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upload client side MH and MH DC model commit

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pjpatil12
2025-12-14 18:04:03 +05:30
parent 1d476c4851
commit 14eac5b958
10 changed files with 266 additions and 117 deletions
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app/models/__pycache__/manhole_domestic_chamber_model.cpython-313.pyc
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app/models/__pycache__/manhole_excavation_model.cpython-313.pyc
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app/models/__pycache__/mh_dc_client_model.cpython-313.pyc Normal file
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app/models/__pycache__/mh_ex_client_model.cpython-313.pyc
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app/models/mh_dc_client_model.py
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@@ -13,29 +13,25 @@ class ManholeDomesticChamberClient(db.Model):
# Basic Fields
Location = db.Column(db.String(255))
Node_No = db.Column(db.String(100))
MH_TOP_LEVEL = db.Column(db.Float)
MH_IL_LEVEL = db.Column(db.Float)
Depth_of_MH = db.Column(db.Float)
# Excavation categories
d_0_to_0_75 = db.Column(db.Float)
d_1_06_to_1_65 = db.Column(db.Float)
d_1_06_to_1_65 = db.Column(db.Float)
d_1_66_to_2_15 = db.Column(db.Float)
d_2_16_to_2_65 = db.Column(db.Float)
d_2_66_to_3_15 = db.Column(db.Float)
d_3_16_to_3_65= db.Column(db.Float)
d_3_66_to_4_15 = db.Column(db.Float)
d_4_16_to_4_65 = db.Column(db.Float)
d_4_66_to_5_15 = db.Column(db.Float)
d_5_16_to_5_65 = db.Column(db.Float)
d_5_66_to_6_15 = db.Column(db.Float)
d_6_16_to_6_65 = db.Column(db.Float)
d_6_66_to_7_15 = db.Column(db.Float)
d_7_16_to_7_65 = db.Column(db.Float)
d_7_66_to_8_15 = db.Column(db.Float)
d_8_16_to_8_65 = db.Column(db.Float)
d_8_66_to_9_15 = db.Column(db.Float)
d_9_16_to_9_65 = db.Column(db.Float)
# Excavation categories
d_0_to_1_5 = db.Column(db.Float)
d_1_5_to_2_0 = db.Column(db.Float)
d_2_0_to_2_5 = db.Column(db.Float)
d_2_5_to_3_0 = db.Column(db.Float)
d_3_0_to_3_5 = db.Column(db.Float)
d_3_5_to_4_0 = db.Column(db.Float)
d_4_0_to_4_5= db.Column(db.Float)
d_4_5_to_5_0 = db.Column(db.Float)
d_5_0_to_5_5 = db.Column(db.Float)
d_5_5_to_6_0 = db.Column(db.Float)
d_6_0_to_6_5 = db.Column(db.Float)
Domestic_Chambers = db.Column(db.Float)

29
app/models/mh_ex_client_model.py
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@@ -14,25 +14,32 @@ class ManholeExcavationClient(db.Model):
Location = db.Column(db.String(255))
MH_NO = db.Column(db.String(100))
GL_m = db.Column(db.Float)
Upto_IL_Depth = db.Column(db.Float)
MH_Invert_Level = db.Column(db.Float)
MH_Top_Level = db.Column(db.Float)
Ex_Level = db.Column(db.Float)
Cutting_Depth = db.Column(db.Float)
MH_Depth = db.Column(db.Float)
ID_of_MH_m = db.Column(db.Float)
Ex_Dia_of_Manhole = db.Column(db.Float)
Dia_of_MH_Cutting = db.Column(db.Float)
Area_of_Manhole = db.Column(db.Float)
# Excavation categories
Marshi_Muddy_Slushy_0_to_1_5 = db.Column(db.Float)
Marshi_Muddy_Slushy_1_5_to_3_0 = db.Column(db.Float)
Marshi_Muddy_Slushy_3_0_to_4_5 = db.Column(db.Float)
Soft_Murum_0_to_1_5 = db.Column(db.Float)
Soft_Murum_1_5_to_3_0 = db.Column(db.Float)
Soft_Murum_3_0_to_4_5 = db.Column(db.Float)
Hard_Murum_0_to_1_5 = db.Column(db.Float)
Hard_Murum_1_5_to_3_0 = db.Column(db.Float)
Hard_Murum_3_0_to_4_5 = db.Column(db.Float)
Soft_Rock_0_to_1_5 = db.Column(db.Float)
Soft_Rock_1_5_to_3_0 = db.Column(db.Float)
Soft_Murum_3_0_to_4_5 = db.Column(db.Float)
Hard_Rock_0_to_1_5 = db.Column(db.Float)
Hard_Rock_1_5_to_3_0 = db.Column(db.Float)
@@ -41,18 +48,24 @@ class ManholeExcavationClient(db.Model):
Hard_Rock_6_0_to_7_5 = db.Column(db.Float)
# Totals
Marshi_Muddy_Slushy_0_to_1_5_total = db.Column(db.Float)
Marshi_Muddy_Slushy_1_5_to_3_0_total = db.Column(db.Float)
Marshi_Muddy_Slushy_3_0_to_4_5_total = db.Column(db.Float)
Soft_Murum_0_to_1_5_total = db.Column(db.Float)
Soft_Murum_1_5_to_3_0_total = db.Column(db.Float)
Soft_Murum_3_0_to_4_5_total = db.Column(db.Float)
Hard_Murum_0_to_1_5_total = db.Column(db.Float)
Hard_Murum_1_5_and_above_total = db.Column(db.Float)
Hard_Murum_1_5_to_3_0_total = db.Column(db.Float)
Hard_Murum_3_0_to_4_5_total = db.Column(db.Float)
Soft_Rock_0_to_1_5_total = db.Column(db.Float)
Soft_Rock_1_5_and_above_total = db.Column(db.Float)
Soft_Rock_1_5_to_3_0_total = db.Column(db.Float)
Soft_Rock_3_0_to_4_5_total = db.Column(db.Float)
Hard_Rock_0_to_1_5_total = db.Column(db.Float)
Hard_Rock_1_5_and_above_total = db.Column(db.Float)
Hard_Rock_1_5_to_3_0_total = db.Column(db.Float)
Hard_Rock_3_0_to_4_5_total = db.Column(db.Float)
Hard_Rock_4_5_to_6_0_total = db.Column(db.Float)
Hard_Rock_6_0_to_7_5_total = db.Column(db.Float)

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app/routes/__pycache__/generate_comparison_report.cpython-313.pyc
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app/routes/generate_comparison_report.py
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@@ -1,15 +1,18 @@
from flask import Blueprint, render_template, request, send_file, flash
from app.models.subcontractor_model import Subcontractor
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 import db
import pandas as pd
import io
generate_report_bp = Blueprint("generate_report", __name__, url_prefix="/report")
generate_report_bp = Blueprint("generate_report", __name__, url_prefix="/report")
@generate_report_bp.route("/comparison_report", methods=["GET", "POST"])
@@ -21,21 +24,26 @@ def comparison_report():
if not subcontractor_id:
flash("Please select a subcontractor.", "danger")
return render_template("generate_comparison_report.html", subcontractors=subcontractors)
return render_template(
"generate_comparison_report.html",
subcontractors=subcontractors
)
subcontractor = Subcontractor.query.get_or_404(subcontractor_id)
# Fetch data
contractor_rows = TrenchExcavation.query.filter_by(subcontractor_id=subcontractor_id).all()
client_rows = TrenchExcavationClient.query.filter_by(subcontractor_id=subcontractor_id).all()
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 = []
diff_rows = []
for row1, row2 in zip(client_rows, contractor_rows):
total1 = (
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) +
@@ -55,7 +63,8 @@ def comparison_report():
(row1.Hard_Rock_6_0_to_7_5_total or 0)
)
total2 = (
# -------- 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) +
@@ -69,54 +78,132 @@ def comparison_report():
(row2.Hard_Rock_6_0_to_7_5_total or 0)
)
diff = total1 - total2
diff = client_total - contractor_total
# ---- store for excel ----
diff_rows.append({
# -------- COMBINED ROW ----------
row_data = {
"Location": row1.Location,
"Node No": row1.MH_NO,
"Client Sum": round(total1, 2),
"Subcontractor Sum": round(total2, 2),
"Diff": round(diff, 2)
})
"MH No": row1.MH_NO,
}
# optional console print
# 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"Location : {row1.Location} | "
f"MH_NO : {row1.MH_NO} | "
f"Client : {total1} | "
f"Sub : {total2} | "
f"Diff : {diff}"
f"{row1.Location} | {row1.MH_NO} | "
f"Client={client_total} | Sub={contractor_total} | Diff={diff}"
)
# -------- Tr.Ex model ----------
df_tr_ex = pd.DataFrame(combined_rows_tr_ex)
# manhole ex
# 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_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}"
# )
# Convert to DataFrame
df_contractor = pd.DataFrame([r.__dict__ for r in contractor_rows])
df_client = pd.DataFrame([r.__dict__ for r in client_rows])
df_diff = pd.DataFrame(diff_rows, columns=["Location", "Node No", "Client Sum", "Subcontractor Sum", "Diff"])
# Drop unwanted columns
drop_cols = ["id", "subcontractor_id", "created_at", "_sa_instance_state", "Remarks"]
df_contractor.drop(columns=drop_cols, errors="ignore", inplace=True)
df_client.drop(columns=drop_cols, errors="ignore", inplace=True)
# Convert to numeric
df_contractor = df_contractor.apply(pd.to_numeric, errors="coerce").fillna(0)
df_client = df_client.apply(pd.to_numeric, errors="coerce").fillna(0)
# EXPORT EXCEL
output = io.BytesIO()
file_name = f"{subcontractor.subcontractor_name}_Comparison_Report.xlsx"
with pd.ExcelWriter(output, engine="xlsxwriter") as writer:
df_contractor.to_excel(writer, index=False, sheet_name="Contractor_Data")
df_client.to_excel(writer, index=False, sheet_name="Client_Data")
df_diff.to_excel(writer, index=False, sheet_name="Diff")
df_tr_ex.to_excel(writer, index=False, sheet_name="Tr.Ex")
output.seek(0)
@@ -127,7 +214,7 @@ def comparison_report():
mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
)
return render_template("generate_comparison_report.html", subcontractors=subcontractors)
return render_template(
"generate_comparison_report.html",
subcontractors=subcontractors
)

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app/services/__pycache__/file_service.cpython-313.pyc
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app/services/file_service.py
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@@ -6,8 +6,11 @@ from app import db
from app.models.trench_excavation_model import TrenchExcavation
from app.models.manhole_excavation_model import ManholeExcavation
from app.models.manhole_domestic_chamber_model import ManholeDomesticChamber
from app.models.tr_ex_client_model import TrenchExcavationClient
from app.models.mh_ex_client_model import ManholeExcavationClient
from app.models.mh_dc_client_model import ManholeDomesticChamberClient
from app.utils.file_utils import ensure_upload_folder
@@ -56,14 +59,17 @@ class FileService:
if file_type == "manhole_domestic_chamber":
return self.process_manhole_domestic_chamber(df, subcontractor_id)
# Trench Excavation save (client)
# Tr Ex save (client)
if file_type =="tr_ex_client":
return self.client_trench_excavation(df, subcontractor_id)
# Manhole Excavation save (client)
# if file_type =="mh_ex_client":
# return self.client_manhole_excavation(df, subcontractor_id)
# Mh Ex save (client)
if file_type =="mh_ex_client":
return self.client_manhole_excavation(df, subcontractor_id)
# Mh and Dc save (client)
if file_type == "mh_dc_client":
return self.client_manhole_domestic_chamber(df, subcontractor_id)
return True, "File uploaded successfully."
@@ -212,7 +218,7 @@ class FileService:
# ---------------------- client ----------------------------------
# Trench Excavation save method (TrenchExcavationClient model)
# Tr Ex save method (TrenchExcavationClient model)
def client_trench_excavation(self, df, subcontractor_id):
df.columns = [str(c).strip() for c in df.columns]
# If the sheet has merged cells -> forward fill Location
@@ -256,49 +262,96 @@ class FileService:
return False, f"Clinnt Tr Ex Save Failed: {e}"
# Manhole Excavation save method (ManholeExcavationClient model)
# def client_manhole_excavation(self, df, subcontractor_id):
# # Clean column names (strip whitespace)
# df.columns = [str(c).strip() for c in df.columns]
# # If the sheet has merged cells -> forward fill Location
# if "Location" in df.columns:
# df["Location"] = df["Location"].ffill()
# Mh Ex save method (ManholeExcavationClient model)
def client_manhole_excavation(self, df, subcontractor_id):
# Clean column names (strip whitespace)
df.columns = [str(c).strip() for c in df.columns]
# If the sheet has merged cells -> forward fill Location
if "Location" in df.columns:
df["Location"] = df["Location"].ffill()
# # REMOVE empty rows
# df = df.dropna(how="all")
# # Identify missing location rows before insert
# missing_loc = df[df["Location"].isna() | (df["Location"].astype(str).str.strip() == "")]
# if not missing_loc.empty:
# return False, f"Error: Some rows have empty Location. Rows: {missing_loc.index.tolist()}"
# REMOVE empty rows
df = df.dropna(how="all")
# Identify missing location rows before insert
missing_loc = df[df["Location"].isna() | (df["Location"].astype(str).str.strip() == "")]
if not missing_loc.empty:
return False, f"Error: Some rows have empty Location. Rows: {missing_loc.index.tolist()}"
# saved_count = 0
saved_count = 0
# try:
# for index, row in df.iterrows():
# record_data = {}
# # Insert only fields that exist in model
# for col in df.columns:
# if hasattr(ManholeExcavationClient, col):
# value = row[col]
try:
for index, row in df.iterrows():
record_data = {}
# Insert only fields that exist in model
for col in df.columns:
if hasattr(ManholeExcavationClient, col):
value = row[col]
# # Normalize empty values
# if pd.isna(value) or str(value).strip() in ["", "-", "—", "nan", "NaN"]:
# value = None
# Normalize empty values
if pd.isna(value) or str(value).strip() in ["", "-", "—", "nan", "NaN"]:
value = None
# record_data[col] = value
record_data[col] = value
# record = ManholeExcavationClient(
# subcontractor_id=subcontractor_id,
# **record_data
# )
record = ManholeExcavationClient(
subcontractor_id=subcontractor_id,
**record_data
)
# db.session.add(record)
# saved_count += 1
db.session.add(record)
saved_count += 1
# db.session.commit()
# return True, f" Client Mh Ex. data saved successfully. Total rows: {saved_count}"
db.session.commit()
return True, f" Client Mh Ex. data saved successfully. Total rows: {saved_count}"
# except Exception as e:
# db.session.rollback()
# return False, f"Client Mh Ex. Save Failed: {e}"
except Exception as e:
db.session.rollback()
return False, f"Client Mh Ex. Save Failed: {e}"
# Mh and Dc save method (ManholeDomesticChamberClient model)
def client_manhole_domestic_chamber(self, df, subcontractor_id):
# Clean column names (strip whitespace)
df.columns = [str(c).strip() for c in df.columns]
# If the sheet has merged cells -> forward fill Location
if "Location" in df.columns:
df["Location"] = df["Location"].ffill()
# REMOVE empty rows
df = df.dropna(how="all")
# Identify missing location rows before insert
missing_loc = df[df["Location"].isna() | (df["Location"].astype(str).str.strip() == "")]
if not missing_loc.empty:
return False, f"Error: Some rows have empty Location. Rows: {missing_loc.index.tolist()}"
saved_count = 0
try:
for index, row in df.iterrows():
record_data = {}
# Insert only fields that exist in model
for col in df.columns:
if hasattr(ManholeDomesticChamberClient, col):
value = row[col]
# Normalize empty values
if pd.isna(value) or str(value).strip() in ["", "-", "—", "nan", "NaN"]:
value = None
record_data[col] = value
record = ManholeDomesticChamberClient(
subcontractor_id=subcontractor_id,
**record_data
)
db.session.add(record)
saved_count += 1
db.session.commit()
return True, f"Mh and Dc data saved successfully. Total rows: {saved_count}"
except Exception as e:
db.session.rollback()
return False, f"Mh and Dc data Save Failed: {e}"