Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,18 +1,24 @@
 import os
-os.environ['HF_HOME'] = '/tmp'
 import time
+import hashlib
 import streamlit as st
 import pandas as pd
 import io
 import plotly.express as px
 import zipfile
-import json
-from cryptography.fernet import Fernet
 from streamlit_extras.stylable_container import stylable_container
 from typing import Optional
 from gliner import GLiNER
 from comet_ml import Experiment
 
+# Set HF_HOME environment variable
+os.environ['HF_HOME'] = '/tmp'
+
+# --- Page Configuration and UI Elements ---
+st.set_page_config(layout="wide", page_title="Named Entity Recognition App")
+st.subheader("HR.ai", divider="green")
+st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
+
 st.markdown(
     """
     <style>
@@ -26,7 +32,6 @@ st.markdown(
         background-color: #B2F2B2; /* A pale green for the sidebar */
         secondary-background-color: #B2F2B2;
     }
-    
     /* Expander background color */
     .streamlit-expanderContent {
         background-color: #F5FFFA;
@@ -60,34 +65,17 @@ st.markdown(
     unsafe_allow_html=True
 )
 
-# --- Page Configuration and UI Elements ---
-st.set_page_config(layout="wide", page_title="Named Entity Recognition App")
-st.subheader("HR.ai", divider="green")
-st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
 expander = st.expander("**Important notes**")
-expander.write("""**Named Entities:** This HR.ai web app predicts thirty-six (36) labels: "Email", "Phone_number", "Street_address", "City", "Country", "Date_of_birth", "Marital_status", "Person", "Full_time", "Part_time", "Contract", "Terminated", "Retired", "Job_title", "Date", "Organization", "Role", "Performance_score", "Leave_of_absence", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance", "Pay_rate", "Annual_salary", "Tax", "Deductions", "Interview_type", "Applicant", "Referral", "Job_board", "Recruiter", "Offer_letter", "Agreement", "Certification", "Skill"
-
-Results are presented in easy-to-read tables, visualized in an interactive tree map, pie chart and bar chart, and are available for download along with a Glossary of tags.      
-
-**How to Use:** Type or paste your text into the text area below, then press Ctrl + Enter. Click the 'Results' button to extract and tag entities in your text data.      
-
-**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""")
+expander.write("""**Named Entities:** This HR.ai web app predicts thirty-six (36) labels: "Email", "Phone_number", "Street_address", "City", "Country", "Date_of_birth", "Marital_status", "Person", "Full_time", "Part_time", "Contract", "Terminated", "Retired", "Job_title", "Date", "Organization", "Role", "Performance_score", "Leave_of_absence", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance", "Pay_rate", "Annual_salary", "Tax", "Deductions", "Interview_type", "Applicant", "Referral", "Job_board", "Recruiter", "Offer_letter", "Agreement", "Certification", "Skill"Results are presented in easy-to-read tables, visualized in an interactive tree map, pie chart and bar chart, and are available for download along with a Glossary of tags.
+      **How to Use:** Type or paste your text into the text area below, then press Ctrl + Enter. Click the 'Results' button to extract and tag entities in your text data.
+      **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 HR.ai web app on your website. Feel free to adjust the width and height values to fit your page.")
     code = '''
-    <iframe
-	src="https://aiecosystem-hr-ai.hf.space"
-	frameborder="0"
-	width="850"
-	height="450"
-    ></iframe>
+    <iframe	src="https://aiecosystem-hr-ai.hf.space"	frameborder="0"	width="850"	height="450"></iframe>
     '''
     st.code(code, language="html")
     st.text("")
@@ -101,23 +89,18 @@ 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.")
 
 # --- Label Definitions ---
-
-labels = ["Role_Date", "Job_Title_Date", "Email", "Phone_number", "Street_address", "City", "Country", "Date_of_birth", "Marital_status", "Person", "Full_time", "Part_time", "Contract", "Terminated", "Retired", "Job_title", "Date", "Organization", "Role", "Performance_score", "Leave_of_absence", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance", "Pay_rate", "Annual_salary", "Tax", "Deductions", "Interview_type", "Applicant", "Referral", "Job_board", "Recruiter", "Offer_letter", "Agreement", "Certification", "Skill"]
-
-
+labels = ["Email", "Phone_number", "Street_address", "City", "Country", "Date_of_birth", "Marital_status", "Person", "Full_time", "Part_time", "Contract", "Terminated", "Retired", "Job_title", "Date", "Organization", "Role", "Performance_score", "Leave_of_absence", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance", "Pay_rate", "Annual_salary", "Tax", "Deductions", "Interview_type", "Applicant", "Referral", "Job_board", "Recruiter", "Offer_letter", "Agreement", "Certification", "Skill"]
 
 # Create a mapping dictionary for labels to categories
-
 category_mapping = {
     "Contact Information": ["Email", "Phone_number", "Street_address", "City", "Country"],
     "Personal Details": ["Date_of_birth", "Marital_status", "Person"],
     "Employment Status": ["Full_time", "Part_time", "Contract", "Terminated", "Retired"],
-    "Employment Information" : ["Job_title", "Date", "Organization", "Role", "Role_Date", "Job_Title_Date"],
+    "Employment Information" : ["Job_title", "Date", "Organization", "Role"],
     "Performance": ["Performance_score"],
     "Attendance": ["Leave_of_absence"],
     "Benefits": ["Retirement_plan", "Bonus", "Stock_options", "Health_insurance"],
@@ -125,16 +108,9 @@ category_mapping = {
     "Deductions": ["Tax", "Deductions"],
     "Recruitment & Sourcing": ["Interview_type", "Applicant", "Referral", "Job_board", "Recruiter"],
     "Legal & Compliance": ["Offer_letter", "Agreement"],
-    "Professional_Development": [ "Certification", "Skill"]
+    "Professional_Development": ["Certification", "Skill"]
 }
 
-
-
-
-
-
-
-
 # --- Model Loading ---
 @st.cache_resource
 def load_ner_model():
@@ -144,21 +120,17 @@ def load_ner_model():
     except Exception as e:
         st.error(f"Failed to load NER model. Please check your internet connection or model availability: {e}")
         st.stop()
-model = load_ner_model()
 
-# Flatten the mapping to a single dictionary
+model = load_ner_model()
 reverse_category_mapping = {label: category for category, label_list in category_mapping.items() for label in label_list}
 
 # --- Text Input and Clear Button ---
 text = st.text_area("Type or paste your text below, and then press Ctrl + Enter", height=250, key='my_text_area')
-
 def clear_text():
     """Clears the text area."""
     st.session_state['my_text_area'] = ""
-
 st.button("Clear text", on_click=clear_text)
 
-
 # --- Results Section ---
 if st.button("Results"):
     start_time = time.time()
@@ -166,229 +138,109 @@ if st.button("Results"):
         st.warning("Please enter some text to extract entities.")
     else:
         with st.spinner("Extracting entities...", show_time=True):
-            entities = model.predict_entities(text, labels)
-            df = pd.DataFrame(entities)
-
-            if not df.empty:
-                df['category'] = df['label'].map(reverse_category_mapping)
-                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)
-                
-                st.subheader("Grouped Entities by Category", divider = "green")
-                
-                # Create tabs for each category
-                category_names = sorted(list(category_mapping.keys()))
-                category_tabs = st.tabs(category_names)
-                
-                for i, category_name in enumerate(category_names):
-                    with category_tabs[i]:
-                        df_category_filtered = df[df['category'] == category_name]
-                        if not df_category_filtered.empty:
-                            st.dataframe(df_category_filtered.drop(columns=['category']), use_container_width=True)
-                        else:
-                            st.info(f"No entities found for the '{category_name}' category.")
-
+            try:
+                entities = model.predict_entities(text, labels)
+                df = pd.DataFrame(entities)
                 
-
-                with st.expander("See Glossary of tags"):
-                    st.write('''
-                    - **text**: ['entity extracted from your text data']
-                    - **score**: ['accuracy score; how accurately a tag has been assigned to a given entity']
-                    - **label**: ['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']
-                    ''')
-                st.divider()
+                if not df.empty:
+                    df['category'] = df['label'].map(reverse_category_mapping)
+                    
+                    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)
+                        
+                    st.subheader("Grouped Entities by Category", divider="green")
+                    category_names = sorted(list(category_mapping.keys()))
+                    category_tabs = st.tabs(category_names)
+                    
+                    for i, category_name in enumerate(category_names):
+                        with category_tabs[i]:
+                            df_category_filtered = df[df['category'] == category_name]
+                            if not df_category_filtered.empty:
+                                st.dataframe(df_category_filtered.drop(columns=['category']), use_container_width=True)
+                            else:
+                                st.info(f"No entities found for the '{category_name}' category.")
                                 
-                # Tree map
-                st.subheader("Tree map", divider = "green")
-                fig_treemap = px.treemap(df, path=[px.Constant("all"), 'category', 'label', 'text'], values='score', color='category')
-                fig_treemap.update_layout(margin=dict(t=50, l=25, r=25, b=25), paper_bgcolor='#F5FFFA', plot_bgcolor='#F5FFFA')
-                st.plotly_chart(fig_treemap)
-
-                @st.cache_resource
-                def load_gliner_model():
-                    try:
-                        return GLiNER.from_pretrained("knowledgator/gliner-multitask-v1.0", device="cpu")
-                    except Exception as e:
-                        st.error(f"Error loading the GLiNER model: {e}")
-                        st.stop()
-                
-                model = load_gliner_model()
-                st.subheader("Question-Answering", divider = "violet")
-
-
-                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:
-            # Create a unique key for each button using the index
-            if st.button("Delete", key=f"delete_{i}"):
-                # Remove the label at the specific index
-                st.session_state.user_labels.pop(i)
-                # Rerun to update the UI
-                st.rerun()
-else:
-    st.info("No questions defined yet. Use the input above to add one.")
-
-def get_stable_color(label):
-    """Generates a consistent hexadecimal color from a given string."""
-    hash_object = hashlib.sha1(label.encode('utf-8'))
-    hex_dig = hash_object.hexdigest()
-    return '#' + hex_dig[:6]
-
-st.divider()
-
-# --- Main Processing Logic ---
-if st.button("Extract Answers"):
-    if not user_text.strip():
-        st.warning("Please enter some text to analyze.")
-    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:
-                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'})
+                    with st.expander("See Glossary of tags"):
+                        st.write('''
+                        - **text**: ['entity extracted from your text data']
+                        - **score**: ['accuracy score; how accurately a tag has been assigned to a given entity']
+                        - **label**: ['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']
+                        ''')
+                        
+                    st.divider()
                     
-                    st.subheader("Extracted Answers", divider = "violet")
-                    st.dataframe(df, use_container_width=True)
-
+                    # Tree map
+                    st.subheader("Tree map", divider="green")
+                    fig_treemap = px.treemap(df, path=[px.Constant("all"), 'category', 'label', 'text'], values='score', color='category')
+                    fig_treemap.update_layout(margin=dict(t=50, l=25, r=25, b=25), paper_bgcolor='#F5FFFA', plot_bgcolor='#F5FFFA')
+                    st.plotly_chart(fig_treemap)
                     
-
+                    # Download Section
+                    st.divider()
+                    
+                    df_results = df[['label', 'text', 'score']]
+                    csv_data = df_results.to_csv(index=False).encode('utf-8')
                     
-                    csv_data = df.to_csv(index=False).encode('utf-8')
                     with stylable_container(
-                        key="download_button",
-                        css_styles="""button { background-color: red; border: 1px solid black; padding: 5px; color: white; }""",
+                        key="download_csv_button",
+                        css_styles="""button { background-color: #D4F4D4; border: 1px solid black; padding: 5px; color: black; }""",
                     ):
                         st.download_button(
-                            label="Download CSV",
+                            label="Download results (CSV)",
                             data=csv_data,
                             file_name="nlpblogs_results.csv",
                             mime="text/csv",
+                            key="download_csv"
+                        )
+                        
+                    dfa = pd.DataFrame(
+                        data={
+                            'Column Name': ['text', 'label', 'score', 'start', 'end'],
+                            'Description': [
+                                'entity extracted from your text data',
+                                'label (tag) assigned to a given extracted entity',
+                                'accuracy score; how accurately a tag has been assigned to a given entity',
+                                'index of the start of the corresponding entity',
+                                '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_results.to_csv(index=False))
+                        myzip.writestr("Glossary of tags.csv", dfa.to_csv(index=False))
+                    
+                    with stylable_container(
+                        key="download_zip_button",
+                        css_styles="""button { background-color: #D4F4D4; border: 1px solid black; padding: 5px; color: black; }""",
+                    ):
+                        st.download_button(
+                            label="Download results and glossary (zip)",
+                            data=buf.getvalue(),
+                            file_name="nlpblogs_results.zip",
+                            mime="application/zip",
+                            key="download_zip"
                         )
-
-                    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.log_figure(figure=fig_treemap, figure_name="entity_treemap_categories")
                         experiment.end()
+                else:
+                    st.warning("No entities were found in the provided text.")
             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()
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-                
-                # Download Section
-                st.divider()
-                                
-                dfa = pd.DataFrame(
-                    data={
-                        'Column Name': ['text', 'label', 'score', 'start', 'end'],
-                        'Description': [
-                            'entity extracted from your text data',
-                            'label (tag) assigned to a given extracted entity',
-                            'accuracy score; how accurately a tag has been assigned to a given entity',
-                            'index of the start of the corresponding entity',
-                            '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: red; border: 1px solid black; padding: 5px; color: white; }""",
-                ):
-                    st.download_button(
-                        label="Download results and glossary (zip)",
-                        data=buf.getvalue(),
-                        file_name="nlpblogs_results.zip",
-                        mime="application/zip",
-                    )
-                                
-                if comet_initialized:
-                    experiment.log_figure(figure=fig_treemap, figure_name="entity_treemap_categories")
-                    experiment.end()
-            else: # If df is empty
-                st.warning("No entities were found in the provided text.")
-    
-    end_time = time.time()
-    elapsed_time = end_time - start_time
-    st.text("")
-    st.text("")
-    st.info(f"Results processed in **{elapsed_time:.2f} seconds**.")
-    
+                    
+        end_time = time.time()
+        elapsed_time = end_time - start_time
+        st.info(f"Results processed in **{elapsed_time:.2f} seconds**.")