Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,27 +1,18 @@
 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>
@@ -35,6 +26,7 @@ st.markdown(
         background-color: #B2F2B2; /* A pale green for the sidebar */
         secondary-background-color: #B2F2B2;
     }
+    
     /* Expander background color */
     .streamlit-expanderContent {
         background-color: #F5FFFA;
@@ -68,37 +60,53 @@ 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 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("")
     st.text("")
     st.divider()
     st.subheader("🚀 Ready to build your own AI Web App?", divider="green")
-    st.link_button("AI Web App Builder", "https://nlpblogs.com/custom-web-app-development/", type="primary")
+    st.link_button("AI Web App Builder", " https://nlpblogs.com/custom-web-app-development/", 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.")
 
 # --- Label Definitions ---
+
 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"],
@@ -111,9 +119,16 @@ 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():
@@ -123,17 +138,21 @@ 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
 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()
@@ -141,109 +160,192 @@ if st.button("Results"):
         st.warning("Please enter some text to extract entities.")
     else:
         with st.spinner("Extracting entities...", show_time=True):
-            try:
-                entities = model.predict_entities(text, labels)
-                df = pd.DataFrame(entities)
+            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)
                 
-                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.")
+                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.")
+
+                
+
+                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']
+                    - **category**: ['the high-level category for the label']
+                    - **start**: ['index of the start of the corresponding entity']
+                    - **end**: ['index of the end of the corresponding entity']
+                    ''')
+                st.divider()
                                 
-                    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()
-                    
-                    # 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()
+                # 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)
+                
+                # --- Model Loading and Caching ---
+@st.cache_resource
+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-v1.0", device="cpu")
+    except Exception as e:
+        st.error(f"Error loading the GLiNER model: {e}")
+        st.stop()
+
+# Load the model
+model = load_gliner_model()
+
+
+
+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:
+    # Use enumerate to create a unique key for each item
+    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 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(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'})
                     
-                    df_results = df[['label', 'text', 'score']]
-                    csv_data = df_results.to_csv(index=False).encode('utf-8')
+                    st.subheader("Extracted Answers", divider = "violet")
+                    st.dataframe(df, use_container_width=True)
+
+                  
                     
-                    with stylable_container(
-                        key="download_csv_button",
-                        css_styles="""button { background-color: #D4F4D4; border: 1px solid black; padding: 5px; color: black; }""",
-                    ):
-                        st.download_button(
-                            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',
-                            ]
-                        }
+
+
+
+
+                
+                st.divider()
+                                
+                dfa = pd.DataFrame(
+                    data={
+                        'Column Name': ['text', 'label', 'score', 'start', 'end', 'category'],
+                        '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',
+                            'the broader category the entity belongs to',
+                        ]
+                    }
+                )
+                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",
                     )
-                    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_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}")
+                                
                 if comet_initialized:
-                    experiment.log_text(f"Error: {e}")
+                    experiment.log_figure(figure=fig_treemap, figure_name="entity_treemap_categories")
                     experiment.end()
-                    
-        end_time = time.time()
-        elapsed_time = end_time - start_time
-        st.info(f"Results processed in **{elapsed_time:.2f} seconds**.")
+            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**.")