app.py

import gradio as gr
from transformers import FlaxAutoModelForSeq2SeqLM, AutoTokenizer
from transformers import AutoModel
import torch
import numpy as np
import random

# Load models (same as before)
bert_model_name = "alexdseo/RecipeBERT"
bert_tokenizer = AutoTokenizer.from_pretrained(bert_model_name)
bert_model = AutoModel.from_pretrained(bert_model_name)
bert_model.eval()

MODEL_NAME_OR_PATH = "flax-community/t5-recipe-generation"
t5_tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH, use_fast=True)
t5_model = FlaxAutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME_OR_PATH)

# ... (all your existing functions remain the same) ...
# get_embedding, average_embedding, get_cosine_similarity, etc.

def generate_recipe_interface(required_ingredients_text, available_ingredients_text, max_ingredients, max_retries):
    """Gradio interface function"""
    try:
        # Parse ingredient inputs
        required_ingredients = [ing.strip() for ing in required_ingredients_text.split(',') if ing.strip()]
        available_ingredients = [ing.strip() for ing in available_ingredients_text.split(',') if ing.strip()]
        
        # Find optimal ingredient combination
        optimized_ingredients = find_best_ingredients(
            required_ingredients,
            available_ingredients,
            max_ingredients
        )
        
        # Generate recipe
        recipe = generate_recipe_with_t5(optimized_ingredients, max_retries)
        
        # Format output
        ingredients_list = '\n'.join([f"• {ing}" for ing in recipe['ingredients']])
        directions_list = '\n'.join([f"{i+1}. {dir}" for i, dir in enumerate(recipe['directions'])])
        used_ingredients = ', '.join(optimized_ingredients)
        
        return (
            recipe['title'],
            ingredients_list,
            directions_list,
            used_ingredients
        )
        
    except Exception as e:
        return f"Error: {str(e)}", "", "", ""

# Create Gradio interface
with gr.Blocks(title="AI Recipe Generator") as demo:
    gr.Markdown("# 🍳 AI Recipe Generator")
    gr.Markdown("Generate recipes using AI with intelligent ingredient combination!")
    
    with gr.Row():
        with gr.Column():
            required_ing = gr.Textbox(
                label="Required Ingredients (comma-separated)",
                placeholder="chicken, rice, onion",
                lines=2
            )
            available_ing = gr.Textbox(
                label="Available Ingredients (comma-separated)",
                placeholder="garlic, tomato, pepper, herbs",
                lines=2
            )
            max_ing = gr.Slider(
                minimum=3,
                maximum=10,
                value=7,
                step=1,
                label="Maximum Ingredients"
            )
            max_retries = gr.Slider(
                minimum=1,
                maximum=10,
                value=5,
                step=1,
                label="Max Retries"
            )
            generate_btn = gr.Button("Generate Recipe", variant="primary")
        
        with gr.Column():
            title_output = gr.Textbox(label="Recipe Title", interactive=False)
            ingredients_output = gr.Textbox(label="Ingredients", lines=8, interactive=False)
            directions_output = gr.Textbox(label="Directions", lines=10, interactive=False)
            used_ingredients_output = gr.Textbox(label="Used Ingredients", interactive=False)
    
    generate_btn.click(
        fn=generate_recipe_interface,
        inputs=[required_ing, available_ing, max_ing, max_retries],
        outputs=[title_output, ingredients_output, directions_output, used_ingredients_output]
    )
    
    # Example
    gr.Examples(
        examples=[
            ["chicken, rice", "onion, garlic, tomato, herbs, pepper", 6, 3],
            ["pasta", "cheese, mushrooms, cream, spinach, garlic", 5, 3],
        ],
        inputs=[required_ing, available_ing, max_ing, max_retries]
    )

if __name__ == "__main__":
    demo.launch()