from flask import Blueprint, render_template, request, send_file, flash from app.models.subcontractor_model import Subcontractor from app.models.manhole_excavation_model import ManholeExcavation from app.models.trench_excavation_model import TrenchExcavation from app.models.manhole_domestic_chamber_model import ManholeDomesticChamber from app.models.mh_ex_client_model import ManholeExcavationClient from app.models.tr_ex_client_model import TrenchExcavationClient from app.models.mh_dc_client_model import ManholeDomesticChamberClient from app import db import pandas as pd import io file_report_bp = Blueprint("file_report", __name__, url_prefix="/file") # get report by contractor id and dowanload @file_report_bp.route("/report", methods=["GET", "POST"]) def report_file(): 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("report.html", subcontractors=subcontractors) # Fetch subcontractor for file name subcontractor = Subcontractor.query.get(subcontractor_id) manhole_excavation = ManholeExcavation.query.filter_by(subcontractor_id=subcontractor_id).all() trench_excavation = TrenchExcavation.query.filter_by(subcontractor_id=subcontractor_id).all() domestic_chamber = ManholeDomesticChamber.query.filter_by(subcontractor_id=subcontractor_id).all() # Convert to DataFrame df_mh_exc = pd.DataFrame([m.__dict__ for m in manhole_excavation]) df_trench = pd.DataFrame([t.__dict__ for t in trench_excavation]) df_domestic = pd.DataFrame([d.__dict__ for d in domestic_chamber]) # Drop unnecessary SQLAlchemy fields drop_cols = ["id", "subcontractor_id", "created_at", "_sa_instance_state","Remarks"] df_mh_exc.drop(columns=drop_cols, errors="ignore", inplace=True) df_trench.drop(columns=drop_cols, errors="ignore", inplace=True) df_domestic.drop(columns=drop_cols, errors="ignore", inplace=True) mh_exc_columns = [ "Location", "MH_NO", "Upto_IL_Depth", "Cutting_Depth", "ID_of_MH_m", "Ex_Dia_of_Manhole", "Area_of_Manhole", "Soft_Murum_0_to_1_5", "Soft_Murum_1_5_to_3_0", "Soft_Murum_3_0_to_4_5", "Hard_Murum_0_to_1_5", "Hard_Murum_1_5_to_3_0", "Soft_Rock_0_to_1_5", "Soft_Rock_1_5_to_3_0", "Hard_Rock_0_to_1_5", "Hard_Rock_1_5_to_3_0", "Hard_Rock_3_0_to_4_5", "Hard_Rock_4_5_to_6_0", "Hard_Rock_6_0_to_7_5", "Soft_Murum_0_to_1_5_total", "Soft_Murum_1_5_to_3_0_total", "Soft_Murum_3_0_to_4_5_total", "Hard_Murum_0_to_1_5_total", "Hard_Murum_1_5_and_above_total", "Soft_Rock_0_to_1_5_total", "Soft_Rock_1_5_and_above_total", "Hard_Rock_0_to_1_5_total", "Hard_Rock_1_5_and_above_total", "Hard_Rock_3_0_to_4_5_total", "Hard_Rock_4_5_to_6_0_total", "Hard_Rock_6_0_to_7_5_total", "Remarks", "Total" ] trench_columns = [ "Location", "MH_NO", "CC_length", "Invert_Level", "MH_Top_Level", "Ground_Level", "ID_of_MH_m", "Actual_Trench_Length", "Pipe_Dia_mm", "Width_0_to_2_5", "Width_2_5_to_3_0", "Width_3_0_to_4_5", "Width_4_5_to_6_0", "Upto_IL_Depth", "Cutting_Depth", "Avg_Depth", "Soft_Murum_0_to_1_5", "Soft_Murum_1_5_to_3_0", "Soft_Murum_3_0_to_4_5", "Hard_Murum_0_to_1_5", "Hard_Murum_1_5_to_3_0", "Soft_Rock_0_to_1_5", "Soft_Rock_1_5_to_3_0", "Hard_Rock_0_to_1_5", "Hard_Rock_1_5_to_3_0", "Hard_Rock_3_0_to_4_5", "Hard_Rock_4_5_to_6_0", "Hard_Rock_6_0_to_7_5", "Soft_Murum_0_to_1_5_total", "Soft_Murum_1_5_to_3_0_total", "Soft_Murum_3_0_to_4_5_total", "Hard_Murum_0_to_1_5_total", "Hard_Murum_1_5_and_above_total", "Soft_Rock_0_to_1_5_total", "Soft_Rock_1_5_and_above_total", "Hard_Rock_0_to_1_5_total", "Hard_Rock_1_5_and_above_total", "Hard_Rock_3_0_to_4_5_total", "Hard_Rock_4_5_to_6_0_total", "Hard_Rock_6_0_to_7_5_total", "Remarks", "Total" ] domestic_columns = [ "Location", "Node_No", "Depth_of_MH", "d_0_to_0_75", "d_1_06_to_1_65", "d_1_66_to_2_15", "d_2_16_to_2_65", "d_2_66_to_3_15", "d_3_16_to_3_65", "d_3_66_to_4_15", "d_4_16_to_4_65", "d_4_66_to_5_15", "d_5_16_to_5_65", "d_5_66_to_6_15", "d_6_16_to_6_65", "d_6_66_to_7_15", "d_7_16_to_7_65", "d_7_66_to_8_15", "d_8_16_to_8_65", "d_8_66_to_9_15", "d_9_16_to_9_65", "Domestic_Chambers" ] # Reorder columns serial wise df_mh_exc = df_mh_exc.reindex(columns=mh_exc_columns, fill_value="") df_trench = df_trench.reindex(columns=trench_columns, fill_value="") df_domestic = df_domestic.reindex(columns=domestic_columns, fill_value="") # WRITE EXCEL FILE output = io.BytesIO() file_name = f"{subcontractor.subcontractor_name}_Report.xlsx" with pd.ExcelWriter(output, engine="xlsxwriter") as writer: df_trench.to_excel(writer, index=False, sheet_name="Tr.Ex.") df_mh_exc.to_excel(writer, index=False, sheet_name="MH.Ex.") df_domestic.to_excel(writer, index=False, sheet_name="MH & DC") output.seek(0) return send_file( output, download_name=file_name, as_attachment=True, mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" ) return render_template("report.html", subcontractors=subcontractors) @file_report_bp.route("/client_vs_subcont", methods=["GET", "POST"]) def client_vs_all_subcontractor(): if request.method == "POST": RA_Bill_No = request.form.get("RA_Bill_No") if not RA_Bill_No: flash("Please select RA Bill No.", "danger") return render_template("generate_comparison_client_vs_subcont.html") # CLIENT DATA (RA BILL WISE) client_trench = TrenchExcavationClient.query.filter_by(RA_Bill_No=RA_Bill_No).all() client_mh = ManholeExcavationClient.query.filter_by(RA_Bill_No=RA_Bill_No).all() client_dc = ManholeDomesticChamberClient.query.filter_by(RA_Bill_No=RA_Bill_No).all() df_client_tr = pd.DataFrame([c.__dict__ for c in client_trench]) df_client_mh = pd.DataFrame([c.__dict__ for c in client_mh]) df_client_dc = pd.DataFrame([c.__dict__ for c in client_dc]) # ALL SUBCONTRACTOR DATA sub_trench = TrenchExcavation.query.filter_by(RA_Bill_No=RA_Bill_No).all() sub_mh = ManholeExcavation.query.filter_by(RA_Bill_No=RA_Bill_No).all() sub_dc = ManholeDomesticChamber.query.filter_by(RA_Bill_No=RA_Bill_No).all() df_sub_tr = pd.DataFrame([s.__dict__ for s in sub_trench]) df_sub_mh = pd.DataFrame([s.__dict__ for s in sub_mh]) df_sub_dc = pd.DataFrame([s.__dict__ for s in sub_dc]) # CLEAN COLUMNS drop_cols = ["id","created_at","Remarks","_sa_instance_state"] for df in [df_client_tr, df_client_mh, df_client_dc,df_sub_tr, df_sub_mh, df_sub_dc]: if not df.empty: df.drop(columns=drop_cols, errors="ignore", inplace=True) # GROUP SUBCONTRACTOR DATA qty_cols = [ "Soft_Murum_0_to_1_5_total", "Soft_Murum_1_5_to_3_0_total", "Soft_Murum_3_0_to_4_5_total", "Hard_Murum_0_to_1_5_total", "Hard_Murum_1_5_and_above_total", "Soft_Rock_0_to_1_5_total", "Soft_Rock_1_5_and_above_total", "Hard_Rock_0_to_1_5_total", "Hard_Rock_1_5_to_3_0_total", "Hard_Rock_3_0_to_4_5_total", "Hard_Rock_4_5_to_6_0_total", "Hard_Rock_6_0_to_7_5_total" ] mh_dc_qty_cols = [ "d_0_to_0_75", "d_0_76_to_1_05", "d_1_06_to_1_65", "d_1_66_to_2_15", "d_2_16_to_2_65", "d_2_66_to_3_15", "d_3_16_to_3_65", "d_3_66_to_4_15", "d_4_16_to_4_65", "d_4_66_to_5_15", "d_5_16_to_5_65", "d_5_66_to_6_15", "d_6_16_to_6_65", "d_6_66_to_7_15", "d_7_16_to_7_65", "d_7_66_to_8_15", "d_8_16_to_8_65", "d_8_66_to_9_15", "d_9_16_to_9_65", "Domestic_Chambers" ] df_sub_tr_grp = (df_sub_tr.groupby(["Location", "MH_NO"], as_index=False)[qty_cols].sum()) df_sub_mh_grp = (df_sub_mh.groupby(["Location", "MH_NO"], as_index=False)[qty_cols].sum()) df_sub_dc_grp = (df_sub_dc.groupby(["Location", "Node_No"], as_index=False)[mh_dc_qty_cols].sum()) # MERGE CLIENT VS SUBCONTRACTOR df_tr_cmp = df_client_tr.merge( df_sub_tr_grp, on=["Location", "MH_NO"], how="left", suffixes=("_Client", "_Sub") ) df_mh_cmp = df_client_mh.merge( df_sub_mh_grp, on=["Location", "MH_NO"], how="left", suffixes=("_Client", "_Sub") ) if "MH_NO" in df_client_dc.columns: df_client_dc.rename(columns={"MH_NO": "Node_No"}, inplace=True) df_dc_cmp = df_client_dc.merge( df_sub_dc_grp, on=["Location", "Node_No"], how="left", suffixes=("_Client", "_Sub") ) # CALCULATE DIFFERENCE for col in qty_cols: if f"{col}_Client" in df_tr_cmp.columns: df_tr_cmp[f"{col}_Diff"] = ( df_tr_cmp[f"{col}_Client"].fillna(0) - df_tr_cmp[f"{col}_Sub"].fillna(0) ) print("Sum of df_tr_cmp::",df_tr_cmp) df_mh_cmp[f"{col}_Diff"] = ( df_mh_cmp[f"{col}_Client"].fillna(0) - df_mh_cmp[f"{col}_Sub"].fillna(0) ) print("Sum of df_mh_cmp::",df_mh_cmp) df_dc_cmp["Domestic_Chambers_Diff"] = ( df_dc_cmp["Domestic_Chambers_Client"].fillna(0) - df_dc_cmp["Domestic_Chambers_Sub"].fillna(0) ) # EXPORT EXCEL output = io.BytesIO() file_name = f"Client_vs_All_Subcontractor_RA_{RA_Bill_No}.xlsx" with pd.ExcelWriter(output, engine="xlsxwriter") as writer: df_tr_cmp.to_excel(writer, sheet_name="Tr.Ex", index=False) df_mh_cmp.to_excel(writer, sheet_name="Mh.Ex", index=False) df_dc_cmp.to_excel(writer, sheet_name="MH & DC", index=False) output.seek(0) return send_file( output, download_name=file_name, as_attachment=True, mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" ) return render_template("generate_comparison_client_vs_subcont.html")