Source: aoai-app-recipe.py · GitHub · microsoft/generative-ai-for-beginners Authors: Microsoft (microsoft) Licence: MIT — https://spdx.org/licenses/MIT.html
from openai import OpenAI
import os
import re
from dotenv import load_dotenv
# load environment variables from .env file
load_dotenv()
# SECURITY: Validate environment variables with helpful error messages
def get_required_env(var_name: str) -> str:
"""Get a required environment variable or raise an error with helpful message."""
value = os.getenv(var_name)
if not value:
raise ValueError(f"Missing required environment variable: {var_name}. Please set it in your .env file.")
return value
# SECURITY: Input validation functions
def validate_number_input(value: str, min_val: int = 1, max_val: int = 20) -> int:
"""Validate and sanitize numeric input."""
try:
num = int(value)
if num < min_val or num > max_val:
raise ValueError(f"Number must be between {min_val} and {max_val}")
return num
except ValueError:
raise ValueError(f"Please enter a valid number between {min_val} and {max_val}")
def validate_text_input(value: str, max_length: int = 500) -> str:
"""Validate and sanitize text input to prevent prompt injection."""
if len(value) > max_length:
raise ValueError(f"Input too long. Maximum {max_length} characters allowed.")
# Remove potentially dangerous characters/patterns
sanitized = re.sub(r'[<>{}[\]|\\`]', '', value)
# Limit to alphanumeric, spaces, commas, and basic punctuation
if not re.match(r'^[\w\s,.\'-]+$', sanitized, re.UNICODE):
raise ValueError("Input contains invalid characters")
return sanitized.strip()
# configure the OpenAI client against the Azure OpenAI (Microsoft Foundry) v1 endpoint
client = OpenAI(
api_key=get_required_env('AZURE_OPENAI_API_KEY'),
base_url=f"{get_required_env('AZURE_OPENAI_ENDPOINT').rstrip('/')}/openai/v1/",
)
deployment = get_required_env('AZURE_OPENAI_DEPLOYMENT')
# SECURITY: Validate all user inputs
try:
no_recipes_input = input("No of recipes (for example, 5): ")
no_recipes = validate_number_input(no_recipes_input, 1, 20)
ingredients_input = input("List of ingredients (for example, chicken, potatoes, and carrots): ")
ingredients = validate_text_input(ingredients_input, 500)
filter_input = input("Filter (for example, vegetarian, vegan, or gluten-free): ")
filter_value = validate_text_input(filter_input, 100) if filter_input.strip() else "none"
except ValueError as e:
print(f"Input validation error: {e}")
exit(1)
# interpolate the number of recipes into the prompt and ingredients
# Note: Using validated and sanitized inputs
prompt = f"Show me {no_recipes} recipes for a dish with the following ingredients: {ingredients}. Per recipe, list all the ingredients used, no {filter_value}: "
response = client.responses.create(model=deployment, input=prompt, max_output_tokens=600, store=False)
# print response
print("Recipes:")
old_prompt_result = response.output_text
if not old_prompt_result:
print("No response received.")
else:
print(old_prompt_result)
prompt_shopping = "Produce a shopping list, and please don't include ingredients that I already have at home: "
new_prompt = f"Given ingredients at home {ingredients} and these generated recipes: {old_prompt_result}, {prompt_shopping}"
response = client.responses.create(model=deployment, input=new_prompt, max_output_tokens=600, store=False)
# print response
print("\n=====Shopping list ======= \n")
if response.output_text:
print(response.output_text)
else:
print("No response received.")