Update app.py

app.py

@@ -265,33 +265,26 @@ def analyze_competitors(row, co, country_code):
         our_content = fetch_content(our_url, query)
         our_score = calculate_relevance_score(our_content, query, co)
     
-    # Add our URL to the results if it's not already there
     if not any(r['is_our_url'] for r in results):
         results.append({
-            'Position': row['position'],  # Use the position from the main table
+            'Position': len(results) + 1,
             'URL': our_url,
             'Score': our_score,
             'is_our_url': True
         })
     
-    # Create DataFrame
-    results_df = pd.DataFrame(results)
-    results_df['Position'] = results_df['Position'].astype(float)  # Convert to float for proper sorting
-    
     # Sort results by position
-    results_df = results_df.sort_values('Position', ascending=True).reset_index(drop=True)
+    results = sorted(results, key=lambda x: x['Position'])
     
-    # Update positions to be consecutive integers
-    results_df['Position'] = range(1, len(results_df) + 1)
+    # Create DataFrame
+    results_df = pd.DataFrame(results)
+    results_df['Position'] = results_df['Position'].astype(int)
     
     # Mark our URL
     results_df['URL'] = results_df.apply(
         lambda x: f"{x['URL']} (Our URL)" if x['is_our_url'] else x['URL'], axis=1
     )
     
-    # Format Score to 2 decimal points
-    results_df['Score'] = results_df['Score'].apply(lambda x: f"{x:.2f}")
-    
     # Keep only the columns we want to display
     results_df = results_df[['Position', 'URL', 'Score']]
     
@@ -301,7 +294,7 @@ def show_competitor_analysis(row, co, country_code):
     if st.button("Check Competitors", key=f"comp_{row['page']}"):
         st.write(f"Competitor Analysis for: {row['query']}")
         with st.spinner('Analyzing competitors...'):
-            results_df = analyze_competitors(row, co, country_code)
+            results_df = analyze_competitors(row, co, country_code=country_code)
             
             # Display the Markdown table
             st.markdown(results_df.to_markdown(index=False), unsafe_allow_html=True)
@@ -671,7 +664,7 @@ def show_tabular_data(df, co, country_code):
         cols[4].write(row.impressions)
         cols[5].write(f"{row.ctr:.2%}")
         cols[6].write(f"{row.position:.1f}")
-        cols[7].write(f"{row.relevancy_score:.2f}" if not pd.isna(row.relevancy_score) and row.relevancy_score != 0 else "N/A")
+        cols[7].write(f"{row.relevancy_score:.4f}" if not pd.isna(row.relevancy_score) and row.relevancy_score != 0 else "N/A")
         
         # Competitors column
         if not pd.isna(row.relevancy_score) and row.relevancy_score != 0:
@@ -691,12 +684,15 @@ 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 Position in ascending order (should already be sorted, but just in case)
+                    # Sort the results by Position in ascending order
                     results_df = results_df.sort_values('Position', ascending=True).reset_index(drop=True)
                     
-                    # Ensure our URL's score matches the main table and format to 2 decimal points
+                    # Update the Position for our URL
                     our_url_mask = results_df['URL'].str.contains('Our URL')
-                    results_df.loc[our_url_mask, 'Score'] = f"{row.relevancy_score:.2f}"
+                    results_df.loc[our_url_mask, 'Position'] = row.position
+                    
+                    # Ensure our URL's score matches the main table
+                    results_df.loc[our_url_mask, 'Score'] = row.relevancy_score
                     
                     # Create a custom style function to highlight only our URL's row
                     def highlight_our_url(row):