import streamlit as st import time import pandas as pd import io from transformers import pipeline from streamlit_extras.stylable_container import stylable_container import plotly.express as px import zipfile import os from comet_ml import Experiment # Comet ML is imported, but not used in the exact same way for caching st.set_page_config(layout="wide", page_title="Named Entity Recognition App") st.subheader("7-Persian Named Entity Recognition Web App", divider="red") st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary") expander = st.expander("**Important notes on the 7-Persian Named Entity Recognition Web App**") expander.write(''' **Named Entities:** This 7-Persian Named Entity Recognition Web App predicts seven (7) labels (“person”, “location”, “money”, “organization”, “date”, “percent value”, “time”). Results are presented in an easy-to-read table, visualized in an interactive tree map, pie chart, and bar chart, and are available for download along with a Glossary of tags. Please check and adjust the language settings in your computer, so the Persian characters are handled properly in your downloaded file. **How to Use:** Type or paste your text and press Ctrl + Enter. Then, click the 'Results' button to extract and tag entities in your text data. **Usage Limits:** Unlimited number of Result requests. **Customization:** To change the app's background color to white or black, click the three-dot menu on the right-hand side of your app, go to Settings and then Choose app theme, colors and fonts. **Technical issues:** If your connection times out, please refresh the page or reopen the app's URL. For any errors or inquiries, please contact us at info@nlpblogs.com ''') with st.sidebar: container = st.container(border=True) container.write("**Named Entity Recognition (NER)** is the task of extracting and tagging entities in text data. Entities can be persons, organizations, locations, countries, products, events etc.") st.subheader("Related NLP Web Apps", divider="red") st.link_button("14-Named Entity Recognition Web App", "https://nlpblogs.com/shop/named-entity-recognition-ner/14-named-entity-recognition-web-app/", type="primary") COMET_API_KEY = os.environ.get("COMET_API_KEY") COMET_WORKSPACE = os.environ.get("COMET_WORKSPACE") COMET_PROJECT_NAME = os.environ.get("COMET_PROJECT_NAME") if COMET_API_KEY and COMET_WORKSPACE and COMET_PROJECT_NAME: comet_initialized = True else: comet_initialized = False st.warning("Comet ML not initialized. Check environment variables.") # --- Caching the model with st.cache_resource --- @st.cache_resource def load_ner_model(): return pipeline("token-classification", model="HooshvareLab/bert-fa-base-uncased-ner-peyma", aggregation_strategy="max") # Load the model using the cached function model = load_ner_model() # --- End Caching --- text = st.text_area("Type or paste your text below, and then press Ctrl + Enter", key='my_text_area') st.write("**Input text**: ", text) def clear_text(): st.session_state['my_text_area'] = "" st.button("Clear text", on_click=clear_text) st.divider() if st.button("Results"): if not text.strip(): # Add a check for empty input st.warning("Please enter some text to process.") else: with st.spinner("Wait for it...", show_time=True): # No need for time.sleep(5) here unless it's for artificial delay # The model is already loaded thanks to st.cache_resource text1 = model(text) df1 = pd.DataFrame(text1) pattern = r'[^\w\s]' df1['word'] = df1['word'].replace(pattern, '', regex=True) df2 = df1.replace('', 'Unknown') df = df2.dropna() # Initialize Comet ML experiment here, as it's per-run if comet_initialized: experiment = Experiment( api_key=COMET_API_KEY, workspace=COMET_WORKSPACE, project_name=COMET_PROJECT_NAME, ) experiment.log_parameter("input_text", text) experiment.log_table("predicted_entities", df) properties = {"border": "2px solid gray", "color": "blue", "font-size": "16px"} df_styled = df.style.set_properties(**properties) st.dataframe(df_styled) with st.expander("See Glossary of tags"): st.write(''' '**word**': ['entity extracted from your text data'] '**score**': ['accuracy score; how accurately a tag has been assigned to a given entity'] '**entity_group**': ['label (tag) assigned to a given extracted entity'] '**start**': ['index of the start of the corresponding entity'] '**end**': ['index of the end of the corresponding entity'] **What does B and I mean in front of each entity_group?** Supposing that there are two words (word A, word B). **B** indicates that word A is the beginning of an entity_group and **I** indicates that word B is inside that entity_group. For example, **Los** is the beginning of the entity_group **Location** and **Angeles** is inside the entity_group **Location**. Los (B-LOC) - Beginning of the entity_group **Location** Angeles (I-LOC) - Inside the entity_group **Location** ''') if df is not None and not df.empty: # Added check for empty DataFrame fig = px.treemap(df, path=[px.Constant("all"), 'word', 'entity_group'], values='score', color='entity_group') fig.update_layout(margin=dict(t=50, l=25, r=25, b=25)) st.subheader("Tree map", divider="red") st.plotly_chart(fig) if comet_initialized: experiment.log_figure(figure=fig, figure_name="entity_treemap") if df is not None and not df.empty: # Added check for empty DataFrame value_counts1 = df['entity_group'].value_counts() df1 = pd.DataFrame(value_counts1) final_df = df1.reset_index().rename(columns={"index": "entity_group"}) col1, col2 = st.columns(2) with col1: fig1 = px.pie(final_df, values='count', names='entity_group', hover_data=['count'], labels={'count': 'count'}, title='Percentage of predicted labels') fig1.update_traces(textposition='inside', textinfo='percent+label') st.subheader("Pie Chart", divider="red") st.plotly_chart(fig1) if comet_initialized: experiment.log_figure(figure=fig1, figure_name="label_pie_chart") with col2: fig2 = px.bar(final_df, x="count", y="entity_group", color="entity_group", text_auto=True, title='Occurrences of predicted labels') st.subheader("Bar Chart", divider="red") st.plotly_chart(fig2) if comet_initialized: experiment.log_figure(figure=fig2, figure_name="label_bar_chart") dfa = pd.DataFrame( data={ 'word': ['entity extracted from your text data'], 'score': ['accuracy score; how accurately a tag has been assigned to a given entity'], 'entity_group': ['label (tag) assigned to a given extracted entity'], 'start': ['index of the start of the corresponding entity'], 'end': ['index of the end of the corresponding entity'], }) buf = io.BytesIO() with zipfile.ZipFile(buf, "w") as myzip: myzip.writestr("Summary of the results.csv", df.to_csv(index=False)) myzip.writestr("Glossary of tags.csv", dfa.to_csv(index=False)) with stylable_container( key="download_button", css_styles="""button { background-color: yellow; border: 1px solid black; padding: 5px; color: black; }""", ): st.download_button( label="Download zip file", data=buf.getvalue(), file_name="zip file.zip", mime="application/zip", ) if comet_initialized: experiment.log_asset(buf.getvalue(), file_name="downloadable_results.zip") st.divider() if comet_initialized: experiment.end()