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"""
Main App
"""
import streamlit as st
from transformers import AutoModelForSeq2SeqLM
from src.tokenizers import IndoNLGTokenizer
@st.cache(allow_output_mutation=True)
def fetch_tokenizer_model():
"""
Fetch tokenizer and model
"""
tokenizer = IndoNLGTokenizer.from_pretrained("indobenchmark/indobart-v2")
model = AutoModelForSeq2SeqLM.from_pretrained("haryoaw/id-recigen-bart")
return tokenizer, model
tokenizer, model = fetch_tokenizer_model()
def predict_recipe(food: str) -> str:
"""
Predict Ingredients Here!
Parameters
----------
food: str
The food that will be used
Returns
-------
str
Return the model here
"""
inp = tokenizer(food.lower(), return_tensors="pt")["input_ids"]
generated = model.generate(
inp, max_length=500, do_sample=False, num_beams=10, num_beam_groups=2
)
returned_input: str = tokenizer.decode(generated[0], skip_special_tokens=True)
returned_input = "\n".join([x.strip() for x in returned_input.split("||")])
return returned_input
def create_frontend() -> None:
"""
Create front end streamlit here
"""
st.markdown("# Food Ingredients Generator Indonesia Showcase!")
st.write("🥑Generate your ingredients here!")
with st.form("my_form"):
food_name = st.text_input(
"Food", value="Nasi Goreng Ayam", help="Input your food here!"
)
submitted = st.form_submit_button("Submit")
if submitted:
predicted = predict_recipe(food_name)
st.markdown(f"## Bahan ( Ingredients ) `{food_name}`:")
st.text(predicted)
st.markdown("## Additional Note")
st.write(
"❗Please note that the model is trained with the food that use:"
)
for i, ingr in enumerate(("ayam", "tempe", "ikan", "kambing", "telur", "tahu", "sapi")):
st.write(f"{i+1}. {ingr}")
st.markdown("## Models")
st.markdown(
"🤗 Huggingface Model: [Link](https://huggingface.co/haryoaw/id-recigen-bart)"
)
st.write("Thank you 😊")
if __name__ == "__main__":
create_frontend()
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