Update app.py

app.py

@@ -475,52 +475,61 @@ def show_tabular_data(df, co):
     start_idx = (st.session_state.current_page - 1) * rows_per_page
     end_idx = start_idx + rows_per_page
     
-    # Prepare data for display
-    display_df = df.iloc[start_idx:end_idx].copy()
-    display_df['page'] = display_df['page'].apply(lambda x: x[:50] + '...' if len(x) > 50 else x)
-    display_df['ctr'] = display_df['ctr'].apply(lambda x: f"{x:.2%}")
-    display_df['position'] = display_df['position'].apply(lambda x: f"{x:.1f}")
-    display_df['relevancy_score'] = display_df['relevancy_score'].apply(lambda x: f"{x:.4f}" if pd.notnull(x) else "N/A")
-
-    # Display the table
-    st.table(display_df)
-
-    # Add calculate and show competitors buttons
-    for index, row in display_df.iterrows():
-        col1, col2 = st.columns(2)
-        with col1:
-            if pd.isna(row['relevancy_score']) or row['relevancy_score'] == "N/A":
-                if st.button("Calculate", key=f"calc_{index}"):
-                    with st.spinner('Calculating...'):
-                        score = calculate_single_relevancy(df.loc[index])
-                        df.at[index, 'relevancy_score'] = score
-                    st.experimental_rerun()
-        with col2:
-            if row['relevancy_score'] != "N/A":
-                if st.button("Show Competitors", key=f"comp_{index}"):
-                    st.write(f"Competitor Analysis for: {row['query']}")
-                    with st.spinner('Analyzing competitors...'):
-                        results_df = analyze_competitors(df.loc[index], co)
-                        results_df = results_df.sort_values('relevancy_score', ascending=False).reset_index(drop=True)
-                        
-                        our_rank = results_df.index[results_df['url'] == row['page']].tolist()
-                        if our_rank:
-                            our_rank = our_rank[0] + 1
-                            total_results = len(results_df)
-                            our_score = results_df.loc[results_df['url'] == row['page'], 'relevancy_score'].values[0]
-                            
-                            st.table(results_df)
-                            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}")
-                            
-                            if our_rank == 1:
-                                st.success("Your page has the highest relevancy score!")
-                            elif our_rank <= 3:
-                                st.info("Your page is among the top 3 most relevant results.")
-                            elif our_rank > total_results / 2:
-                                st.warning("Your page's relevancy score is in the lower half of the results. Consider optimizing your content.")
-                        else:
-                            st.error(f"Our page '{row['page']}' is not in the results. This indicates an error in fetching or processing the page.")
+    # Display column headers
+    cols = st.columns([3, 2, 1, 1, 1, 1, 1, 1])
+    headers = ['Page', 'Query', 'Clicks', 'Impressions', 'CTR', 'Position', 'Relevancy Score', 'Competitors']
+    for col, header in zip(cols, headers):
+        col.write(f"**{header}**")
+
+    # Display each row
+    for index, row in df.iloc[start_idx:end_idx].iterrows():
+        cols = st.columns([3, 2, 1, 1, 1, 1, 1, 1])
+        cols[0].write(row['page'])
+        cols[1].write(row['query'])
+        cols[2].write(row['clicks'])
+        cols[3].write(row['impressions'])
+        cols[4].write(f"{row['ctr']:.2%}")
+        cols[5].write(f"{row['position']:.1f}")
+        
+        # Relevancy Score column
+        if pd.isna(row['relevancy_score']) or row['relevancy_score'] == 0:
+            if cols[6].button("Calculate", key=f"calc_{index}"):
+                with st.spinner('Calculating...'):
+                    score = calculate_single_relevancy(row)
+                    df.at[index, 'relevancy_score'] = score
+                st.experimental_rerun()
+        else:
+            cols[6].write(f"{row['relevancy_score']:.4f}")
+        
+        # Competitors column
+        competitor_button = cols[7].button("Show Competitors", key=f"comp_{index}", disabled=pd.isna(row['relevancy_score']) or row['relevancy_score'] == 0)
+        if competitor_button:
+            st.write(f"Competitor Analysis for: {row['query']}")
+            with st.spinner('Analyzing competitors...'):
+                results_df = analyze_competitors(row, co)
+                
+                # Sort the results by relevancy score in descending order
+                results_df = results_df.sort_values('relevancy_score', ascending=False).reset_index(drop=True)
+                
+                # Find our page's rank
+                our_rank = results_df.index[results_df['url'] == row['page']].tolist()
+                if our_rank:
+                    our_rank = our_rank[0] + 1  # Adding 1 because index starts at 0
+                    total_results = len(results_df)
+                    our_score = results_df.loc[results_df['url'] == row['page'], 'relevancy_score'].values[0]
+                    
+                    st.dataframe(results_df)
+                    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}")
+                    
+                    if our_rank == 1:
+                        st.success("Your page has the highest relevancy score!")
+                    elif our_rank <= 3:
+                        st.info("Your page is among the top 3 most relevant results.")
+                    elif our_rank > total_results / 2:
+                        st.warning("Your page's relevancy score is in the lower half of the results. Consider optimizing your content.")
+                else:
+                    st.error(f"Our page '{row['page']}' is not in the results. This indicates an error in fetching or processing the page.")
 
     return df  # Return the updated dataframe