Building a Sustainable Living Tips Generator with Next.js, Flask, and Google Gemini AI - Part 1: Backend Development

backend/ ├── app.py # Main application file ├── requirements.txt # Project dependencies ├── .env # Environment variables └── README.md # Backend documentation 

mkdir -p sustainai-web-tips/backend cd sustainai-web-tips/backend python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate 

# Flask - Web framework for building the API flask==2.0.1 # Flask-CORS - Handle Cross-Origin Resource Sharing flask-cors==3.0.10 # Google Generative AI - Interface with Gemini AI google-generativeai==0.3.0 # Python-dotenv - Load environment variables python-dotenv==0.19.0 

pip install -r requirements.txt 

# Google Gemini AI API key # Get your key from: https://makersuite.google.com/app/apikey GOOGLE_API_KEY=your_gemini_api_key_here 

""" SustainAI Tips Backend Server This Flask application serves as the backend for the SustainAI Tips web application. It uses the Google Gemini 1.5 Pro AI model to generate personalized sustainability tips based on user location and habits. Key Components: - Flask server with CORS support - Google Gemini AI integration - Structured prompt engineering - Error handling and validation Author: Antony Ngemu Date: March 2025 """ import os from flask import Flask, request, jsonify from flask_cors import CORS import google.generativeai as genai from dotenv import load_dotenv from typing import List, Dict, Union, Optional # Load environment variables from .env file load_dotenv() # Initialize Flask application app = Flask(__name__) # Configure CORS for frontend integration # This allows our Next.js frontend to make requests to this backend CORS(app, resources={ r"/api/*": { "origins": ["http://localhost:3000", "http://127.0.0.1:3000"], "methods": ["POST", "OPTIONS"], "allow_headers": ["Content-Type"] } }) def initialize_ai_model() -> None: """ Initialize the Google Gemini AI model with API key. Raises: ValueError: If GOOGLE_API_KEY is not found in environment variables Exception: If model initialization fails """ try: api_key = os.getenv('GOOGLE_API_KEY') if not api_key: raise ValueError("GOOGLE_API_KEY not found in environment variables") genai.configure(api_key=api_key) except Exception as e: print(f"Error initializing AI model: {str(e)}") raise def construct_ai_prompt(location: str, habits: str) -> str: """ Construct a detailed prompt for the AI model. Args: location (str): User's location for location-specific tips habits (str): User's current habits for targeted recommendations Returns: str: Formatted prompt string for the AI model """ return f""" Generate practical and personalized sustainability tips for someone in {location} with the following habits: {habits}. Format the response in markdown with the following categories: 1. Quick Wins (Easy to implement immediately) 2. Sustainable Living 3. Transportation & Mobility 4. Community & Social Impact 5. Environmental Protection For each tip: - Make it specific to {location} - Include cost implications or savings - Make it actionable and practical - Consider local resources and infrastructure - Use bullet points with bold headers Start each category with a markdown header (##) and number. Format each tip as a markdown bullet point (*). Use bold (**) for tip headers. """ def generate_ai_tips(location: str, habits: str) -> List[str]: """ Generate personalized sustainability tips using Google Gemini AI. Args: location (str): User's location for location-specific tips habits (str): User's current habits for targeted recommendations Returns: List[str]: List of markdown-formatted tips organized by categories Raises: Exception: If there's an error generating tips from the AI model """ try: # Initialize Gemini 1.5 Pro model  model = genai.GenerativeModel('gemini-1.5-pro') # Generate AI response using the constructed prompt  prompt = construct_ai_prompt(location, habits) response = model.generate_content(prompt) if not response or not response.text: raise ValueError("Empty response from AI model") # Process and clean the response  tips = response.text.strip().split('\n') return [tip.strip() for tip in tips if tip.strip()] except Exception as e: print(f"Error generating tips: {str(e)}") raise def validate_input(data: Dict[str, str]) -> Optional[str]: """ Validate the input data from the request. Args: data (Dict[str, str]): Request data containing location and habits Returns: Optional[str]: Error message if validation fails, None if successful """ if not data: return "Request body is empty" location = data.get('location', '').strip() habits = data.get('habits', '').strip() if not location: return "Location is required" if not habits: return "Habits are required" return None @app.route('/api/tips', methods=['POST']) def get_tips() -> Union[Dict, tuple]: """ API endpoint to generate sustainability tips. Expects POST request with JSON body containing: - location: string - habits: string Returns: Union[Dict, tuple]: JSON response with generated tips or error message """ try: # Extract and validate input data  data = request.get_json() validation_error = validate_input(data) if validation_error: return jsonify({ 'error': validation_error }), 400 # Generate tips using AI  tips = generate_ai_tips( location=data['location'].strip(), habits=data['habits'].strip() ) return jsonify({'tips': tips}) except Exception as e: # Log error and return safe error message  print(f"Error processing request: {str(e)}") return jsonify({ 'error': 'Failed to generate tips. Please try again.' }), 500 if __name__ == '__main__': try: # Initialize AI model before starting the server  initialize_ai_model() # Run Flask development server  app.run(debug=True) except Exception as e: print(f"Failed to start server: {str(e)}") 

python app.py 

curl -X POST http://localhost:5000/api/tips \ -H "Content-Type: application/json" \ -d '{"location":"San Francisco","habits":"driving car, using plastic bags"}'