import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load the model and tokenizer
@st.cache_resource
def load_model():
    tokenizer = AutoTokenizer.from_pretrained("Izza-shahzad-13/recipe-generator")
    model = AutoModelForSeq2SeqLM.from_pretrained("Izza-shahzad-13/recipe-generator")
    return tokenizer, model

tokenizer, model = load_model()

# Streamlit App
st.title("🍳 Recipe Generator App")
st.markdown("Generate delicious recipes by entering the ingredients you have!")

# Input ingredients
ingredients = st.text_area(
    "Enter ingredients (comma-separated):",
    placeholder="e.g., chicken, onion, garlic, tomatoes",
)

# Generate recipe button
if st.button("Generate Recipe"):
    if ingredients:
        with st.spinner("Generating your recipe... 🍲"):
            # Prepare input for the model
            inputs = tokenizer(f"Ingredients: {ingredients}", return_tensors="pt")

            # Generate recipe
            outputs = model.generate(inputs["input_ids"], max_length=150, num_beams=5, early_stopping=True)
            recipe = tokenizer.decode(outputs[0], skip_special_tokens=True)

            # Display the recipe
            st.success("Here's your recipe:")
            st.write(recipe)
    else:
        st.warning("Please enter some ingredients!")

# Footer
st.markdown("---")
st.markdown("Built with ❤️ using Streamlit and Hugging Face 🤗")