Update src/streamlit_app.py

src/streamlit_app.py

@@ -19,40 +19,40 @@ st.markdown(
     <style>
     /* Main app background and text color */
     .stApp {
-        background-color: #FFF8F0; /* A very light, creamy orange */
-        color: #000000; /* Black for text */
+        background-color: #F0F2F5; /* A very light gray */
+        color: #333333; /* Dark gray for text */
     }
     /* Sidebar background color */
     .css-1d36184 {
-        background-color: #FFC080; /* A soft orange for the sidebar */
-        secondary-background-color: #FFC080;
+        background-color: #B0C4DE; /* A soft, slate blue for the sidebar */
+        secondary-background-color: #B0C4DE;
     }
     /* Expander background color */
     .streamlit-expanderContent {
-        background-color: #FFF8F0;
+        background-color: #F0F2F5;
     }
     /* Expander header background color */
     .streamlit-expanderHeader {
-        background-color: #FFF8F0;
+        background-color: #F0F2F5;
     }
     /* Text Area background and text color */
     .stTextArea textarea {
-        background-color: #FFDDAA; /* A light, soft orange */
-        color: #000000; /* Black for text */
+        background-color: #FFFFFF; /* White background */
+        color: #333333; /* Dark gray for text */
     }
     /* Button background and text color */
     .stButton > button {
-        background-color: #FFDDAA;
-        color: #000000;
+        background-color: #6495ED; /* A bright, cornflower blue */
+        color: #FFFFFF; /* White text */
     }
     /* Warning box background and text color */
     .stAlert.st-warning {
-        background-color: #FFBB88; /* A slightly darker orange for warnings */
+        background-color: #FFD700; /* A gold color for warnings */
         color: #000000;
     }
     /* Success box background and text color */
     .stAlert.st-success {
-        background-color: #FFBB88; /* A slightly darker orange for success boxes */
+        background-color: #90EE90; /* A light green for success */
         color: #000000;
     }
     </style>
@@ -61,26 +61,31 @@ st.markdown(
 )
 
 
-
-
-
-
-
 # --- Page Configuration and UI Elements ---
 st.set_page_config(layout="wide", page_title="Named Entity Recognition App")
-st.subheader("Multilingual", divider="orange")
+st.subheader("Multilingual", divider="green")
 st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
 expander = st.expander("**Important notes**")
-expander.write("""**Named Entities:** This Business Core predicts twenty-six (26) labels: "Person", "Contact", "Company", "Department", "Vendor", "Client", "Office", "Warehouse", "Address", "City", "State", "Country", "Date", "Time", "Time Period", "Revenue", "Cost", "Budget", "Invoice Number", "Product", "Service", "Task", "Project", "Status", "Asset", "Transaction"
+
+expander.write("""**Named Entities:** This Multilingual predicts twenty-six (26) labels: 
+"Person","First_name","Last_name","Title","Job_title","Affiliation","Gender","Age","Date","Nationality","Location","City","Country", "Role","Relationship"
+
 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     
+
+**Supported Languages:** English    
+
+**Language settings:** Please check and adjust the language settings in your computer, so the characters of your chosen language are handled properly in your downloaded file.
+
 **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 Business Core 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 Multilingual web app on your website. Feel free to adjust the width and height values to fit your page.")
     code = '''
     <iframe
 	src="https://aiecosystem-business-core.hf.space"
@@ -207,7 +212,7 @@ if st.button("Results"):
                     experiment.log_parameter("input_text", text)
                     experiment.log_table("predicted_entities", df)
                 
-                st.subheader("Grouped Entities by Category", divider = "orange")
+                st.subheader("Grouped Entities by Category", divider = "green")
                 
                 # Create tabs for each category
                 category_names = sorted(list(category_mapping.keys()))
@@ -235,7 +240,7 @@ if st.button("Results"):
                 st.divider()
                                 
                 # Tree map
-                st.subheader("Tree map", divider = "orange")
+                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='#FFF8F0', plot_bgcolor='#FFF8F0')
                 st.plotly_chart(fig_treemap)
@@ -246,26 +251,26 @@ if st.button("Results"):
                 col1, col2 = st.columns(2)
                 
                 with col1:
-                    st.subheader("Pie chart", divider = "orange")
+                    st.subheader("Pie chart", divider = "green")
                     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='#FFF8F0',
-                        plot_bgcolor='#FFF8F0'
+                        paper_bgcolor='#F0F2F5',
+                        plot_bgcolor='#F0F2F5'
                     )
                     st.plotly_chart(fig_pie)
                 
                 with col2:
-                    st.subheader("Bar chart", divider = "orange")
+                    st.subheader("Bar chart", divider = "green")
                     fig_bar = px.bar(grouped_counts, x="count", y="category", color="category", text_auto=True, title='Occurrences of predicted categories')
                     fig_bar.update_layout(
-                        paper_bgcolor='#FFF8F0',
-                        plot_bgcolor='#FFF8F0'
+                        paper_bgcolor='#F0F2F5',
+                        plot_bgcolor='#F0F2F5'
                     )
                     st.plotly_chart(fig_bar)
                 
                 # Most Frequent Entities
-                st.subheader("Most Frequent Entities", divider="orange")
+                st.subheader("Most Frequent Entities", divider="green")
                 word_counts = df['text'].value_counts().reset_index()
                 word_counts.columns = ['Entity', 'Count']
                 repeating_entities = word_counts[word_counts['Count'] > 1]
@@ -273,8 +278,8 @@ 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='#FFF8F0',
-                        plot_bgcolor='#FFF8F0')
+                        paper_bgcolor='#F0F2F5',
+                        plot_bgcolor='#F0F2F5')
                     st.plotly_chart(fig_repeating_bar)
                 else:
                     st.warning("No entities were found that occur more than once.")