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Update app.py
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app.py
CHANGED
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@@ -431,8 +431,11 @@ def create_project_proposals(budget_cluster_df, problem_cluster_df, location_clu
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# prompt = f"Generate a solution oriented project proposal for the following:\n\nLocation: {location}\nProblem Domain: {problem_domain}\nProblems: {problems_summary}\n\nProject Proposal:"
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prompt = f"Generate a solution-oriented project proposal for the following public problem (only output the proposal):\n\n Geographical/Digital Location: {location}\nProblem Category: {problem_domain}\nProblems: {problems_summary}\n\nProject Proposal:"
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else:
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print(f"Skipping empty problem descriptions for location: {location}, problem domain: {problem_domain}")
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@@ -606,14 +609,23 @@ def process_excel(file):
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consoleMessage_and_Print("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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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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@@ -652,8 +664,8 @@ def process_excel(file):
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example_files = []
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example_files.append('#TaxDirection (Responses)_BasicExample.xlsx')
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# example_files.append('#TaxDirection (Responses)_UltimateExample.xlsx')
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# prompt = f"Generate a solution oriented project proposal for the following:\n\nLocation: {location}\nProblem Domain: {problem_domain}\nProblems: {problems_summary}\n\nProject Proposal:"
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prompt = f"Generate a solution-oriented project proposal for the following public problem (only output the proposal):\n\n Geographical/Digital Location: {location}\nProblem Category: {problem_domain}\nProblems: {problems_summary}\n\nProject Proposal:"
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proposal = generate_project_proposal(prompt)
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# Check if proposal is valid
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if isinstance(proposal, str) and proposal.strip(): # Valid string that's not empty
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proposals[(loc, prob)] = proposal
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else:
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print(f"Skipping empty problem descriptions for location: {location}, problem domain: {problem_domain}")
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consoleMessage_and_Print("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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### Convert project_proposals dictionary to DataFrame
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project_proposals_df = pd.DataFrame.from_dict(project_proposals, orient='index', columns=['Solutions Proposed'])
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project_proposals_df.index.names = ['Location_Cluster', 'Problem_Cluster']
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project_proposals_df.reset_index(inplace=True)
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consoleMessage_and_Print("Creating the Excel file.")
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output_filename = "OutPut_PPs.xlsx"
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with pd.ExcelWriter(output_filename) as writer:
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try:
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project_proposals_df.to_excel(writer, sheet_name='Project_Proposals', index=False)
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except Exception as e:
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consoleMessage_and_Print("Error during Project Proposal excelling at the end")
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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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example_files = []
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# example_files.append('#TaxDirection (Responses)_BasicExample.xlsx')
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example_files.append('#TaxDirection (Responses)_IntermediateExample.xlsx')
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# example_files.append('#TaxDirection (Responses)_UltimateExample.xlsx')
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