Update app.py

app.py

@@ -11,23 +11,20 @@ import docx
 import zipfile
 from gliner import GLiNER
 
+st.subheader("8-English Named Entity Recognition Web App", divider="red")
+st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
 
-
-st.subheader("8-Named Entity Recognition Web App", divider = "red")
-st.link_button("by nlpblogs", "https://nlpblogs.com", type = "tertiary")
-
-expander = st.expander("**Important notes on the 8-Named Entity Recognition Web App**")
+expander = st.expander("**Important notes on the 8-English Named Entity Recognition Web App**")
 expander.write('''
-    
     **Named Entities:**
-    This 8-Named Entity Recognition Web App predicts eight (8) labels (“person”, “country”, “city”, “organization”, “date”, “money”, “percent value”, “position”). Results are presented in an easy-to-read table, visualized in an interactive tree map, pie chart, and bar chart, and are available for download along with a Glossary of tags.
+    This 8-English Named Entity Recognition Web App predicts eight (8) labels (“person”, “country”, “city”, “organization”, “date”, “money”, “percent value”, “position”). Results are presented in an easy-to-read table, 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:**
     Upload your .pdf or .docx file. Then, click the 'Results' button to extract and tag entities in your text data.
     
     **Usage Limits:**
     You can request results up to 10 times.
- 
+        
     **Customization:**
     To change the app's background color to white or black, click the three-dot menu on the right-hand side of your app, go to Settings and then Choose app theme, colors and fonts.
     
@@ -35,41 +32,29 @@ expander.write('''
     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:
     container = st.container(border=True)
     container.write("**Named Entity Recognition (NER)** is the task of extracting and tagging entities in text data. Entities can be persons, organizations, locations, countries, products, events etc.")
-    
-   
-    
-    
-    
-    st.subheader("Related NLP Web Apps", divider = "red")
-    st.link_button("14-Named Entity Recognition Web App", "https://nlpblogs.com/shop/named-entity-recognition-ner/14-named-entity-recognition-web-app/", type = "primary")
-
+    st.subheader("Related NLP Web Apps", divider="red")
+    st.link_button("14-Named Entity Recognition Web App", "https://nlpblogs.com/shop/named-entity-recognition-ner/14-named-entity-recognition-web-app/", type="primary")
 
-
-   
-
-    
-    
-       
+# Cache the GLiNER model to prevent reloading on every rerun
+@st.cache_resource
+def load_gliner_model():
+    """Loads the GLiNER model."""
+    return GLiNER.from_pretrained("xomad/gliner-model-merge-large-v1.0")
 
 if 'file_upload_attempts' not in st.session_state:
     st.session_state['file_upload_attempts'] = 0
 
 max_attempts = 10
-
-
 upload_file = st.file_uploader("Upload your file. Accepted file formats include: .pdf, .docx", type=['pdf', 'docx'])
 text = None
 df = None
 
 if upload_file is not None:
-    
     file_extension = upload_file.name.split('.')[-1].lower()
     if file_extension == 'pdf':
         try:
@@ -89,94 +74,92 @@ if upload_file is not None:
             st.error(f"An error occurred while reading docx: {e}")
     else:
         st.warning("Unsupported file type.")
-    
+    # Stop execution here if a file was uploaded but not processed yet or if an error occurred
+    # to prevent the "Results" button from being clicked without valid text.
+    if text is None:
         st.stop()
 
-
-        
-
-
-
-
-
 if st.button("Results"):
     if st.session_state['file_upload_attempts'] >= max_attempts:
         st.error(f"You have requested results {max_attempts} times. You have reached your daily request limit.")
         st.stop()
+    if text is None:
+        st.warning("Please upload a file first to get results.")
+        st.stop()
+
     st.session_state['file_upload_attempts'] += 1
-    
-    
+
     with st.spinner('Wait for it...', show_time=True):
-        time.sleep(5)
-        model = GLiNER.from_pretrained("xomad/gliner-model-merge-large-v1.0")
+        # Load the model using the cached function
+        model = load_gliner_model()
         labels = ["person", "country", "city", "organization", "date", "money", "percent value", "position"]
         entities = model.predict_entities(text, labels)
         df = pd.DataFrame(entities)
-            
-        properties = {"border": "2px solid gray", "color": "blue", "font-size": "16px"}
-        df_styled = df.style.set_properties(**properties)
-        st.dataframe(df_styled)
-        with st.expander("See Glossary of tags"):
-            st.write('''
-            '**text**': ['entity extracted from your text data'] 
-            
-            '**score**': ['accuracy score; how accurately a tag has been assigned to a given entity']
-            
-            '**label**': ['label (tag) assigned to a given extracted entity']
-            
-            '**start**': ['index of the start of the corresponding entity']
-            
-            '**end**': ['index of the end of the corresponding entity']
+
+    properties = {"border": "2px solid gray", "color": "blue", "font-size": "16px"}
+    df_styled = df.style.set_properties(**properties)
+    st.dataframe(df_styled)
+
+    with st.expander("See Glossary of tags"):
+        st.write('''
+        '**text**': ['entity extracted from your text data']
         
-    ''')
-        if df is not None:
-            fig = px.treemap(df, path=[px.Constant("all"), 'text', 'label'],
-                                 values='score', color='label')
-            fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
-            st.subheader("Tree map", divider = "red")
-            st.plotly_chart(fig)
-        if df is not None:
-            value_counts1 = df['label'].value_counts()
-            df1 = pd.DataFrame(value_counts1)
-            final_df = df1.reset_index().rename(columns={"index": "label"})
-            col1, col2 = st.columns(2)
-            with col1:
-                fig1 = px.pie(final_df, values='count', names='label', hover_data=['count'], labels={'count': 'count'}, title='Percentage of predicted labels')
-                fig1.update_traces(textposition='inside', textinfo='percent+label')
-                st.subheader("Pie Chart", divider = "red")
-                st.plotly_chart(fig1)
-            with col2:
-                fig2 = px.bar(final_df, x="count", y="label", color="label", text_auto=True, title='Occurrences of predicted labels')
-                st.subheader("Bar Chart", divider = "red")
-                st.plotly_chart(fig2)
-
-                
-
-            dfa = pd.DataFrame(
+        '**score**': ['accuracy score; how accurately a tag has been assigned to a given entity']
+        
+        '**label**': ['label (tag) assigned to a given extracted entity']
+        
+        '**start**': ['index of the start of the corresponding entity']
+        
+        '**end**': ['index of the end of the corresponding entity']
+        ''')
+
+    if df is not None:
+        fig = px.treemap(df, path=[px.Constant("all"), 'text', 'label'],
+                            values='score', color='label')
+        fig.update_layout(margin=dict(t=50, l=25, r=25, b=25))
+        st.subheader("Tree map", divider="red")
+        st.plotly_chart(fig)
+
+    if df is not None:
+        value_counts1 = df['label'].value_counts()
+        df1 = pd.DataFrame(value_counts1)
+        final_df = df1.reset_index().rename(columns={"index": "label"})
+        col1, col2 = st.columns(2)
+        with col1:
+            fig1 = px.pie(final_df, values='count', names='label', hover_data=['count'], labels={'count': 'count'}, title='Percentage of predicted labels')
+            fig1.update_traces(textposition='inside', textinfo='percent+label')
+            st.subheader("Pie Chart", divider="red")
+            st.plotly_chart(fig1)
+        with col2:
+            fig2 = px.bar(final_df, x="count", y="label", color="label", text_auto=True, title='Occurrences of predicted labels')
+            st.subheader("Bar Chart", divider="red")
+            st.plotly_chart(fig2)
+
+        dfa = pd.DataFrame(
             data={
                 'text': ['entity extracted from your text data'], 'score': ['accuracy score; how accurately a tag has been assigned to a given entity'], 'label': ['label (tag) assigned to a given extracted entity'],
                 'start': ['index of the start of the corresponding entity'],
                 'end': ['index of the end of the corresponding entity'],
                 })
-            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: yellow; border: 1px solid black; padding: 5px; color: black; }""",
-            ):
-                st.download_button(
+        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: yellow; border: 1px solid black; padding: 5px; color: black; }""",
+        ):
+            st.download_button(
                 label="Download zip file",
                 data=buf.getvalue(),
                 file_name="zip file.zip",
                 mime="application/zip",
-                )
-        
-        
-        
-     
-                    
+            )
+
 st.divider()
 st.write(f"Number of times you requested results: {st.session_state['file_upload_attempts']}/{max_attempts}")
+    
+
+  
+