app.py

import streamlit as st
from transformers import pipeline
import pandas as pd
import nannyml as nml


if 'count' not in st.session_state:
	st.session_state.count = 0
      
if 'dissable' not in st.session_state:
	st.session_state.dissable = False

def increment_counter():
	st.session_state.count += 1

@st.cache_resource
def get_model(url):
    tokenizer_kwargs = {'padding':True, 'truncation':True, 'max_length':512}
    return pipeline(model=url, **tokenizer_kwargs)

rating_classification_model = get_model("NannyML/amazon-reviews-sentiment-bert-base-uncased-6000-samples")


label_mapping = {
    'LABEL_0': 'Negative',
    'LABEL_1': 'Neutral',
    'LABEL_2': 'Positive'
}

review = st.text_input(label='write a review', value='I love this book!')
single_review_button = st.button(label='Classify Single Review')
if review and single_review_button:
    rating = rating_classification_model(review)[0]
    label = label_mapping[rating['label']]
    score = rating['score']
    st.write(f"{label} — confidence: {round(score, 2)}")


# # # # # # # #

reference_df = pd.read_csv('../reference.csv')
analysis_df = pd.read_csv('../analysis.csv')

reference_df['label'] = reference_df['label'].astype(str)
reference_df['pred_label'] = reference_df['pred_label'].astype(str)

analysis_df['label'] = analysis_df['label'].astype(str)
analysis_df['pred_label'] = analysis_df['pred_label'].astype(str)


estimator = nml.CBPE(
    y_pred_proba={
        '0': 'pred_proba_label_negative',
        '1': 'pred_proba_label_neutral',
        '2': 'pred_proba_label_positive'},
    y_pred='pred_label',
    y_true='label',
    problem_type='classification_multiclass',
    metrics='f1',
    chunk_size=400,
)
estimator.fit(reference_df)

calculator = nml.PerformanceCalculator(
    y_pred_proba={
        '0': 'pred_proba_label_negative',
        '1': 'pred_proba_label_neutral',
        '2': 'pred_proba_label_positive'},
    y_true='label',
    y_pred='pred_label',
    problem_type='classification_multiclass',
    metrics=['f1'],
    chunk_size=400,
)
calculator.fit(reference_df)

multiple_reviews_button = st.button('Estimate Model Performance on 400 Reviews', on_click=increment_counter, disabled=st.session_state.dissable)


if multiple_reviews_button:
    prod_data = analysis_df[0: st.session_state.count *  400]
    results = estimator.estimate(prod_data.drop(columns=['label']))
    realize_results = calculator.calculate(prod_data)
    fig = results.compare(realize_results).plot()
    st.plotly_chart(fig, use_container_width=True, theme=None)

    st.write(f'Batch {st.session_state.count} / 5')

if st.session_state.count >= 5:
      st.session_state.count = 0