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import streamlit as st
from transformers import T5Tokenizer, T5ForConditionalGeneration
MODEL_NAME_OR_PATH = "flax-community/t5-recipe-generation"
tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME_OR_PATH, use_fast=True)
model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME_OR_PATH)
def generate_recipe(input_items):
prefix = "items: "
input_text = prefix + input_items
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output_ids = model.generate(input_ids)
generated_recipe = tokenizer.decode(output_ids[0], skip_special_tokens=True)
return generated_recipe
def main():
st.title("Recipe Generation")
input_items = st.text_area("Enter the recipe instructions:")
if st.button("Generate Recipe"):
generated_recipe = generate_recipe(input_items)
st.subheader("Generated Recipe:")
st.text(generated_recipe)
if __name__ == "__main__":
main()
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