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Update app.py
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app.py
CHANGED
@@ -462,7 +462,7 @@ def nlp_pipeline(original_df):
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# Create cluster dataframes
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budget_cluster_df, problem_cluster_df = create_cluster_dataframes(processed_df)
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return processed_df, budget_cluster_df
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# # # Generate project proposals
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# # location_clusters = dict(enumerate(processed_df['Location_Category_Words'].unique()))
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@@ -491,7 +491,7 @@ def process_excel(file):
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# Process the DataFrame
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console_messages.append("Processing the DataFrame...")
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# processed_df, budget_cluster_df, problem_cluster_df, project_proposals, location_clusters, problem_clusters = nlp_pipeline(df)
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processed_df
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output_filename = "OutPut_PPs.xlsx"
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with pd.ExcelWriter(output_filename) as writer:
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@@ -502,9 +502,23 @@ def process_excel(file):
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# project_proposals_df.to_excel(writer, sheet_name='Project_Proposals', index=False)
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budget_cluster_df.to_excel(writer, sheet_name='Financial_Weights')
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processed_df.to_excel(writer, sheet_name='Input_Processed', index=False)
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console_messages.append("Processing completed. Ready for download.")
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return output_filename, "\n".join(console_messages) # Return the processed DataFrame as Excel file
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# Create cluster dataframes
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budget_cluster_df, problem_cluster_df = create_cluster_dataframes(processed_df)
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return processed_df, budget_cluster_df, problem_cluster_df, location_clusters, problem_clusters
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# # # Generate project proposals
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# # location_clusters = dict(enumerate(processed_df['Location_Category_Words'].unique()))
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# Process the DataFrame
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console_messages.append("Processing the DataFrame...")
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# processed_df, budget_cluster_df, problem_cluster_df, project_proposals, location_clusters, problem_clusters = nlp_pipeline(df)
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processed_df, budget_cluster_df, problem_cluster_df, location_clusters, problem_clusters = nlp_pipeline(df)
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output_filename = "OutPut_PPs.xlsx"
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with pd.ExcelWriter(output_filename) as writer:
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# project_proposals_df.to_excel(writer, sheet_name='Project_Proposals', index=False)
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budget_cluster_df.to_excel(writer, sheet_name='Financial_Weights')
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problem_cluster_df.to_excel(writer, sheet_name='Problem_Descriptions')
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processed_df.to_excel(writer, sheet_name='Input_Processed', index=False)
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# Ensure location_clusters and problem_clusters are in DataFrame format
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if isinstance(location_clusters, pd.DataFrame):
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location_clusters.to_excel(writer, sheet_name='Location_Clusters', index=False)
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else:
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console_messages.append("Converting Location Clusters to df")
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pd.DataFrame(location_clusters).to_excel(writer, sheet_name='Location_Clusters', index=False)
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if isinstance(problem_clusters, pd.DataFrame):
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problem_clusters.to_excel(writer, sheet_name='Problem_Clusters', index=False)
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else:
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console_messages.append("Converting Problem Clusters to df")
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pd.DataFrame(problem_clusters).to_excel(writer, sheet_name='Problem_Clusters', index=False)
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console_messages.append("Processing completed. Ready for download.")
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return output_filename, "\n".join(console_messages) # Return the processed DataFrame as Excel file
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