RecipeGenerator / app.py
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import gradio as gr
from transformers import pipeline
# Load the recipe generation model
generator = pipeline("text2text-generation", model="flax-community/t5-recipe-generation")
def generate_recipe(ingredients):
"""
Generate a recipe based on the provided ingredients
"""
# Check if ingredients are provided
if not ingredients.strip():
return "Please enter some ingredients!"
try:
# Format the prompt for the model
# The model expects format: "items: ingredient1, ingredient2, ingredient3"
prompt = f"items: {ingredients}"
# Generate the recipe
result = generator(prompt, max_length=512, do_sample=True)
# Extract the generated recipe
recipe = result[0]['generated_text']
# Format the output nicely
output = "#Generated Recipe\n\n"
output += "---\n\n"
output += recipe
output += "\n\n---\n\n"
output += "**Tip**: Try different ingredient combinations for variety!"
return output
except Exception as e:
return f"Error generating recipe: {str(e)}\n\nPlease try again with different ingredients."
# Example ingredient combinations for users to try
examples = [
["chicken, rice, garlic, tomatoes, onion"],
["pasta, cream, parmesan cheese, mushrooms, spinach"],
["eggs, milk, flour, sugar, vanilla extract"],
["salmon, lemon, dill, potatoes, butter"],
["beef, beans, chili powder, onion, tomatoes"],
["tofu, soy sauce, ginger, broccoli, sesame oil"]
]
# Create the Gradio interface
demo = gr.Interface(
fn=generate_recipe,
inputs=gr.Textbox(
label="Enter Your Ingredients",
placeholder="chicken, rice, garlic, tomatoes, onion",
lines=3,
info="Enter ingredients separated by commas"
),
outputs=gr.Markdown(label="Your Recipe"),
title="AI Recipe Generator",
description="Enter the ingredients you have, and I'll generate a delicious recipe for you!",
examples=examples,
theme=gr.themes.Soft(),
article="""
### How to use:
1. Enter your available ingredients separated by commas
2. Click 'Submit' or press Enter
3. Get a complete recipe with instructions!
**Powered by T5 Recipe Generation Model from Hugging Face**
"""
)
if __name__ == "__main__":
demo.launch()