Spaces:
Running
Running
File size: 1,412 Bytes
755e467 1c82651 755e467 b4fd20a 755e467 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 |
import gradio as gr
from transformers import pipeline
# Load a model from Hugging Face for recipe generation
# Use a pipeline as a high-level helper
from transformers import pipeline
model = pipeline("text2text-generation", model="flax-community/t5-recipe-generation")
# Recipe generation function
def suggest_recipes(ingredients):
prompt = f" You are expert in cooking. Please suggest 3 recipes using the following ingredients: {ingredients}. Give the title to each recipe. Include preparation time for each recipe at the beginning."
response = model(prompt)
# Parse model output into a readable format
recipes = []
for i, recipe in enumerate(response):
recipes.append(f"Recipe {i+1}: {recipe['generated_text']}")
return "\n\n".join(recipes)
# Gradio interface
with gr.Blocks() as app:
gr.Markdown("# Recipe Suggestion App")
gr.Markdown("Provide the ingredients you have, and this app will suggest recipes along with preparation times!")
with gr.Row():
ingredients_input = gr.Textbox(label="Enter Ingredients (comma-separated):", placeholder="e.g., eggs, milk, flour")
recipe_output = gr.Textbox(label="Suggested Recipes:", lines=10, interactive=False)
generate_button = gr.Button("Get Recipes")
generate_button.click(suggest_recipes, inputs=ingredients_input, outputs=recipe_output)
# Launch the app
app.launch()
|