Update src/streamlit_app.py

src/streamlit_app.py

@@ -7,13 +7,13 @@ import io
 import plotly.express as px
 import zipfile
 import json
-from cryptography.fernet import Fernet
+from cryptography.fernet import Fernet # This import is not used
 from streamlit_extras.stylable_container import stylable_container
 from typing import Optional
 from gliner import GLiNER
 from comet_ml import Experiment
 
-
+# --- CSS Styling for the App ---
 st.markdown(
     """
     <style>
@@ -22,36 +22,36 @@ st.markdown(
         background-color: #E8F5E9; /* A very light green */
         color: #1B5E20; /* Dark green for the text */
     }
-    
+
     /* Sidebar background color */
     .css-1d36184 {
         background-color: #A5D6A7; /* A medium light green */
         secondary-background-color: #A5D6A7;
     }
-    
+
     /* Expander background color and header */
     .streamlit-expanderContent, .streamlit-expanderHeader {
         background-color: #E8F5E9;
     }
-    
+
     /* Text Area background and text color */
     .stTextArea textarea {
         background-color: #81C784; /* A slightly darker medium green */
         color: #1B5E20; /* Dark green for text */
     }
-    
+
     /* Button background and text color */
     .stButton > button {
         background-color: #81C784;
         color: #1B5E20;
     }
-    
+
     /* Warning box background and text color */
     .stAlert.st-warning {
         background-color: #66BB6A; /* A medium-dark green for the warning box */
         color: #1B5E20;
     }
-    
+
     /* Success box background and text color */
     .stAlert.st-success {
         background-color: #66BB6A; /* A medium-dark green for the success box */
@@ -61,10 +61,6 @@ st.markdown(
     """,
     unsafe_allow_html=True
 )
-   
-
-
-
 
 # --- Page Configuration and UI Elements ---
 st.set_page_config(layout="wide", page_title="Named Entity Recognition App")
@@ -72,44 +68,41 @@ st.subheader("RetailTag", divider="violet")
 st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
 
 expander = st.expander("**Important notes**")
-expander.write("""**Named Entities:** This RetailTag web app predicts eighteen (18) labels:
+expander.write("""
+**Named Entities:** This RetailTag web app predicts eighteen (18) labels:
 "Product_Name", "Product_Type", "Brand", "Model_Number", "SKU", "Product_Attribute", "Service_Type", "Order_Number", "Monetary_Value", "Payment_Method", "Discount", "Shipping_Method", "Quantity", "Organization", "Location", "Person", "Date", "Time"
-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 RetailTag web app on your website. Feel free to adjust the width and height values to fit your page.")
     code = '''
-    <iframe
-	src="https://aiecosystem-retailtag.hf.space"
-	frameborder="0"
-	width="850"
-	height="450"
-    ></iframe>
-   
+    <iframe src="https://aiecosystem-chainsense.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="violet")
-    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 ---
-[
+# The list of labels is assigned to a variable for use in the model loading function
+labels = [
     "Product_Name",
     "Product_Type",
     "Brand",
@@ -130,9 +123,6 @@ if not comet_initialized:
     "Time"
 ]
 
-
-# Corrected mapping dictionary
-
 # Create a mapping dictionary for labels to categories
 category_mapping = {
     "Product & Service Entities": [
@@ -161,17 +151,17 @@ category_mapping = {
     ]
 }
 
-
-
 # --- Model Loading ---
 @st.cache_resource
 def load_ner_model():
     """Loads the GLiNER model and caches it."""
     try:
-        return GLiNER.from_pretrained("knowledgator/gliner-multitask-large-v0.5", nested_ner=True, num_gen_sequences=2, gen_constraints= labels)
+        # The 'labels' variable is now correctly passed to the function
+        return GLiNER.from_pretrained("knowledgator/gliner-multitask-large-v0.5", nested_ner=True, num_gen_sequences=2, gen_constraints=labels)
     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
@@ -183,10 +173,8 @@ text = st.text_area("Type or paste your text below, and then press Ctrl + Enter"
 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()
@@ -196,9 +184,10 @@ 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:
                     experiment = Experiment(
                         api_key=COMET_API_KEY,
@@ -208,7 +197,7 @@ if st.button("Results"):
                     experiment.log_parameter("input_text", text)
                     experiment.log_table("predicted_entities", df)
                 
-                st.subheader("Grouped Entities by Category", divider = "violet")
+                st.subheader("Grouped Entities by Category", divider="violet")
                 
                 # Create tabs for each category
                 category_names = sorted(list(category_mapping.keys()))
@@ -222,8 +211,6 @@ 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']
@@ -233,20 +220,20 @@ if st.button("Results"):
                     - **end**: ['index of the end of the corresponding entity']
                     ''')
                 st.divider()
-                                
+
                 # Tree map
-                st.subheader("Tree map", divider = "violet")
+                st.subheader("Tree map", divider="violet")
                 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='#E8F5E9', plot_bgcolor='#E8F5E9')
                 st.plotly_chart(fig_treemap)
-                
+
                 # Pie and Bar charts
                 grouped_counts = df['category'].value_counts().reset_index()
                 grouped_counts.columns = ['category', 'count']
                 col1, col2 = st.columns(2)
-                
+
                 with col1:
-                    st.subheader("Pie chart", divider = "violet")
+                    st.subheader("Pie chart", divider="violet")
                     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(
@@ -254,19 +241,16 @@ if st.button("Results"):
                         plot_bgcolor='#E8F5E9'
                     )
                     st.plotly_chart(fig_pie)
-                
-                
-
 
                 with col2:
-                    st.subheader("Bar chart", divider = "violet")
+                    st.subheader("Bar chart", divider="violet")
                     fig_bar = px.bar(grouped_counts, x="count", y="category", color="category", text_auto=True, title='Occurrences of predicted categories')
-                    fig_bar.update_layout(  # Changed from fig_pie to fig_bar
+                    fig_bar.update_layout(
                         paper_bgcolor='#E8F5E9',
                         plot_bgcolor='#E8F5E9'
                     )
                     st.plotly_chart(fig_bar)
-                
+
                 # Most Frequent Entities
                 st.subheader("Most Frequent Entities", divider="violet")
                 word_counts = df['text'].value_counts().reset_index()
@@ -275,16 +259,18 @@ if st.button("Results"):
                 if not repeating_entities.empty:
                     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'},
+                    fig_repeating_bar.update_layout(
+                        xaxis={'categoryorder': 'total descending'},
                         paper_bgcolor='#E8F5E9',
-                        plot_bgcolor='#E8F5E9')
+                        plot_bgcolor='#E8F5E9'
+                    )
                     st.plotly_chart(fig_repeating_bar)
                 else:
                     st.warning("No entities were found that occur more than once.")
-                
+
                 # Download Section
                 st.divider()
-                                
+
                 dfa = pd.DataFrame(
                     data={
                         'Column Name': ['text', 'label', 'score', 'start', 'end'],
@@ -294,7 +280,6 @@ if st.button("Results"):
                             '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',
-                            
                         ]
                     }
                 )
@@ -302,10 +287,10 @@ if st.button("Results"):
                 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; }""",
+                    css_styles="""button { background-color: #81C784; border: 1px solid black; padding: 5px; color: #1B5E20; }""",
                 ):
                     st.download_button(
                         label="Download results and glossary (zip)",
@@ -313,15 +298,15 @@ if st.button("Results"):
                         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**.")
+
+        end_time = time.time()
+        elapsed_time = end_time - start_time
+        st.text("")
+        st.text("")
+        st.info(f"Results processed in **{elapsed_time:.2f} seconds**.")