications: Lesson 1: AI doesn't have to be serious. Most AI tutorials focus on optimization, efficiency, and accuracy. But the best projects are ones people actually want to use. Adding personality makes your projects memorable. Lesson 2: Simple prompts can be powerful. The entire "roasting" capability comes from a well-crafted system prompt. You don't need fine-tuning or complex models, just clear instructions and examples. Lesson 3: User experience matters more than complexity. A 100-line Flask app with great UX beats a microservices architecture with boring design. Focus on the user experience first, optimize later. Lesson 4: File processing is a valuable skill. Being able to parse CSVs and PDFs opens up countless project possibilities. Financial statements, receipts, invoices, medical records, they're all structured data waiting to be analyzed. This pattern applies to many domains: This pattern applies to many domains: Upload document → AI analyzes → Useful/entertaining output It's simple, it works, and users understand it immediately Upload document → AI analyzes → Useful/entertaining output It's simple, it works, and users understand it immediately Try It Yourself: Weekend Project Checklist Want to build your own AI app this weekend? Here's your roadmap: Friday night (0.5 hours): Friday night (0.5 hours): Pick your painful task (finance, health, career, dating) Write 10 example roasts manually Define your AI's personality in 3 sentences Pick your painful task (finance, health, career, dating) Write 10 example roasts manually Define your AI's personality in 3 sentences Saturday (1 hour): Saturday (1 hour): Set up Flask + OpenAI Build file upload handling Test your system prompt with real data Iterate on temperature/prompt until roasts are funny Set up Flask + OpenAI Build file upload handling Test your system prompt with real data Iterate on temperature/prompt until roasts are funny Sunday (1.5 hours): Sunday (1.5 hours): Design UI with Tailwind Add one-click sharing Test on 5 friends (get honest feedback) Deploy to your preferred cloud platform Design UI with Tailwind Add one-click sharing Test on 5 friends (get honest feedback) Deploy to your preferred cloud platform Total time: 3 hours\Total cost: $0 (Heroku free tier + OpenAI free credits) Total time: 3 hours\Total cost: $0 (Heroku free tier + OpenAI free credits) The Code (Seriously, It's That Simple) Here's the entire backend in 81 lines: from flask import Flask, request, jsonify, render_template import io, pandas as pd, pdfplumber from openai import OpenAI from dotenv import load_dotenv import os

load_dotenv() app = Flask(__name__) client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))

SYSTEM_PROMPT = """You are a Savage Financial Parent - brutally honest, wickedly funny, armed with transaction history. Roast their spending using SPECIFIC details. Think disappointed parent meets stand-up comedian. Keep under 200 words. End with Financial Maturity Grade (A-F)."""

def process_csv(file): df = pd.read_csv(file) amount_col = next((c for c in df.columns if 'amount' in c.lower()), df.columns[-1]) df[amount_col] = pd.to_numeric( df[amount_col].astype(str).str.replace('$', ''), errors='coerce' ) total = df[amount_col].abs().sum() top5 = df.nlargest(5, amount_col) return { 'total_spent': f"${total:,.2f}", 'top_expenses': top5.to_dict('records') }

def generate_roast(summary): prompt = f""" Total Spent: {summary['total_spent']} Top Expenses: {summary['top_expenses']}

Roast these spending habits with savage humor. """ response = client.chat.completions.create( model="gpt-4o-mini", messages=[ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": prompt} ], temperature=0.9 ) return response.choices[0].message.content

@app.route('/') def home(): return render_template('home.html')

@app.route('/roast', methods=['POST']) def roast(): try: file = request.files['file'] file_bytes = io.BytesIO(file.read()) summary = process_csv(file_bytes) roast_text = generate_roast(summary) return jsonify({ 'success': True, 'roast': roast_text, 'summary': summary }) except Exception as e: return jsonify({'error': str(e)}), 500

if __name__ == '__main__': app.run(debug=True) from flask import Flask, request, jsonify, render_template import io, pandas as pd, pdfplumber from openai import OpenAI from dotenv import load_dotenv import os

load_dotenv() app = Flask(__name__) client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))

SYSTEM_PROMPT = """You are a Savage Financial Parent - brutally honest, wickedly funny, armed with transaction history. Roast their spending using SPECIFIC details. Think disappointed parent meets stand-up comedian. Keep under 200 words. End with Financial Maturity Grade (A-F)."""

def process_csv(file): df = pd.read_csv(file) amount_col = next((c for c in df.columns if 'amount' in c.lower()), df.columns[-1]) df[amount_col] = pd.to_numeric( df[amount_col].astype(str).str.replace('$', ''), errors='coerce' ) total = df[amount_col].abs().sum() top5 = df.nlargest(5, amount_col) return { 'total_spent': f"${total:,.2f}", 'top_expenses': top5.to_dict('records') }

def generate_roast(summary): prompt = f""" Total Spent: {summary['total_spent']} Top Expenses: {summary['top_expenses']}

Roast these spending habits with savage humor. """ response = client.chat.completions.create( model="gpt-4o-mini", messages=[ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": prompt} ], temperature=0.9 ) return response.choices[0].message.content

@app.route('/') def home(): return render_template('home.html')

@app.route('/roast', methods=['POST']) def roast(): try: file = request.files['file'] file_bytes = io.BytesIO(file.read()) summary = process_csv(file_bytes) roast_text = generate_roast(summary) return jsonify({ 'success': True, 'roast': roast_text, 'summary': summary }) except Exception as e: return jsonify({'error': str(e)}), 500

if __name__ == '__main__': app.run(debug=True) That's it. 81 lines. No ML training, no complex infrastructure, no month-long sprints. Final Thoughts: Learn by Building This tutorial project - a simple weekend build with 81 lines of Python - teaches more practical AI skills than any of those complex systems. Why? Because you can actually build it yourself, understand every line, and adapt it to your own ideas. Why? The lesson? The best way to learn AI development is to build projects that are: The lesson? Simple enough to finish (weekend projects, not month-long sprints) Interesting enough to share (add personality, make it fun) Practical enough to extend (real use cases, clear applications) Simple enough to finish (weekend projects, not month-long sprints) Simple enough to finish Interesting enough to share (add personality, make it fun) Interesting enough to share Practical enough to extend (real use cases, clear applications) Practical enough to extend So stop reading tutorials and start building. Pick a file format (PDF, CSV, JSON), add some AI analysis, and ship it this weekend. And when you build something cool, share it! The AI development community loves creative projects. Resources & Next Steps Complete tutorial code: https://github.com/ademicho123/FinanceRoaster (Fork it and customize!) Complete tutorial code: What to do after building this: What to do after building this: Deploy it online (Heroku, Railway, or DigitalOcean) Share with friends for feedback Adapt the pattern for a different domain Add the project to your portfolio Deploy it online (Heroku, Railway, or DigitalOcean) Share with friends for feedback Adapt the pattern for a different domain Add the project to your portfolio P.S. After you build this, try uploading your own bank statement. The AI is surprisingly insightful (and funny). Consider it a feature test and free financial feedback. P.S. Additionally, if you extend this tutorial with cool features, please submit a pull request! The best additions will be merged into the main repo with credit to you.