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