Update src/streamlit_app.py

src/streamlit_app.py

@@ -15,47 +15,59 @@ from gliner import GLiNER
 from comet_ml import Experiment
 
 # --- Page Configuration and UI Elements ---
-st.set_page_config(layout="wide", page_title="Combined NLP App")
-st.subheader("Combined NLP App", divider="green")
+st.set_page_config(layout="wide", page_title="NER")
+st.subheader("HR.ai", divider="green")
 st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
 
+
 st.markdown(
     """
     <style>
+    /* Main app background and text color */
     .stApp {
-        background-color: #F5FFFA;
-        color: #000000;
+        background-color: #F5FFFA; /* Mint cream, a very light green */
+        color: #000000; /* Black for the text */
     }
+    /* Sidebar background color */
     .css-1d36184 {
-        background-color: #B2F2B2;
+        background-color: #B2F2B2; /* A pale green for the sidebar */
         secondary-background-color: #B2F2B2;
     }
-    .streamlit-expanderContent {
-        background-color: #F5FFFA;
-    }
-    .streamlit-expanderHeader {
+    /* Expander background color and header */
+    .streamlit-expanderContent, .streamlit-expanderHeader {
         background-color: #F5FFFA;
     }
+    /* Text Area background and text color */
     .stTextArea textarea {
-        background-color: #D4F4D4;
-        color: #000000;
+        background-color: #D4F4D4; /* A light, soft green */
+        color: #000000; /* Black for text */
     }
+    /* Text Input background and text color */
     .stTextInput input {
-        background-color: #B39DDB;
-        color: #1A0A26;
+        background-color: #D4F4D4; /* Same as the text area for consistency */
+        color: #000000;
     }
+    /* Button background and text color */
     .stButton > button {
         background-color: #D4F4D4;
         color: #000000;
     }
+    /* Warning box background and text color */
     .stAlert.st-warning {
-        background-color: #C8F0C8;
+        background-color: #C8F0C8; /* A light green for the warning box */
         color: #000000;
     }
+    /* Success box background and text color */
     .stAlert.st-success {
-        background-color: #C8F0C8;
+        background-color: #C8F0C8; /* A light green for the success box */
         color: #000000;
     }
+    /* Tab color when active */
+    .stTabs [data-baseweb="tab-list"] button[aria-selected="true"] {
+        background-color: #D4F4D4;
+        color: #000000;
+    }
+    /* Tab font size */
     .stTabs [data-baseweb="tab-list"] button [data-testid="stMarkdownContainer"] p {
         font-size: 1.25rem;
     }
@@ -64,14 +76,24 @@ st.markdown(
     unsafe_allow_html=True
 )
 
-expander = st.expander("**Important notes**")
-expander.write("""
-This application combines two NLP tools in one place:
-- **HR.ai**: Specializes in extracting 36 predefined HR-related entities from your text.
-- **InfoFinder**: Answers custom wh-questions by finding relevant entities in your text.
+expander.write("""**Named Entities:** This HR.ai web app predicts thirty-six (37) labels: "Job_with_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"
+
+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:** 
+1. 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.  
+2. Type or paste your text into the text area below, then press Ctrl + Enter. 
+3. 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.
+4. Click the 'Extract Answers' button to extract the answer to your question.
+
+**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""")
 
-Both tools feature interactive visualizations and data download options.
-""")
 
 with st.sidebar:
     st.write("Use the following code to embed the web app on your website. Feel free to adjust the width and height values to fit your page.")
@@ -141,10 +163,10 @@ def get_stable_color(label):
     return '#' + hex_dig[:6]
 
 # --- Main App with Tabs ---
-tab1, tab2 = st.tabs(["HR.ai (Named Entity Recognition)", "InfoFinder (Question-Answering)"])
+tab1, tab2 = st.tabs(["HR.ai", "InfoFinder"])
 
 with tab1:
-    st.markdown("### HR.ai: Named Entity Recognition")
+    st.subheader("HR.ai")
     
     # Load model for this tab
     model_hr = load_gliner_model("HR_AI")
@@ -194,7 +216,7 @@ with tab1:
                         ''')
                     st.divider()
                     
-                    st.subheader("Tree map", divider="green")
+                    st.subheader("Candidate Card", 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)
@@ -255,7 +277,7 @@ with tab1:
             st.info(f"Results processed in **{elapsed_time:.2f} seconds**.")
 
 with tab2:
-    st.markdown("### InfoFinder: Question-Answering")
+    st.subheader("InfoFinder")
 
     # Load model for this tab
     model_qa = load_gliner_model("InfoFinder")
@@ -281,7 +303,7 @@ with tab2:
             st.warning("Please enter a question.")
             
     st.markdown("---")
-    st.subheader("Record of Questions", divider="violet")
+    st.subheader("Record of Questions", divider="green")
 
     if st.session_state.user_labels:
         for i, label in enumerate(st.session_state.user_labels):
@@ -321,10 +343,10 @@ with tab2:
                         df2 = df1[['label', 'text', 'score']]
                         df = df2.rename(columns={'label': 'question', 'text': 'answer'})
                         
-                        st.subheader("Extracted Answers", divider="violet")
+                        st.subheader("Extracted Answers", divider="green")
                         st.dataframe(df, use_container_width=True)
 
-                        st.subheader("Tree map", divider="violet")
+                        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)