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poemsforaphrodite
commited on
Commit
•
858a793
1
Parent(s):
6b4ee7d
Update app.py
Browse files
app.py
CHANGED
@@ -484,8 +484,8 @@ def show_tabular_data(df, co):
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selected_indices = [i for i, selected in enumerate(st.session_state.selected_rows) if selected]
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with st.spinner('Calculating relevancy scores...'):
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for index in selected_indices:
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if pd.isna(df.
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df.
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st.success(f"Calculated relevancy scores for {len(selected_indices)} selected rows.")
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st.experimental_rerun()
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@@ -496,41 +496,41 @@ def show_tabular_data(df, co):
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col.write(f"**{header}**")
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# Display each row
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for i,
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cols = st.columns([0.5, 3, 2, 1, 1, 1, 1, 1, 1])
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# Checkbox for row selection
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cols[0].checkbox("", key=f"select_{
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on_change=lambda i
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[True if j ==
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# Truncate and make the URL clickable
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truncated_url = row
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cols[1].markdown(f"[{truncated_url}]({row
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cols[2].write(row
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cols[3].write(row
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cols[4].write(row
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cols[5].write(f"{row
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cols[6].write(f"{row
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cols[7].write(f"{row
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# Competitors column
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competitor_button = cols[8].button("Show", key=f"comp_{
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if competitor_button:
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st.write(f"Competitor Analysis for: {row
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with st.spinner('Analyzing competitors...'):
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results_df = analyze_competitors(row, co)
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# Sort the results by relevancy score in descending order
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results_df = results_df.sort_values('relevancy_score', ascending=False).reset_index(drop=True)
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# Find our page's rank
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our_rank = results_df.index[results_df['url'] == row
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if our_rank:
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our_rank = our_rank[0] + 1 # Adding 1 because index starts at 0
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total_results = len(results_df)
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our_score = results_df.loc[results_df['url'] == row
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st.dataframe(results_df)
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st.write(f"Our page ranks {our_rank} out of {total_results} in terms of relevancy score.")
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@@ -543,7 +543,7 @@ def show_tabular_data(df, co):
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elif our_rank > total_results / 2:
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st.warning("Your page's relevancy score is in the lower half of the results. Consider optimizing your content.")
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else:
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st.error(f"Our page '{row
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return df # Return the updated dataframe
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selected_indices = [i for i, selected in enumerate(st.session_state.selected_rows) if selected]
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with st.spinner('Calculating relevancy scores...'):
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for index in selected_indices:
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if pd.isna(df.iloc[index]['relevancy_score']) or df.iloc[index]['relevancy_score'] == 0:
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df.iloc[index, df.columns.get_loc('relevancy_score')] = calculate_single_relevancy(df.iloc[index])
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st.success(f"Calculated relevancy scores for {len(selected_indices)} selected rows.")
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st.experimental_rerun()
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col.write(f"**{header}**")
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# Display each row
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for i, row in enumerate(df.iloc[start_idx:end_idx].itertuples(), start=start_idx):
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cols = st.columns([0.5, 3, 2, 1, 1, 1, 1, 1, 1])
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# Checkbox for row selection
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cols[0].checkbox("", key=f"select_{i}", value=st.session_state.selected_rows[i],
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on_change=lambda idx=i: setattr(st.session_state, 'selected_rows',
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[True if j == idx else x for j, x in enumerate(st.session_state.selected_rows)]))
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# Truncate and make the URL clickable
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truncated_url = row.page[:30] + '...' if len(row.page) > 30 else row.page
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cols[1].markdown(f"[{truncated_url}]({row.page})")
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cols[2].write(row.query)
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cols[3].write(row.clicks)
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cols[4].write(row.impressions)
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cols[5].write(f"{row.ctr:.2%}")
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cols[6].write(f"{row.position:.1f}")
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cols[7].write(f"{row.relevancy_score:.4f}" if not pd.isna(row.relevancy_score) and row.relevancy_score != 0 else "N/A")
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# Competitors column
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competitor_button = cols[8].button("Show", key=f"comp_{i}", disabled=pd.isna(row.relevancy_score) or row.relevancy_score == 0)
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if competitor_button:
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st.write(f"Competitor Analysis for: {row.query}")
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with st.spinner('Analyzing competitors...'):
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results_df = analyze_competitors(row._asdict(), co)
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# Sort the results by relevancy score in descending order
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results_df = results_df.sort_values('relevancy_score', ascending=False).reset_index(drop=True)
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# Find our page's rank
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our_rank = results_df.index[results_df['url'] == row.page].tolist()
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if our_rank:
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our_rank = our_rank[0] + 1 # Adding 1 because index starts at 0
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total_results = len(results_df)
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our_score = results_df.loc[results_df['url'] == row.page, 'relevancy_score'].values[0]
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st.dataframe(results_df)
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st.write(f"Our page ranks {our_rank} out of {total_results} in terms of relevancy score.")
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elif our_rank > total_results / 2:
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st.warning("Your page's relevancy score is in the lower half of the results. Consider optimizing your content.")
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else:
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st.error(f"Our page '{row.page}' is not in the results. This indicates an error in fetching or processing the page.")
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return df # Return the updated dataframe
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