app.py

from transformers import FlaxAutoModelForSeq2SeqLM, AutoTokenizer
import gradio as gr

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

prefix = "items: "
generation_kwargs = {
    "max_length": 512,
    "min_length": 64,
    "no_repeat_ngram_size": 3,
    "do_sample": True,
    "top_k": 60,
    "top_p": 0.95
}

special_tokens = tokenizer.all_special_tokens
tokens_map = {
    "<sep>": "--",
    "<section>": "\n"
}

def skip_special_tokens(text, special_tokens):
    for token in special_tokens:
        text = text.replace(token, "")
    return text

def target_postprocessing(texts, special_tokens):
    if not isinstance(texts, list):
        texts = [texts]
    new_texts = []
    for text in texts:
        text = skip_special_tokens(text, special_tokens)
        for k, v in tokens_map.items():
            text = text.replace(k, v)
        new_texts.append(text)
    return new_texts

def generation_function(texts):
    _inputs = texts if isinstance(texts, list) else [texts]
    inputs = [prefix + inp for inp in _inputs]
    inputs = tokenizer(
        inputs,
        max_length=256,
        padding="max_length",
        truncation=True,
        return_tensors="jax"
    )
    input_ids = inputs.input_ids
    attention_mask = inputs.attention_mask
    output_ids = model.generate(
        input_ids=input_ids,
        attention_mask=attention_mask,
        **generation_kwargs
    )
    generated = output_ids.sequences
    generated_recipe = target_postprocessing(
        tokenizer.batch_decode(generated, skip_special_tokens=False),
        special_tokens
    )
    return generated_recipe

iface = gr.Interface(
    fn=generation_function,
    inputs="text",
    outputs="text",
    title="Recipe Generation",
    description="Generate a recipe based on an input text."
)

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