from flask import Blueprint, render_template, request, send_file, flash from app.models.subcontractor_model import Subcontractor from app.models.trench_excavation_model import TrenchExcavation from app.models.tr_ex_client_model import TrenchExcavationClient from app.models.manhole_excavation_model import ManholeExcavation from app.models.mh_ex_client_model import ManholeExcavationClient from app.models.manhole_domestic_chamber_model import ManholeDomesticChamber from app.models.mh_dc_client_model import ManholeDomesticChamberClient from app import db import pandas as pd import io generate_report_bp = Blueprint("generate_report", __name__, url_prefix="/report") @generate_report_bp.route("/comparison_report", methods=["GET", "POST"]) def comparison_report(): subcontractors = Subcontractor.query.all() if request.method == "POST": subcontractor_id = request.form.get("subcontractor_id") if not subcontractor_id: flash("Please select a subcontractor.", "danger") return render_template( "generate_comparison_report.html", subcontractors=subcontractors ) subcontractor = Subcontractor.query.get_or_404(subcontractor_id) # --------------------------- Tr.Ex model ----------------------------- contractor_tr_ex = TrenchExcavation.query.filter_by( subcontractor_id=subcontractor_id ).all() client_tr_ex = TrenchExcavationClient.query.filter_by( subcontractor_id=subcontractor_id ).all() combined_rows_tr_ex = [] for row1, row2 in zip(client_tr_ex, contractor_tr_ex): # -------- Tr.Ex CLIENT TOTAL ---------- client_total = ( (row1.Marshi_Muddy_Slushy_0_to_1_5_total or 0) + (row1.Marshi_Muddy_Slushy_1_5_to_3_0_total or 0) + (row1.Marshi_Muddy_Slushy_3_0_to_4_5_total or 0) + (row1.Soft_Murum_0_to_1_5_total or 0) + (row1.Soft_Murum_1_5_to_3_0_total or 0) + (row1.Soft_Murum_3_0_to_4_5_total or 0) + (row1.Hard_Murum_0_to_1_5_total or 0) + (row1.Hard_Murum_1_5_to_3_0_total or 0) + (row1.Hard_Murum_3_0_to_4_5_total or 0) + (row1.Soft_Rock_0_to_1_5_total or 0) + (row1.Soft_Rock_1_5_to_3_0_total or 0) + (row1.Soft_Rock_3_0_to_4_5_total or 0) + (row1.Hard_Rock_0_to_1_5_total or 0) + (row1.Hard_Rock_1_5_to_3_0_total or 0) + (row1.Hard_Rock_3_0_to_4_5_total or 0) + (row1.Hard_Rock_4_5_to_6_0_total or 0) + (row1.Hard_Rock_6_0_to_7_5_total or 0) ) # -------- SUBCONTRACTOR TOTAL ---------- contractor_total = ( (row2.Soft_Murum_0_to_1_5_total or 0) + (row2.Soft_Murum_1_5_to_3_0_total or 0) + (row2.Soft_Murum_3_0_to_4_5_total or 0) + (row2.Hard_Murum_0_to_1_5_total or 0) + (row2.Hard_Murum_1_5_and_above_total or 0) + (row2.Soft_Rock_0_to_1_5_total or 0) + (row2.Soft_Rock_1_5_and_above_total or 0) + (row2.Hard_Rock_0_to_1_5_total or 0) + (row2.Hard_Rock_1_5_and_above_total or 0) + (row2.Hard_Rock_4_5_to_6_0_total or 0) + (row2.Hard_Rock_6_0_to_7_5_total or 0) ) diff = client_total - contractor_total # -------- COMBINED ROW ---------- row_data = { "Location": row1.Location, "MH No": row1.MH_NO, } # Client columns for col, val in row1.__dict__.items(): if col.startswith("_") or col in ["id", "created_at", "subcontractor_id"]: continue row_data[f"Client_{col}"] = val row_data["Client_Total"] = round(client_total, 2) # Subcontractor columns for col, val in row2.__dict__.items(): if col.startswith("_") or col in ["id", "created_at", "subcontractor_id"]: continue row_data[f"Sub_{col}"] = val row_data["Sub_Total"] = round(contractor_total, 2) row_data["Diff"] = round(diff, 2) combined_rows_tr_ex.append(row_data) # Console log print( f"{row1.Location} | {row1.MH_NO} | " f"Client={client_total} | Sub={contractor_total} | Diff={diff}" ) df_tr_ex = pd.DataFrame(combined_rows_tr_ex) # --------------------------- Mh.Ex model ----------------------------- contractor_mh_ex = ManholeExcavation.query.filter_by( subcontractor_id=subcontractor_id ).all() client_mh_ex = ManholeExcavationClient.query.filter_by( subcontractor_id=subcontractor_id ).all() combined_rows_mh_ex = [] for row1, row2 in zip(client_mh_ex, contractor_mh_ex): # -------- Tr.Ex CLIENT TOTAL ---------- client_total = ( (row1.Marshi_Muddy_Slushy_0_to_1_5_total or 0) + (row1.Marshi_Muddy_Slushy_1_5_to_3_0_total or 0) + (row1.Marshi_Muddy_Slushy_3_0_to_4_5_total or 0) + (row1.Soft_Murum_0_to_1_5_total or 0) + (row1.Soft_Murum_1_5_to_3_0_total or 0) + (row1.Soft_Murum_3_0_to_4_5_total or 0) + (row1.Hard_Murum_0_to_1_5_total or 0) + (row1.Hard_Murum_1_5_to_3_0_total or 0) + (row1.Hard_Murum_3_0_to_4_5_total or 0) + (row1.Soft_Rock_0_to_1_5_total or 0) + (row1.Soft_Rock_1_5_to_3_0_total or 0) + (row1.Soft_Rock_3_0_to_4_5_total or 0) + (row1.Hard_Rock_0_to_1_5_total or 0) + (row1.Hard_Rock_1_5_to_3_0_total or 0) + (row1.Hard_Rock_3_0_to_4_5_total or 0) + (row1.Hard_Rock_4_5_to_6_0_total or 0) + (row1.Hard_Rock_6_0_to_7_5_total or 0) ) # -------- SUBCONTRACTOR TOTAL ---------- contractor_total = ( (row2.Soft_Murum_0_to_1_5_total or 0) + (row2.Soft_Murum_1_5_to_3_0_total or 0) + (row2.Soft_Murum_3_0_to_4_5_total or 0) + (row2.Hard_Murum_0_to_1_5_total or 0) + (row2.Hard_Murum_1_5_and_above_total or 0) + (row2.Soft_Rock_0_to_1_5_total or 0) + (row2.Soft_Rock_1_5_and_above_total or 0) + (row2.Hard_Rock_0_to_1_5_total or 0) + (row2.Hard_Rock_1_5_and_above_total or 0) + (row2.Hard_Rock_3_0_to_4_5_total or 0) + (row2.Hard_Rock_4_5_to_6_0_total or 0) + (row2.Hard_Rock_6_0_to_7_5_total or 0) ) diff = client_total - contractor_total # -------- COMBINED ROW ---------- row_data = { "Location": row1.Location, "MH No": row1.MH_NO, } # Client columns for col, val in row1.__dict__.items(): if col.startswith("_") or col in ["id", "created_at", "subcontractor_id"]: continue row_data[f"Client_{col}"] = val row_data["Client_Total"] = round(client_total, 2) # Subcontractor columns for col, val in row2.__dict__.items(): if col.startswith("_") or col in ["id", "created_at", "subcontractor_id"]: continue row_data[f"Sub_{col}"] = val row_data["Sub_Total"] = round(contractor_total, 2) row_data["Diff"] = round(diff, 2) combined_rows_mh_ex.append(row_data) # Console log print( f"{row1.Location} | {row1.MH_NO} | " f"Client={client_total} | Sub={contractor_total} | Diff={diff}" ) df_mh_ex = pd.DataFrame(combined_rows_mh_ex) # ---------------------------- MH dC Model -------------------------- contractor_mh_dc = ManholeDomesticChamber.query.filter_by( subcontractor_id=subcontractor_id ).all() client_mh_dc = ManholeDomesticChamberClient.query.filter_by( subcontractor_id=subcontractor_id ).all() combined_rows_mh_dc = [] for row1, row2 in zip(client_mh_dc, contractor_mh_dc): client_total = ( (row1.d_0_to_1_5 or 0) + (row1.d_1_5_to_2_0 or 0) + (row1.d_2_0_to_2_5 or 0) + (row1.d_2_5_to_3_0 or 0) + (row1.d_3_0_to_3_5 or 0) + (row1.d_3_5_to_4_0 or 0) + (row1.d_4_0_to_4_5 or 0) + (row1.d_4_5_to_5_0 or 0) + (row1.d_5_0_to_5_5 or 0) + (row1.d_5_5_to_6_0 or 0) + (row1.d_6_0_to_6_5 or 0) + (row1.Domestic_Chambers or 0) ) contractor_total = ( (row2.d_0_to_0_75 or 0) + (row2.d_0_76_to_1_05 or 0) + (row2.d_1_06_to_1_65 or 0) + (row2.d_1_66_to_2_15 or 0) + (row2.d_2_16_to_2_65 or 0) + (row2.d_2_66_to_3_15 or 0) + (row2.d_3_16_to_3_65 or 0) + (row2.d_3_66_to_4_15 or 0) + (row2.d_4_16_to_4_65 or 0) + (row2.d_4_66_to_5_15 or 0) + (row2.d_5_16_to_5_65 or 0) + (row2.d_5_66_to_6_15 or 0) + (row2.d_6_16_to_6_65 or 0) + (row2.d_6_66_to_7_15 or 0) + (row2.d_7_16_to_7_65 or 0) + (row2.d_7_66_to_8_15 or 0) + (row2.d_8_16_to_8_65 or 0) + (row2.d_8_66_to_9_15 or 0) + (row2.d_9_16_to_9_65 or 0) + (row2.Domestic_Chambers or 0) ) diff = client_total - contractor_total # -------- COMBINED ROW ---------- row_data = { "Location": row1.Location, "Node No": row1.Node_No, } # Client columns for col, val in row1.__dict__.items(): if col.startswith("_") or col in ["id", "created_at", "subcontractor_id"]: continue row_data[f"Client_{col}"] = val row_data["Client_Total"] = round(client_total, 2) # Subcontractor columns for col, val in row2.__dict__.items(): if col.startswith("_") or col in ["id", "created_at", "subcontractor_id"]: continue row_data[f"Sub_{col}"] = val row_data["Sub_Total"] = round(contractor_total, 2) row_data["Diff"] = round(diff, 2) combined_rows_mh_dc.append(row_data) # Console log print( f"{row1.Location} | {row1.Node_No} | " f"Client={client_total} | Sub={contractor_total} | Diff={diff}" ) df_mh_dc = pd.DataFrame(combined_rows_mh_dc) output = io.BytesIO() file_name = f"{subcontractor.subcontractor_name}_Comparison_Report.xlsx" with pd.ExcelWriter(output, engine="xlsxwriter") as writer: df_tr_ex.to_excel(writer, index=False, sheet_name="Tr.Ex") df_mh_ex.to_excel(writer,index=False,sheet_name="Mh.Ex") df_mh_dc.to_excel(writer,index=False,sheet_name="MH & DC") output.seek(0) return send_file( output, as_attachment=True, download_name=file_name, mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" ) return render_template( "generate_comparison_report.html", subcontractors=subcontractors )