Update app.py

app.py

@@ -554,8 +554,19 @@ def show_tabular_data(df, co, country_code):
     if 'selected_rows' not in st.session_state or len(st.session_state.selected_rows) != len(df):
         st.session_state.selected_rows = [False] * len(df)
     
-    # Add a "Calculate Relevancy" button at the top
-    if st.button("Calculate Relevancy for Selected"):
+    # Add a "Calculate Relevancy" button at the top with custom styling
+    st.markdown(
+        """
+        <style>
+        .stButton > button {
+            background-color: #4CAF50;
+            color: white;
+        }
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+    if st.button("Click here to calculate relevancy for selected pages"):
         selected_indices = [i for i, selected in enumerate(st.session_state.selected_rows) if selected]
         with st.spinner('Calculating relevancy scores...'):
             for index in selected_indices:
@@ -565,14 +576,14 @@ def show_tabular_data(df, co, country_code):
         st.experimental_rerun()
     
     # Display column headers
-    cols = st.columns([0.5, 3, 2, 1, 1, 1, 1, 1, 1, 1])  # Added an extra column
-    headers = ['Select', 'Page', 'Query', 'Clicks', 'Impressions', 'CTR', 'Position', 'Relevancy Score', 'Competitors', 'Compare']
+    cols = st.columns([0.5, 3, 2, 1, 1, 1, 1, 1, 1])  # Removed the extra column for "Compare"
+    headers = ['Select', 'Page', 'Query', 'Clicks', 'Impressions', 'CTR', 'Position', 'Relevancy Score', 'Competitors']
     for col, header in zip(cols, headers):
         col.write(f"**{header}**")
 
     # Display each row
     for i, row in enumerate(df.iloc[start_idx:end_idx].itertuples(), start=start_idx):
-        cols = st.columns([0.5, 3, 2, 1, 1, 1, 1, 1, 1, 1])  # Added an extra column
+        cols = st.columns([0.5, 3, 2, 1, 1, 1, 1, 1, 1])  # Removed the extra column for "Compare"
         
         # Checkbox for row selection
         cols[0].checkbox("", key=f"select_{i}", value=st.session_state.selected_rows[i],
@@ -597,27 +608,32 @@ def show_tabular_data(df, co, country_code):
             with st.spinner('Analyzing competitors...'):
                 results_df = analyze_competitors(row._asdict(), co, country_code=country_code)
                 
-                # Sort the results by Score in descending order
-                results_df = results_df.sort_values('Score', ascending=False).reset_index(drop=True)
+                # Sort the results by Position in ascending order
+                results_df = results_df.sort_values('Position', ascending=True).reset_index(drop=True)
+                
+                # Update the Position for our URL
+                our_url_mask = results_df['URL'].str.contains('Our URL')
+                results_df.loc[our_url_mask, 'Position'] = row.position
+                
+                # Create a custom style function to highlight only our URL's row
+                def highlight_our_url(row):
+                    if 'Our URL' in row['URL']:
+                        return ['background-color: lightgreen'] * len(row)
+                    return [''] * len(row)
+                
+                # Apply the custom style and hide the index
+                styled_df = results_df.style.apply(highlight_our_url, axis=1).hide(axis="index")
                 
-                # Find our page's rank
-                our_rank = results_df.index[results_df['URL'].str.contains('Our URL')].tolist()
-                if our_rank:
-                    our_rank = our_rank[0] + 1  # Adding 1 because index starts at 0
+                # Display the styled DataFrame
+                st.markdown(styled_df.to_html(), unsafe_allow_html=True)
+                
+                # Extract our result for additional insights
+                our_result = results_df[results_df['URL'].str.contains('Our URL')]
+                
+                if not our_result.empty:
+                    our_rank = our_result['Position'].values[0]
                     total_results = len(results_df)
-                    our_score = results_df.loc[results_df['URL'].str.contains('Our URL'), 'Score'].values[0]
-                    
-                    # Create a custom style function to highlight only our URL's row
-                    def highlight_our_url(row):
-                        if 'Our URL' in row['URL']:
-                            return ['background-color: lightgreen'] * len(row)
-                        return [''] * len(row)
-                    
-                    # Apply the custom style and hide the index
-                    styled_df = results_df.style.apply(highlight_our_url, axis=1).hide(axis="index")
-                    
-                    # Display the styled DataFrame
-                    st.markdown(styled_df.to_html(), unsafe_allow_html=True)
+                    our_score = our_result['Score'].values[0]
                     
                     st.write(f"Our page ranks {our_rank} out of {total_results} in terms of relevancy score.")
                     st.write(f"Our relevancy score: {our_score:.4f}")
@@ -631,10 +647,20 @@ def show_tabular_data(df, co, country_code):
                 else:
                     st.error(f"Our page '{row.page}' is not in the results. This indicates an error in fetching or processing the page.")
 
-        # New button for content comparison
-        compare_button = cols[9].button("Compare", key=f"compare_{i}")
-        if compare_button:
-            compare_with_top_result(row._asdict(), co, country_code)
+                # Add "Compare" button at the bottom of the competitors table
+                st.markdown(
+                    """
+                    <style>
+                    .stButton > button {
+                        background-color: #008CBA;
+                        color: white;
+                    }
+                    </style>
+                    """,
+                    unsafe_allow_html=True
+                )
+                if st.button("Compare Your Relevancy Score to the Page In First Place", key=f"compare_{i}"):
+                    compare_with_top_result(row._asdict(), co, country_code)
 
     return df  # Return the updated dataframe