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| import os | |
| os.environ['HF_HOME'] = '/tmp' | |
| import time | |
| import streamlit as st | |
| import pandas as pd | |
| import io | |
| import plotly.express as px | |
| import hashlib | |
| from gliner import GLiNER | |
| from streamlit_extras.stylable_container import stylable_container | |
| from comet_ml import Experiment | |
| # A new function to generate a stable color for a given string (label) | |
| def get_stable_color(s): | |
| """ | |
| Generates a consistent, stable color for a given string. | |
| This ensures the same label always has the same color in the treemap. | |
| """ | |
| hash_object = hashlib.sha256(s.encode('utf-8')) | |
| hex_digest = hash_object.hexdigest() | |
| # Use the first 6 hex digits for RGB color | |
| return f'#{hex_digest[:6]}' | |
| # --- Page Configuration and UI Elements | |
| st.set_page_config(layout="wide", page_title="Named Entity Recognition App") | |
| st.subheader("InfoFinder", divider="violet") | |
| st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary") | |
| expander = st.expander("**Important notes**") | |
| expander.write("""**How to Use:** | |
| 1. Type or paste your text into the text area below, then press Ctrl + Enter. | |
| 2. Click the 'Add Question' button to add your question to the Record of Questions. You can manage your questions by deleting them one by one. | |
| 3. Click the 'Extract Answers' button to extract the answer to your question. | |
| Results are presented in an easy-to-read table, visualized in an interactive tree map and are available for download. | |
| **Usage Limits:** You can request results unlimited times for one (1) month. | |
| **Supported Languages:** English | |
| **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: | |
| st.write("Use the following code to embed the InfoFinder web app on your website. Feel free to adjust the width and height values to fit your page.") | |
| code = ''' | |
| <iframe | |
| src="https://aiecosystem-infofinder.hf.space" | |
| frameborder="0" | |
| width="850" | |
| height="450" | |
| ></iframe> | |
| ''' | |
| st.code(code, language="html") | |
| st.text("") | |
| st.text("") | |
| st.divider() | |
| st.subheader("🚀 Ready to build your own AI Web App?", divider="violet") | |
| st.link_button("AI Web App Builder", "https://nlpblogs.com/build-your-named-entity-recognition-app/", type="primary") | |
| # --- Comet ML Setup --- | |
| 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") | |
| comet_initialized = bool(COMET_API_KEY and COMET_WORKSPACE and COMET_PROJECT_NAME) | |
| if not comet_initialized: | |
| st.warning("Comet ML not initialized. Check environment variables.") | |
| # --- Initialize session state for labels | |
| if 'user_labels' not in st.session_state: | |
| st.session_state.user_labels = [] | |
| # --- Model Loading and Caching --- | |
| def load_gliner_model(): | |
| """ | |
| Initializes and caches the GLiNER model. | |
| This ensures the model is only loaded once, improving performance. | |
| """ | |
| try: | |
| return GLiNER.from_pretrained("knowledgator/gliner-multitask-large-v0.5", device="cpu") | |
| except Exception as e: | |
| st.error(f"Error loading the GLiNER model: {e}") | |
| st.stop() | |
| # Load the model | |
| model = load_gliner_model() | |
| word_limit = 200 | |
| user_text = st.text_area(f"Type or paste your text below (max {word_limit} words), and then press Ctrl + Enter", height=250, key='my_text_area') | |
| word_count = len(user_text.split()) | |
| st.markdown(f"**Word count:** {word_count}/{word_limit}") | |
| def clear_text(): | |
| """Clears the text area by resetting its value in session state.""" | |
| st.session_state['my_text_area'] = "" | |
| st.button("Clear text", on_click=clear_text) | |
| st.subheader("Question-Answering", divider = "violet") | |
| # Replaced two columns with a single text input | |
| question_input = st.text_input("Ask wh-questions. **Wh-questions begin with what, when, where, who, whom, which, whose, why and how. We use them to ask for specific information.**") | |
| if st.button("Add Question"): | |
| if question_input: | |
| if question_input not in st.session_state.user_labels: | |
| st.session_state.user_labels.append(question_input) | |
| st.success(f"Added question: {question_input}") | |
| else: | |
| st.warning("This question has already been added.") | |
| else: | |
| st.warning("Please enter a question.") | |
| st.markdown("---") | |
| st.subheader("Record of Questions", divider="violet") | |
| if st.session_state.user_labels: | |
| for i, label in enumerate(st.session_state.user_labels): | |
| col_list, col_delete = st.columns([0.9, 0.1]) | |
| with col_list: | |
| st.write(f"- {label}", key=f"label_{i}") | |
| with col_delete: | |
| if st.button("Delete", key=f"delete_{i}"): | |
| st.session_state.user_labels.pop(i) | |
| st.rerun() | |
| else: | |
| st.info("No questions defined yet. Use the input above to add one.") | |
| st.divider() | |
| if st.button("Extract Answers"): | |
| if not user_text.strip(): | |
| st.warning("Please enter some text to analyze.") | |
| elif word_count > word_limit: | |
| st.warning(f"Your text exceeds the {word_limit} word limit. Please shorten it to continue.") | |
| elif not st.session_state.user_labels: | |
| st.warning("Please define at least one question.") | |
| else: | |
| if comet_initialized: | |
| experiment = Experiment(api_key=COMET_API_KEY, workspace=COMET_WORKSPACE, project_name=COMET_PROJECT_NAME) | |
| experiment.log_parameter("input_text_length", len(user_text)) | |
| experiment.log_parameter("defined_labels", st.session_state.user_labels) | |
| start_time = time.time() | |
| with st.spinner("Analyzing text...", show_time=True): | |
| try: | |
| # Corrected: Changed model_qa to model | |
| entities = model.predict_entities(user_text, st.session_state.user_labels) | |
| end_time = time.time() | |
| elapsed_time = end_time - start_time | |
| st.info(f"Processing took **{elapsed_time:.2f} seconds**.") | |
| if entities: | |
| df1 = pd.DataFrame(entities) | |
| df2 = df1[['label', 'text', 'score']] | |
| df = df2.rename(columns={'label': 'question', 'text': 'answer'}) | |
| st.subheader("Extracted Answers", divider="violet") | |
| st.dataframe(df, use_container_width=True) | |
| st.subheader("Tree map", divider="green") | |
| all_labels = df['question'].unique() | |
| label_color_map = {label: get_stable_color(label) for label in all_labels} | |
| fig_treemap = px.treemap(df, path=[px.Constant("all"), 'question', 'answer'], values='score', color='question', color_discrete_map=label_color_map) | |
| fig_treemap.update_layout(margin=dict(t=50, l=25, r=25, b=25), paper_bgcolor='#F3E5F5', plot_bgcolor='#F3E5F5') | |
| st.plotly_chart(fig_treemap) | |
| csv_data = df.to_csv(index=False).encode('utf-8') | |
| st.download_button( | |
| label="Download CSV", | |
| data=csv_data, | |
| file_name="nlpblogs_questions_answers.csv", | |
| mime="text/csv", | |
| ) | |
| if comet_initialized: | |
| experiment.log_metric("processing_time_seconds", elapsed_time) | |
| experiment.log_table("predicted_entities", df) | |
| experiment.log_figure(figure=fig_treemap, figure_name="entity_treemap") | |
| experiment.end() | |
| else: | |
| st.info("No answers were found in the text with the defined questions.") | |
| if comet_initialized: | |
| experiment.end() | |
| except Exception as e: | |
| st.error(f"An error occurred during processing: {e}") | |
| st.write(f"Error details: {e}") | |
| if comet_initialized: | |
| experiment.log_text(f"Error: {e}") | |
| experiment.end() | |