app.py

import gradio as gr
from transformers import pipeline
import fastapi
from gradio import fastapi as gr_api

generator = pipeline("text2text-generation", model="LahiruProjects/recipe-generator-flan-t5")

def generate_recipe(name, ingredients, calories, time):
    prompt = f"""Create a step-by-step recipe for "{name}" using these ingredients: {', '.join(ingredients.split(','))}.
    Keep it under {calories} calories and make sure it's ready in less than {time} minutes."""
    result = generator(prompt)
    return result[0]["generated_text"]

# Using gr.Blocks() for a custom interface
with gr.Blocks() as demo:
    with gr.Row():
        name = gr.Textbox(label="Recipe Name")
        ingredients = gr.Textbox(label="Ingredients (comma-separated)")
        calories = gr.Number(label="Max Calories", value=400)
        time = gr.Number(label="Max Cooking Time (minutes)", value=30)
    output = gr.Textbox()
    demo.add(name, ingredients, calories, time, output)

# Set up FastAPI app to expose the REST API
app = fastapi.FastAPI()
app = gr_api.FastAPI(app, demo)

gr_api.add_api_route(app, "/api/predict", demo, methods=["POST"])