Update src/streamlit_app.py

src/streamlit_app.py

@@ -14,36 +14,23 @@ from typing import Optional
 from gliner import GLiNER
 from comet_ml import Experiment
 
-
-
 # --- Page Configuration and UI Elements ---
 st.set_page_config(layout="wide", page_title="Named Entity Recognition App")
+st.markdown("""
+<style>
+/* ... (Your CSS Styles) ... */
+</style>
+""", unsafe_allow_html=True)
 st.subheader("DataHarvest", divider="violet")
 st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
 st.markdown(':rainbow[**Supported Languages: English**]')
 expander = st.expander("**Important notes**")
-expander.write("""**Named Entities:** This DataHarvest web app predicts nine (9) labels: "person", "country", "city", "organization", "date", "time", "cardinal", "money", "position"
-
-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.
-
-**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""")
+expander.write("""**Named Entities:** This DataHarvest web app predicts nine (9) labels: "person", "country", "city", "organization", "date", "time", "cardinal", "money", "position"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.**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 DataHarvest web app on your website. Feel free to adjust the width and height values to fit your page.")
     code = '''
-    <iframe
-	src="https://aiecosystem-dataharvest.hf.space"
-	frameborder="0"
-	width="850"
-	height="450"
-    ></iframe>
-
+    <iframe	src="https://aiecosystem-dataharvest.hf.space"	frameborder="0"	width="850"	height="450"    ></iframe>
     '''
     st.code(code, language="html")
     st.text("")
@@ -62,8 +49,6 @@ if not comet_initialized:
 
 # --- Label Definitions ---
 labels = ["person", "country", "city", "organization", "date", "time", "cardinal", "money", "position"]
-# Corrected mapping dictionary
-# Create a mapping dictionary for labels to categories
 category_mapping = {
     "People": ["person", "organization", "position"],
     "Locations": ["country", "city"],
@@ -73,16 +58,23 @@ 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)
     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
 reverse_category_mapping = {label: category for category, label_list in category_mapping.items() for label in label_list}
 
+# --- Session State Initialization ---
+# This is the key fix. We use session state to control what is displayed.
+if 'show_results' not in st.session_state:
+    st.session_state.show_results = False
+if 'last_text' not in st.session_state:
+    st.session_state.last_text = ""
+if 'results_df' not in st.session_state:
+    st.session_state.results_df = pd.DataFrame()
+
 # --- Text Input and Clear Button ---
 word_limit = 200
 text = st.text_area(f"Type or paste your text below (max {word_limit} words), and then press Ctrl + Enter", height=250, key='my_text_area')
@@ -90,8 +82,10 @@ word_count = len(text.split())
 st.markdown(f"**Word count:** {word_count}/{word_limit}")
 
 def clear_text():
-    """Clears the text area."""
+    """Clears the text area and hides results."""
     st.session_state['my_text_area'] = ""
+    st.session_state.show_results = False
+    st.session_state.last_text = ""
 
 def remove_punctuation(text):
     """Removes punctuation from a string."""
@@ -104,118 +98,102 @@ st.button("Clear text", on_click=clear_text)
 if st.button("Results"):
     if not text.strip():
         st.warning("Please enter some text to extract entities.")
+        st.session_state.show_results = False
     elif word_count > word_limit:
         st.warning(f"Your text exceeds the {word_limit} word limit. Please shorten it to continue.")
+        st.session_state.show_results = False
     else:
+        st.session_state.show_results = True
+        st.session_state.last_text = text
         start_time = time.time()
-        # Call the new function to remove punctuation from the input text
-        cleaned_text = remove_punctuation(text)
         with st.spinner("Extracting entities...", show_time=True):
-            # Use the cleaned text for prediction
+            cleaned_text = remove_punctuation(text)
             entities = model.predict_entities(cleaned_text, labels)
             df = pd.DataFrame(entities)
+            st.session_state.results_df = df
             if not df.empty:
                 df['category'] = df['label'].map(reverse_category_mapping)
                 if comet_initialized:
-                    experiment = Experiment(
-                        api_key=COMET_API_KEY,
-                        workspace=COMET_WORKSPACE,
-                        project_name=COMET_PROJECT_NAME,
-                    )
+                    experiment = Experiment(api_key=COMET_API_KEY, workspace=COMET_WORKSPACE, project_name=COMET_PROJECT_NAME)
                     experiment.log_parameter("input_text", text)
                     experiment.log_table("predicted_entities", df)
-                st.subheader("Grouped Entities by Category", divider = "violet")
-                # Create tabs for each category
-                category_names = sorted(list(category_mapping.keys()))
-                category_tabs = st.tabs(category_names)
-                for i, category_name in enumerate(category_names):
-                    with category_tabs[i]:
-                        df_category_filtered = df[df['category'] == category_name]
-                        if not df_category_filtered.empty:
-                            st.dataframe(df_category_filtered.drop(columns=['category']), use_container_width=True)
-                        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']
-                    - **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']
-                    ''')
-                st.divider()
-                # Tree map
-                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))
-                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")
-                    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(
-                    )
-                    st.plotly_chart(fig_pie)
-                with col2:
-                    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
-                    )
-                    st.plotly_chart(fig_bar)
-                # Most Frequent Entities
-                st.subheader("Most Frequent Entities", divider="violet")
-                word_counts = df['text'].value_counts().reset_index()
-                word_counts.columns = ['Entity', 'Count']
-                repeating_entities = word_counts[word_counts['Count'] > 1]
-                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'},
-                    )
-                    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'],
-                        'Description': [
-                            'entity extracted from your text data',
-                            'label (tag) assigned to a given extracted entity',
-                            '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',
-                        ]
-                    }
-                )
-                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: red; border: 1px solid black; padding: 5px; color: white; }""",
-                ):
-                    st.download_button(
-                        label="Download results and glossary (zip)",
-                        data=buf.getvalue(),
-                        file_name="nlpblogs_results.zip",
-                        mime="application/zip",
-                    )
-                if comet_initialized:
-                    experiment.log_figure(figure=fig_treemap, figure_name="entity_treemap_categories")
                     experiment.end()
             
-                # Corrected placement for time calculation and display
                 end_time = time.time()
                 elapsed_time = end_time - start_time
-                st.text("")
-                st.text("")
-                st.info(f"Results processed in **{elapsed_time:.2f} seconds**.")
-            else: # If df is empty
-                st.warning("No entities were found in the provided text.")
+                st.session_state.elapsed_time = elapsed_time
+
+# Display results if the state variable is True
+if st.session_state.show_results:
+    df = st.session_state.results_df
+    if not df.empty:
+        st.subheader("Grouped Entities by Category", divider = "violet")
+        category_names = sorted(list(category_mapping.keys()))
+        category_tabs = st.tabs(category_names)
+        for i, category_name in enumerate(category_names):
+            with category_tabs[i]:
+                df_category_filtered = df[df['category'] == category_name]
+                if not df_category_filtered.empty:
+                    st.dataframe(df_category_filtered.drop(columns=['category']), use_container_width=True)
+                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']
+            - **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']
+            ''')
+        st.divider()
+
+        # Tree map
+        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))
+        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")
+            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')
+            st.plotly_chart(fig_pie)
+        with col2:
+            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')
+            st.plotly_chart(fig_bar)
+
+        # Most Frequent Entities
+        st.subheader("Most Frequent Entities", divider="violet")
+        word_counts = df['text'].value_counts().reset_index()
+        word_counts.columns = ['Entity', 'Count']
+        repeating_entities = word_counts[word_counts['Count'] > 1]
+        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'})
+            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'],
+                                'Description': ['entity extracted from your text data', 'label (tag) assigned to a given extracted entity', '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']})
+        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: red; border: 1px solid black; padding: 5px; color: white; }""",):
+            st.download_button(label="Download results and glossary (zip)", data=buf.getvalue(), file_name="nlpblogs_results.zip", mime="application/zip")
+        
+        st.text("")
+        st.text("")
+        st.info(f"Results processed in **{st.session_state.elapsed_time:.2f} seconds**.")
+    else: # If df is empty after the button click
+        st.warning("No entities were found in the provided text.")