Solapur_12-12
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Swapnil9693
210
main.py
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210
main.py
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from flask import Flask, request, render_template
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from sqlalchemy import create_engine
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import pandas as pd
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app = Flask(__name__)
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# ---------------------- SQL CONNECTION -------------------------
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engine = create_engine("mysql+pymysql://root:tiger@localhost/solapur_db")
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# ---------------------- COLUMN MAPPING -------------------------
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contractor_map = {
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'Location': 'location',
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'ID of Manhole ': 'id_of_mh',
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'Ex. dia of MH Excavation ': 'ex_dia_excavation',
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'Area of MH Excavation ': 'area_excavation',
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'Soft Murum/\n0 to 1.5': 'sm_0_1_5',
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'Soft Murum/\n1.5 to 3.0': 'sm_1_5_3_0',
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'Soft Murum/\n3.0 to 4.0': 'sm_3_0_4_0',
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'Hard Murum/WBM\n0 to 1.5': 'hm_0_1_5',
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'Hard Murum/WBM\n1.5 to 3.0': 'hm_1_5_3_0',
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'Soft Rock\n0 to 1.5': 'sr_0_1_5',
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'Soft Rock\n1.5 to 3.0': 'sr_1_5_3_0',
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'Hard Rock\n0 to 1.5': 'hr_0_1_5',
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'Hard Rock\n1.5 to 3.0': 'hr_1_5_3_0',
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'Hard Rock\n3.0 to 4.50': 'hr_3_0_4_5',
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'Hard Rock\n4.5 to 6.0': 'hr_4_5_6_0',
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'Hard Rock\n6.0 To 7.5': 'hr_6_0_7_5',
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'Soft Murum/Soil\n0 to 1.5': 'sms_0_1_5',
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'Soft Murum/Soil\n1.5 to 3.0': 'sms_1_5_3_0',
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'Soft Murum/Soil\n3.0 to 4.0': 'sms_3_0_4_0',
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'Hard Murum/WBM\n0 to 1.5.1': 'hmwbm_0_1_5',
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'Hard Murum/WBM\n1.5 & Above': 'hmwbm_1_5_above',
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'Soft Rock\n0 to 1.5.1': 'sr2_0_1_5',
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'Soft Rock\n1.5 & Above': 'sr2_1_5_above',
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'Hard Rock\n0 to 1.5.1': 'hr2_0_1_5',
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'Hard Rock\n1.5 & Above': 'hr2_1_5_above',
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'Hard Rock\n3.0 to 4.50.1': 'hr2_3_0_4_5',
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'Hard Rock\n4.5 to 6': 'hr2_4_5_6',
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'Hard Rock\n6.0 To 7.5.1': 'hr2_6_0_7_5',
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'Total': 'total'
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}
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client_map = {
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'Location': 'location',
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' MH NOS': 'mh_nos',
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'GL (m)': 'gl',
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'MH Invert Level (m)': 'mh_invert_level',
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'MH Top Level (m)': 'mh_top_level',
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'Ex.Level (m)': 'ex_level',
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'MH Cutting Depth (m)': 'mh_cutting_depth',
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'MH Depth': 'mh_depth',
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'Inner dia of MH (m)': 'inner_dia',
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'Dia of MH Cutting (m)': 'cut_dia',
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'Area of Ex. MH (Sqm)': 'area_exc_mh',
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'Marshi / Muddy / Slushy\n 0 to \n1.5 m': 'mms_0_1_5',
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'Marshi / Muddy / Slushy\n1.5 to 3.0 m': 'mms_1_5_3_0',
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'Marshi / Muddy / Slushy\n3.0 to 4.5m': 'mms_3_0_4_5',
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' Soil / Soft Murum \n0 to 1.5 m': 'ssm_0_1_5',
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'Soil / Soft Murum \n1.5 to 3.0 m': 'ssm_1_5_3_0',
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'Soil / Soft Murum \n3.0 to 4.5m': 'ssm_3_0_4_5',
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' HM & WBM\n0 to 1.5 m': 'hmwbm_0_1_5',
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'HM & WBM\n1.5 to 3.0 m': 'hmwbm_1_5_3_0',
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'HM & WBM\n3.0 to 4.5m': 'hmwbm_3_0_4_5',
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' Soft Rock/Asphalt Rd \n0 to 1.5 m': 'srar_0_1_5',
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' Soft Rock/Asphalt Rd\n1.5 to 3.0 m': 'srar_1_5_3_0',
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' Soft Rock/Asphalt Rd\n3.0 to 4.5m': 'srar_3_0_4_5',
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' Hard Rock \n0 to 1.5 m': 'hr_0_1_5',
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'Hard Rock \n1.5 to 3.0 m': 'hr_1_5_3_0',
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'Hard Rock \n3.0 to 4.5 m': 'hr_3_0_4_5',
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'Hard Rock \n4.5 to 6 m': 'hr_4_5_6',
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'Hard Rock 6.0 to \n7.5m': 'hr_6_0_7_5',
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# SECOND SET
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'Marshi / Muddy / Slushy 0 to \n1.5 m': 'mms2_0_1_5',
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'Marshi / Muddy / Slushy\n1.5 to 3.0 m.1': 'mms2_1_5_3_0',
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'Marshi / Muddy / Slushy\n3.0 to 4.5m.1': 'mms2_3_0_4_5',
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' Soil / Soft Murum \n0 to 1.5 m': 'ssm2_0_1_5',
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'Soil / Soft Murum \n1.5 to 3.0 m': 'ssm2_1_5_3_0',
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'Soil / Soft Murum 3.0 to 4.5m': 'ssm2_3_0_4_5',
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' HM & WBM\n0 to 1.5 m.1': 'hmwbm2_0_1_5',
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'HM & WBM\n1.5 to 3.0 m.1': 'hmwbm2_1_5_3_0',
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'HM & WBM\n3.0 to 4.5m.1': 'hmwbm2_3_0_4_5',
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' Soft Rock/Asphalt Rd \n0 to 1.5 m.1': 'srar2_0_1_5',
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'Soft Rock/Asphalt Rd \n1.5 to 3.0 m': 'srar2_1_5_3_0',
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'Soft Rock/Asphalt Rd \n3.0 to 4.5m': 'srar2_3_0_4_5',
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' Hard Rock \n0 to 1.5 m.1': 'hr2_0_1_5',
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'Hard Rock\n1.5 to 3.0 m': 'hr2_1_5_3_0',
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'Hard Rock\n3.0 to 4.5 m': 'hr2_3_0_4_5',
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'Hard Rock\n4.5 to 6 m': 'hr2_4_5_6',
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'Hard Rock\n6.0 to \n7.5m': 'hr2_6_0_7_5'
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}
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@app.route("/", methods=["GET", "POST"])
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def index():
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return render_template("upload_excel.html")
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# ---------------------- UPLOAD ROUTE -------------------------
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@app.route("/upload_excel", methods=["GET", "POST"])
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def upload_excel():
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if request.method == "POST":
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file = request.files["file"]
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if not file:
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return "No file uploaded!", 400
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# Read both sheets
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df1 = pd.read_excel(file, sheet_name=0)
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df2 = pd.read_excel(file, sheet_name=1)
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print("\n---- SHEET 1 COLUMNS ----")
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print(df1.columns.tolist())
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print("\n---- SHEET 2 COLUMNS ----")
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print(df2.columns.tolist())
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# Rename using mapping
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df1 = df1.rename(columns=contractor_map)
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df2 = df2.rename(columns=client_map)
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# Keep only mapped (known) columns (ignore if some mapped names don't exist)
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df1 = df1[[c for c in contractor_map.values() if c in df1.columns]]
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df2 = df2[[c for c in client_map.values() if c in df2.columns]]
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# Fill empty numeric cells with 0
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df1 = df1.fillna(0)
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df2 = df2.fillna(0)
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# ------------------- DIFFERENCE CALCULATION (EXACT COLUMNS) -------------------
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# Excel formula: SUM(sheet2 AC:AS) - SUM(sheet1 Q:AB)
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# Map those ranges to the cleaned column names (these lists reflect that mapping)
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contractor_cols = [
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'sms_0_1_5','sms_1_5_3_0','sms_3_0_4_0',
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'hmwbm_0_1_5','hmwbm_1_5_above','sr2_0_1_5','sr2_1_5_above',
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'hr2_0_1_5','hr2_1_5_above','hr2_3_0_4_5','hr2_4_5_6','hr2_6_0_7_5'
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]
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client_cols = [
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'mms2_0_1_5','mms2_1_5_3_0','mms2_3_0_4_5',
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'ssm2_0_1_5','ssm2_1_5_3_0','ssm2_3_0_4_5',
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'hmwbm2_0_1_5','hmwbm2_1_5_3_0','hmwbm2_3_0_4_5',
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'srar2_0_1_5','srar2_1_5_3_0','srar2_3_0_4_5',
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'hr2_0_1_5','hr2_1_5_3_0','hr2_3_0_4_5','hr2_4_5_6','hr2_6_0_7_5'
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]
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# For safety, only use columns that exist in the DataFrames — missing ones treated as zeros
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contractor_present = [c for c in contractor_cols if c in df1.columns]
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client_present = [c for c in client_cols if c in df2.columns]
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# Add any missing columns with zeros so sums align (keeps row alignment)
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for c in contractor_cols:
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if c not in df1.columns:
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df1[c] = 0.0
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for c in client_cols:
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if c not in df2.columns:
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df2[c] = 0.0
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# Now compute difference exactly like your Excel formula
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df_difference = pd.DataFrame()
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# If location exists in df1 use it, else try df2, else blank
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if 'location' in df1.columns:
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df_difference["location"] = df1["location"]
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elif 'location' in df2.columns:
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df_difference["location"] = df2["location"]
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else:
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df_difference["location"] = ""
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print("Contractor columns being summed:", [c for c in contractor_cols if c in df1.columns])
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print("Client columns being summed:", [c for c in client_cols if c in df2.columns])
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print("Contractor sum per row:")
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print(df1[contractor_cols].sum(axis=1).round(2))
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print("Client sum per row:")
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print(df2[client_cols].sum(axis=1).round(2))
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df_difference["difference"] = df2[client_cols].sum(axis=1) - df1[contractor_cols].sum(axis=1)
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# ---------------- INSERT INTO DATABASE -----------------------
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df1.to_sql("mh_exc_contractor", con=engine, if_exists="append", index=False)
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df2.to_sql("mh_exc_client", con=engine, if_exists="append", index=False)
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df_difference.to_sql("mh_exc_difference", con=engine, if_exists="append", index=False)
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return "Excel uploaded and ALL tables updated successfully!"
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return render_template("upload_excel.html")
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# ---------------------- RUN FLASK APP -------------------------
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if __name__ == "__main__":
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app.run(host="0.0.0.0", port=5000, debug=True)
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