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import streamlit as st |
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import pandas as pd |
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from annotated_text import annotated_text |
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import time |
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from scripts.predict import InferenceHandler |
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history_df = pd.DataFrame(data=[], columns=['Text', 'Classification', 'Gender', 'Race', 'Sexuality', 'Disability', 'Religion', 'Unspecified']) |
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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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def extract_data(json_obj): |
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row_data = [] |
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row_data.append(json_obj['raw_text']) |
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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(): |
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for result in st.session_state.results: |
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history_df.loc[len(history_df)] = extract_data(result) |
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def output_results(res): |
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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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with rc: |
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st.markdown('### Results') |
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with st.container(border=True): |
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at_list = [] |
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if res['numerical_sentiment'] == 1: |
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for entry in res['category_sentiments'].keys(): |
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val = res['category_sentiments'][entry] |
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if val > 0.0: |
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perc = val * 100 |
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at_list.append((entry, f'{perc:.2f}%', label_dict[entry])) |
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st.markdown(f"#### Text - *\"{res['raw_text']}\"*") |
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st.markdown(f"#### Classification - {':red' if res['numerical_sentiment'] == 1 else ':green'}[{res['text_sentiment']}]") |
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if len(at_list) > 0: |
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annotated_text(at_list) |
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@st.cache_data |
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def analyze_text(text): |
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st.write(f'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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time.sleep(5) |
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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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history_df.loc[-1] = extract_data(res) |
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output_results(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, tab2 = st.tabs(['Classifier', '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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load_history() |
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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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hist_container = st.container() |
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hist_expander = hist_container.expander('History') |
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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 hist_expander: |
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st.dataframe(history_df, hide_index=True) |
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with tab2: |
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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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) |
