apps/inference.py

from pandas.io.formats.format import return_docstring
import streamlit as st
import pandas as pd
from transformers import AutoTokenizer, AutoModelForMaskedLM
from transformers import pipeline
import os
import json


models = []
predicted_tokens = []
predicted_sentence = []


@st.cache(show_spinner=False, persist=True)
def load_model(masked_text, model_name):

    from_flax = False
    if model_name == "flax-community/roberta-hindi":
        from_flax = True

    model = AutoModelForMaskedLM.from_pretrained(model_name, from_flax=from_flax)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    nlp = pipeline("fill-mask", model=model, tokenizer=tokenizer)

    result_sentence = nlp(masked_text)

    return result_sentence


def app():
    st.markdown(
        "<h1 style='text-align: center; color: green;'>RoBERTa Hindi</h1>",
        unsafe_allow_html=True,
    )
    st.markdown(
        "This demo uses pretrained RoBERTa variants for Mask Language Modelling (MLM)"
    )

    target_text_path = "./mlm_custom/mlm_targeted_text.csv"
    target_text_df = pd.read_csv(target_text_path)

    texts = target_text_df["text"]

    st.markdown("""## Select any of the following text : """)
    masked_text = st.selectbox("", texts)

    st.write("You selected:", masked_text)

    selected_models = st.multiselect(
        "Choose models",
        [
            "flax-community/roberta-hindi",
            "mrm8488/HindiBERTa",
            "ai4bharat/indic-bert",
            "neuralspace-reverie/indic-transformers-hi-bert",
            "surajp/RoBERTa-hindi-guj-san",
        ],
        ["flax-community/roberta-hindi"],
    )
    if st.button("Fill the Mask!"):
        with st.spinner("Filling the Mask..."):

            for i in range(len(selected_models)):
                filled_sentence = load_model(masked_text, selected_models[i])
                # st.write(filled_sentence)
                models.append(selected_models[i])
                predicted_tokens.append(filled_sentence[0]["token_str"])
                predicted_sentence.append(filled_sentence[0]["sequence"])

            results_df = pd.DataFrame()
            results_df["Model Name"] = models
            results_df["Predicted Word"] = predicted_tokens
            results_df["Sentence"] = predicted_sentence
            st.dataframe(results_df)