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import streamlit as st |
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import nest_asyncio |
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import pandas as pd |
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from annotated_text import annotation |
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from scripts.predict import InferenceHandler |
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from htbuilder import span, div |
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nest_asyncio.apply() |
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rc = None |
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@st.cache_data |
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def load_inference_handler(api_token): |
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try: |
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return InferenceHandler(api_token) |
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except: |
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return None |
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@st.cache_data |
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def extract_data(json_obj): |
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row_data = [] |
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row_data.append(json_obj['text_input']) |
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row_data.append(json_obj['text_sentiment']) |
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cat_dict = json_obj['category_sentiments'] |
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for cat in cat_dict.keys(): |
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raw_val = cat_dict[cat] |
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val = f'{raw_val * 100: .2f}%' if raw_val is not None else 'N/A' |
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row_data.append(val) |
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return row_data |
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def load_history(parent_elem): |
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with parent_elem: |
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for idx, result in enumerate(st.session_state.results): |
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text = result['text_input'] |
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discriminatory = False |
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data = [] |
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for sent_item in result['results']: |
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sentence = sent_item['sentence'] |
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bin_class = sent_item['binary_classification']['classification'] |
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pred_class = sent_item['binary_classification']['prediction_class'] |
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ml_regr = sent_item['multilabel_regression'] |
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row_data = [sentence, bin_class] |
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if pred_class == 1: |
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discriminatory = True |
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for cat in ml_regr.keys(): |
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perc = ml_regr[cat] * 100 |
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row_data.append(f'{perc:.2f}%') |
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else: |
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for i in range(6): |
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row_data.append(None) |
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data.append(row_data) |
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df = pd.DataFrame(data=data, columns=['Sentence', 'Binary Classification', 'Gender', 'Race', 'Sexuality', 'Disability', 'Religion', 'Unspecified']) |
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with st.expander(label=f'Entry #{idx+1}', icon='🔴' if discriminatory else '🟢'): |
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st.markdown('<hr style="margin: 0.5em 0 0 0;">', unsafe_allow_html=True) |
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st.markdown( |
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f"<p style='text-align: center; font-weight: bold; font-style: italic; font-size: medium;'>\"{text}\"</p>", |
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unsafe_allow_html=True |
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) |
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st.markdown('<hr style="margin: 0 0 0.5em 0;">', unsafe_allow_html=True) |
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st.markdown('##### Sentence Breakdown:') |
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st.dataframe(df) |
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def build_result_tree(parent_elem, results): |
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label_dict = { |
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'Gender': '#4A90E2', |
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'Race': '#E67E22', |
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'Sexuality': '#3B9C5A', |
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'Disability': '#8B5E3C', |
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'Religion': '#A347BA', |
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'Unspecified': '#A0A0A0' |
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} |
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discriminatory_sentiment = False |
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sent_details = [] |
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for result in results['results']: |
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sentence = result['sentence'] |
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bin_class = result['binary_classification']['classification'] |
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pred_class = result['binary_classification']['prediction_class'] |
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ml_regr = result['multilabel_regression'] |
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sent_res = { |
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'sentence': sentence, |
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'classification': f':red[{bin_class}]' if pred_class else f':green[{bin_class}]', |
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'annotated_categories': [] |
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} |
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if pred_class == 1: |
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discriminatory_sentiment = True |
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at_list = [] |
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for entry in ml_regr.keys(): |
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val = ml_regr[entry] |
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if val > 0.0: |
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perc = val * 100 |
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at_list.append(annotation(body=entry, label=f'{perc:.2f}%', background=label_dict[entry])) |
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sent_res['annotated_categories'] = at_list |
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sent_details.append(sent_res) |
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with parent_elem: |
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result_hdr = ':red[Detected Discriminatory Sentiment]' if discriminatory_sentiment else ':green[No Discriminatory Sentiment Detected]' |
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st.markdown(f'### Results - {result_hdr}') |
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with st.container(border=True): |
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st.markdown('<hr style="margin: 0.5em 0 0 0;">', unsafe_allow_html=True) |
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st.markdown( |
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f"<p style='text-align: center; font-weight: bold; font-style: italic; font-size: large;'>\"{results['text_input']}\"</p>", |
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unsafe_allow_html=True |
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) |
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st.markdown('<hr style="margin: 0 0 0.5em 0;">', unsafe_allow_html=True) |
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if discriminatory_sentiment: |
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if (len(results['results']) > 1): |
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st.markdown('##### Sentence Breakdown:') |
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for idx, sent in enumerate(sent_details): |
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with st.expander(label=f'Sentence #{idx+1}', icon='🔴' if len(sent['annotated_categories']) > 0 else '🟢', expanded=True): |
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st.markdown('<hr style="margin: 0.5em 0 0 0;">', unsafe_allow_html=True) |
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st.markdown( |
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f"<p style='text-align: center; font-weight: bold; font-style: italic; font-size: large;'>\"{sent['sentence']}\"</p>", |
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unsafe_allow_html=True |
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) |
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st.markdown('<hr style="margin: 0 0 0.5em 0;">', unsafe_allow_html=True) |
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classification = sent['classification'] |
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st.markdown(f'##### Classification - {classification}') |
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if len(sent['annotated_categories']) > 0: |
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st.markdown( |
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div( |
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span(' ' if idx != 0 else '')[ |
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item |
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] for idx, item in enumerate(sent['annotated_categories']) |
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), |
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unsafe_allow_html=True |
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) |
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st.markdown('\n') |
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else: |
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st.markdown(f"#### Classification - {sent['classification']}") |
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if len(sent['annotated_categories']) > 0: |
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st.markdown( |
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div( |
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span(' ' if idx != 0 else '')[ |
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item |
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] for idx, item in enumerate(sent['annotated_categories']) |
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), |
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unsafe_allow_html=True |
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) |
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st.markdown('\n') |
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@st.cache_data |
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def analyze_text(text): |
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if ih: |
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res = None |
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with rc: |
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with st.spinner("Processing...", show_time=True) as spnr: |
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res = ih.classify_text(text) |
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del spnr |
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if res is not None: |
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st.session_state.results.append(res) |
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build_result_tree(rc, res) |
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st.title('NLPinitiative Text Classifier') |
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st.sidebar.write("") |
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API_KEY = st.sidebar.text_input( |
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"Enter your HuggingFace API Token", |
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help="You can get your free API token in your settings page: https://huggingface.co/settings/tokens", |
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type="password", |
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) |
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ih = load_inference_handler(API_KEY) |
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tab1 = st.empty() |
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tab2 = st.empty() |
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tab3 = st.empty() |
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tab1, tab2, tab3 = st.tabs(['Classifier', 'Input History', 'About This App']) |
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if "results" not in st.session_state: |
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st.session_state.results = [] |
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with tab1: |
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"Text Classifier for determining if entered text is discriminatory (and the categories of discrimination) or Non-Discriminatory." |
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rc = st.container() |
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text_form = st.form(key='classifier', clear_on_submit=True, enter_to_submit=True) |
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with text_form: |
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entry = None |
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text_area = st.text_area('Enter text to classify', value='', disabled=True if ih is None else False) |
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form_btn = st.form_submit_button('submit', disabled=True if ih is None else False) |
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if form_btn and text_area is not None and len(text_area) > 0: |
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analyze_text(text_area) |
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with tab2: |
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load_history(tab2) |
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with tab3: |
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st.markdown( |
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"""The NLPinitiative Discriminatory Text Classifier is an advanced |
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natural language processing tool designed to detect and flag potentially |
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discriminatory or harmful language. By analyzing text for biased, offensive, |
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or exclusionary content, this classifier helps promote more inclusive and |
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respectful communication. Simply enter your text below, and the model will |
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assess it based on linguistic patterns and context. While the tool provides |
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valuable insights, we encourage users to review flagged content thoughtfully |
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and consider context when interpreting results.""" |
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) |
