Update src/streamlit_app.py

src/streamlit_app.py

@@ -139,7 +139,6 @@ st.subheader("Question-Answering", divider = "violet")
 # Replaced two columns with a single text input
 question_input = st.text_input("Ask wh-questions. **Wh-questions begin with what, when, where, who, whom, which, whose, why and how. We use them to ask for specific information.**")
 
-
 if st.button("Add Question"):
     if question_input:
         if question_input not in st.session_state.user_labels:
@@ -149,110 +148,89 @@ if st.button("Add Question"):
             st.warning("This question has already been added.")
     else:
         st.warning("Please enter a question.")
-        st.markdown("---")
-        
-st.subheader("Record of Questions", divider = "violet")
-
-if st.session_state.user_labels:
-    # Use enumerate to create a unique key for each item
-    for i, label in enumerate(st.session_state.user_labels):
-        col_list, col_delete = st.columns([0.9, 0.1])
-        with col_list:
-            st.write(f"- {label}", key=f"label_{i}")
-        with col_delete:
-            # Create a unique key for each button using the index
-            if st.button("Delete", key=f"delete_{i}"):
-                # Remove the label at the specific index
-                st.session_state.user_labels.pop(i)
-                # Rerun to update the UI
-                st.rerun()
-else:
-    st.info("No questions defined yet. Use the input above to add one.")
-
-def get_stable_color(label):
-    """Generates a consistent hexadecimal color from a given string."""
-    hash_object = hashlib.sha1(label.encode('utf-8'))
-    hex_dig = hash_object.hexdigest()
-    return '#' + hex_dig[:6]
-
-st.divider()
-
-# --- Main Processing Logic ---
-if st.button("Extract Answers"):
-    if not user_text.strip():
-        st.warning("Please enter some text to analyze.")
-    elif not st.session_state.user_labels:
-        st.warning("Please define at least one question.")
+            
+    st.markdown("---")
+    st.subheader("Record of Questions", divider="green")
+
+    if st.session_state.user_labels:
+        for i, label in enumerate(st.session_state.user_labels):
+            col_list, col_delete = st.columns([0.9, 0.1])
+            with col_list:
+                st.write(f"- {label}", key=f"label_{i}")
+            with col_delete:
+                if st.button("Delete", key=f"delete_{i}"):
+                    st.session_state.user_labels.pop(i)
+                    st.rerun()
     else:
-        if comet_initialized:
-            experiment = Experiment(
-                api_key=COMET_API_KEY,
-                workspace=COMET_WORKSPACE,
-                project_name=COMET_PROJECT_NAME
-            )
-            experiment.log_parameter("input_text_length", len(user_text))
-            experiment.log_parameter("defined_labels", st.session_state.user_labels)
-
-        start_time = time.time()
-        with st.spinner("Analyzing text...", show_time=True):
-            try:
-                entities = model.predict_entities(user_text, st.session_state.user_labels)
-                end_time = time.time()
-                elapsed_time = end_time - start_time
-                st.info(f"Processing took **{elapsed_time:.2f} seconds**.")
-
-                if entities:
-                    df1 = pd.DataFrame(entities)
-                    df2 = df1[['label', 'text', 'score']]
-                    df = df2.rename(columns={'label': 'question', 'text': 'answer'})
-                    
-                    st.subheader("Extracted Answers", divider = "violet")
-                    st.dataframe(df, use_container_width=True)
-
-                    # Create Tree map
-                    st.subheader("Tree map", divider="violet")
-                    all_labels = df['question'].unique()
-                    label_color_map = {label: get_stable_color(label) for label in all_labels}
-                    fig_treemap = px.treemap(
-                        df,
-                        path=[px.Constant("all"), 'question', 'answer'],
-                        values='score',
-                        color='question',
-                        color_discrete_map=label_color_map
-                    )
-                    fig_treemap.update_layout(margin=dict(t=50, l=25, r=25, b=25), paper_bgcolor='#F3E5F5', plot_bgcolor='#F3E5F5')
-                    st.plotly_chart(fig_treemap)
-
-                    
-                    csv_data = df.to_csv(index=False).encode('utf-8')
-                    with stylable_container(
-                        key="download_button",
-                        css_styles="""button { background-color: red; border: 1px solid black; padding: 5px; color: white; }""",
-                    ):
+        st.info("No questions defined yet. Use the input above to add one.")
+
+    st.divider()
+
+    if st.button("Extract Answers"):
+        if not user_text.strip():
+            st.warning("Please enter some text to analyze.")
+        elif not st.session_state.user_labels:
+            st.warning("Please define at least one question.")
+        else:
+            if comet_initialized:
+                experiment = Experiment(api_key=COMET_API_KEY, workspace=COMET_WORKSPACE, project_name=COMET_PROJECT_NAME)
+                experiment.log_parameter("input_text_length", len(user_text))
+                experiment.log_parameter("defined_labels", st.session_state.user_labels)
+
+            start_time = time.time()
+            with st.spinner("Analyzing text...", show_time=True):
+                try:
+                    entities = model_qa.predict_entities(user_text, st.session_state.user_labels)
+                    end_time = time.time()
+                    elapsed_time = end_time - start_time
+                    st.info(f"Processing took **{elapsed_time:.2f} seconds**.")
+
+                    if entities:
+                        df1 = pd.DataFrame(entities)
+                        df2 = df1[['label', 'text', 'score']]
+                        df = df2.rename(columns={'label': 'question', 'text': 'answer'})
+                        
+                        st.subheader("Extracted Answers", divider="green")
+                        st.dataframe(df, use_container_width=True)
+
+                        st.subheader("Tree map", divider="green")
+                        all_labels = df['question'].unique()
+                        label_color_map = {label: get_stable_color(label) for label in all_labels}
+                        fig_treemap = px.treemap(df, path=[px.Constant("all"), 'question', 'answer'], values='score', color='question', color_discrete_map=label_color_map)
+                        fig_treemap.update_layout(margin=dict(t=50, l=25, r=25, b=25), paper_bgcolor='#F3E5F5', plot_bgcolor='#F3E5F5')
+                        st.plotly_chart(fig_treemap)
+
+                        csv_data = df.to_csv(index=False).encode('utf-8')
                         st.download_button(
                             label="Download CSV",
                             data=csv_data,
-                            file_name="nlpblogs_results.csv",
+                            file_name="nlpblogs_questions_answers.csv",
                             mime="text/csv",
                         )
 
+                        if comet_initialized:
+                            experiment.log_metric("processing_time_seconds", elapsed_time)
+                            experiment.log_table("predicted_entities", df)
+                            experiment.log_figure(figure=fig_treemap, figure_name="entity_treemap")
+                            experiment.end()
+                    else:
+                        st.info("No answers were found in the text with the defined questions.")
+                        if comet_initialized:
+                            experiment.end()
+                except Exception as e:
+                    st.error(f"An error occurred during processing: {e}")
+                    st.write(f"Error details: {e}")
                     if comet_initialized:
-                        experiment.log_metric("processing_time_seconds", elapsed_time)
-                        experiment.log_table("predicted_entities", df)
-                        experiment.log_figure(figure=fig_treemap, figure_name="entity_treemap")
-                        
+                        experiment.log_text(f"Error: {e}")
                         experiment.end()
-                else:
-                    st.info("No answers were found in the text with the defined questions.")
-                    if comet_initialized:
-                        experiment.end()
-            except Exception as e:
-                st.error(f"An error occurred during processing: {e}")
-                st.write(f"Error details: {e}")
-                if comet_initialized:
-                    experiment.log_text(f"Error: {e}")
-                    experiment.end()
-                    
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