Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,5 +1,4 @@
 import os
-os.environ['HF_HOME'] = '/tmp'
 import time
 import streamlit as st
 import pandas as pd
@@ -26,7 +25,6 @@ st.markdown(
         background-color: #B2F2B2; /* A pale green for the sidebar */
         secondary-background-color: #B2F2B2;
     }
-    
     /* Expander background color */
     .streamlit-expanderContent {
         background-color: #F5FFFA;
@@ -66,22 +64,16 @@ 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 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""")
+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("")
@@ -91,6 +83,7 @@ with st.sidebar:
     st.link_button("AI Web App Builder", " https://nlpblogs.com/custom-web-app-development/", type="primary")
 
 # --- Comet ML Setup ---
+os.environ['HF_HOME'] = '/tmp'
 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")
@@ -100,13 +93,9 @@ 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"],
@@ -122,13 +111,6 @@ category_mapping = {
     "Professional_Development": [ "Certification", "Skill"]
 }
 
-
-
-
-
-
-
-
 # --- Model Loading ---
 @st.cache_resource
 def load_ner_model():
@@ -138,6 +120,7 @@ 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
@@ -152,7 +135,6 @@ def clear_text():
 
 st.button("Clear text", on_click=clear_text)
 
-
 # --- Results Section ---
 if st.button("Results"):
     start_time = time.time()
@@ -162,7 +144,6 @@ if st.button("Results"):
         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:
@@ -173,13 +154,12 @@ if st.button("Results"):
                     )
                     experiment.log_parameter("input_text", text)
                     experiment.log_table("predicted_entities", df)
-                
-                st.subheader("Grouped Entities by Category", divider = "green")
-                
+
+                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]
@@ -188,8 +168,6 @@ if st.button("Results"):
                         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']
@@ -200,152 +178,140 @@ if st.button("Results"):
                     - **end**: ['index of the end of the corresponding entity']
                     ''')
                 st.divider()
-                                
+
                 # Tree map
-                st.subheader("Tree map", divider = "green")
+                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()
-
 
+                # --- 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 'user_labels' not in st.session_state:
+                    st.session_state.user_labels = []
+
+                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.")
 
-st.subheader("Question-Answering", divider = "violet")
+                st.divider()
 
-# 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.**")
+                # --- 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(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'})
+
+                                    st.subheader("Extracted Answers", divider="violet")
+                                    st.dataframe(df, use_container_width=True)
+                                    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",
+                                        )
+
+                                    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.")
+                            except Exception as e:
+                                st.error(f"An error occurred during entity extraction: {e}")
+
+            else:  # If df is empty from the first extraction
+                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**.")
 
-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'})
-                    
-                    st.subheader("Extracted Answers", divider = "violet")
-                    st.dataframe(df, use_container_width=True)
-
-                  
-                    
-
-
-
-
-                
-                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",
-                    )
-                                
-                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**.")