Update src/streamlit_app.py

src/streamlit_app.py

@@ -13,46 +13,54 @@ from typing import Optional
 from gliner import GLiNER
 from comet_ml import Experiment
 
+
+
 st.markdown(
     """
     <style>
     /* Main app background and text color */
     .stApp {
-        background-color: #F5FFFA; /* Mint cream, a very light green */
+        background-color: #E0FFFF; /* Light cyan, a very pale blue */
         color: #000000; /* Black for the text */
     }
+
     /* Sidebar background color */
     .css-1d36184 {
-        background-color: #B2F2B2; /* A pale green for the sidebar */
-        secondary-background-color: #B2F2B2;
+        background-color: #ADD8E6; /* Light blue for the sidebar */
+        secondary-background-color: #ADD8E6;
     }
-    
+        
     /* Expander background color */
     .streamlit-expanderContent {
-        background-color: #F5FFFA;
+        background-color: #E0FFFF;
     }
+    
     /* Expander header background color */
     .streamlit-expanderHeader {
-        background-color: #F5FFFA;
+        background-color: #E0FFFF;
     }
+
     /* Text Area background and text color */
     .stTextArea textarea {
-        background-color: #D4F4D4; /* A light, soft green */
+        background-color: #B0E0E6; /* Powder blue, a light, soft blue */
         color: #000000; /* Black for text */
     }
+
     /* Button background and text color */
     .stButton > button {
-        background-color: #D4F4D4;
+        background-color: #B0E0E6;
         color: #000000;
     }
+
     /* Warning box background and text color */
     .stAlert.st-warning {
-        background-color: #C8F0C8; /* A light green for the warning box */
+        background-color: #87CEEB; /* Sky blue for the warning box */
         color: #000000;
     }
+
     /* Success box background and text color */
     .stAlert.st-success {
-        background-color: #C8F0C8; /* A light green for the success box */
+        background-color: #87CEEB; /* Sky blue for the success box */
         color: #000000;
     }
     </style>
@@ -60,34 +68,45 @@ 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("StoryCraft", divider="green")
+st.subheader("StoryCraft", divider="blue")
 st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
 expander = st.expander("**Important notes**")
-expander.write("""**Named Entities:** This HR.ai predicts fifty-nine (59) labels:"Email", "Phone_number", "Street_address", "City", "State", "Zip_code", "Country", "Date_of_birth", "Gender", "Marital_status", "Person", "Full_time", "Part_time", "Contract", "Temporary", "Terminated", "Active", "Retired", "Job_title", "Employment_type", "Year", "Date", "Company", "Organization", "Role", "Position","Performance_review", "Performance_rating", "Performance_score", "Sick_days", "Vacation_days", "Leave_of_absence", "Holidays", "Pension", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance", "Pay_rate", "Hourly_wage", "Annual_salary", "Overtime_pay", "Tax", "Social_security", "Deductions", "Job_posting", "Job_description", "Interview_type", "Applicant", "Candidate", "Referral", "Job_board", "Recruiter","Contract", "Offer_letter", "Agreement", "Training_course", "Certification", "Skill"      
+expander.write("""**Named Entities:** This HR.ai predicts eighteen (18) labels: "Person","Organization","Location","Date","Time","Quantity","Product","Event","Title","Job_title","Artwork","Media", "URL","Website","Hashtag","Email_address","IP_address","File_path"
+
 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 [email protected]""")
 
 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.")
+    st.write("Use the following code to embed the StoryCraft 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"
+   <iframe
+	src="https://aiecosystem-storycraft.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 NER Web App?", divider="green")
+    st.subheader("🚀 Ready to build your own NER Web App?", divider="blue")
     st.link_button("NER Builder", "https://nlpblogs.com", type="primary")
 
 # --- Comet ML Setup ---
@@ -156,7 +175,7 @@ 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 = "blue")
                 
                 # Create tabs for each category
                 category_names = sorted(list(category_mapping.keys()))
@@ -184,9 +203,9 @@ if st.button("Results"):
                 st.divider()
                                 
                 # Tree map
-                st.subheader("Tree map", divider = "green")
+                st.subheader("Tree map", divider = "blue")
                 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')
+                fig_treemap.update_layout(margin=dict(t=50, l=25, r=25, b=25), paper_bgcolor='#E0FFFF', plot_bgcolor='#E0FFFF')
                 st.plotly_chart(fig_treemap)
                 
                 # Pie and Bar charts
@@ -195,26 +214,26 @@ if st.button("Results"):
                 col1, col2 = st.columns(2)
                 
                 with col1:
-                    st.subheader("Pie chart", divider = "green")
+                    st.subheader("Pie chart", divider = "blue")
                     fig_pie = px.pie(grouped_counts, values='count', names='category', hover_data=['count'], labels={'count': 'count'}, title='Percentage of predicted categories')
                     fig_pie.update_traces(textposition='inside', textinfo='percent+label')
                     fig_pie.update_layout(
-                        paper_bgcolor='#F5FFFA',
-                        plot_bgcolor='#F5FFFA'
+                        paper_bgcolor='#E0FFFF',
+                        plot_bgcolor='#E0FFFF'
                     )
                     st.plotly_chart(fig_pie)
                 
                 with col2:
-                    st.subheader("Bar chart", divider = "green")
+                    st.subheader("Bar chart", divider = "blue")
                     fig_bar = px.bar(grouped_counts, x="count", y="category", color="category", text_auto=True, title='Occurrences of predicted categories')
                     fig_pie.update_layout(
-                        paper_bgcolor='#F5FFFA',
-                        plot_bgcolor='#F5FFFA'
+                        paper_bgcolor='#E0FFFF',
+                        plot_bgcolor='#E0FFFF'
                     )
                     st.plotly_chart(fig_bar)
                 
                 # Most Frequent Entities
-                st.subheader("Most Frequent Entities", divider="green")
+                st.subheader("Most Frequent Entities", divider="blue")
                 word_counts = df['text'].value_counts().reset_index()
                 word_counts.columns = ['Entity', 'Count']
                 repeating_entities = word_counts[word_counts['Count'] > 1]
@@ -222,8 +241,8 @@ if st.button("Results"):
                     st.dataframe(repeating_entities, use_container_width=True)
                     fig_repeating_bar = px.bar(repeating_entities, x='Entity', y='Count', color='Entity')
                     fig_repeating_bar.update_layout(xaxis={'categoryorder': 'total descending'},
-                        paper_bgcolor='#F5FFFA',
-                        plot_bgcolor='#F5FFFA')
+                        paper_bgcolor='#E0FFFF',
+                        plot_bgcolor='#E0FFFF')
                     st.plotly_chart(fig_repeating_bar)
                 else:
                     st.warning("No entities were found that occur more than once.")