app.py

from distutils.command.upload import upload
import pandas as pd
import streamlit as st

from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering
from transformers import pipeline


@st.cache
def load_data(file):
    df = pd.read_csv(file, encoding='utf-8', nrows=50)
    return df


def load_pipeline(model_cp, tokenizer_cp):
    return pipeline("question-answering", model=model_cp, tokenizer=tokenizer_cp)


def choose_model():
    with st.sidebar: # streamlit doesn't know how to has this by default?
        st.write("# Model Selection")
        model_cp = st.selectbox('Select model for inference',
                            ('deepset/roberta-base-squad2',
                            'aidan-o-brien/recipe-improver'))
        # If not my model > model_cp = tokenizer_cp, else > albert tokenizer
        if model_cp == "aidan-o-brien/recipe-improver":
            return model_cp, "albert-base-v2"
        else:
            return model_cp, model_cp


# Page config
title = "Recipe Improver"
icon = "🍣"
st.set_page_config(page_title=title, page_icon=icon)
st.title(title)


# Load model and tokenzier
model_cp, tokenizer_cp = choose_model()
question_answer = load_pipeline(model_cp, tokenizer_cp)
st.write("Model and tokenizer successfully loaded.")


# Upload csv - format with expander for aesthetics
with st.expander("Upload csv file"):
    uploaded_file = st.file_uploader("Choose a csv file", type="csv", key='file_uploader')


# If file is uploaded, run inference
if uploaded_file is not None:
    df = load_data(uploaded_file)

    # Run inference on first example
    first_example = df['review'][0]
    question = "how to improve this recipe?"
    answer = question_answer(question=question, context=first_example)

    # Present results
    st.markdown(f"""
    # Results

    The review provided was:

    {first_example}

    The answer given to the question 'how to improve this recipe?' was:

    {answer['answer']}

    """)