text classification app

app.py

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+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