# app/services/file_service.py

import os
import pandas as pd
from werkzeug.utils import secure_filename
from app.config import Config
from app import db
from app.models.trench_excavation_model import TrenchExcavation
from app.utils.file_utils import ensure_upload_folder


class FileService:

    def allowed_file(self, filename):
        return "." in filename and filename.rsplit(".", 1)[1].lower() in Config.ALLOWED_EXTENSIONS

    def handle_file_upload(self, file, subcontractor_id, file_type):

        if not subcontractor_id:
            return False, "Please select subcontractor."
        if not file_type:
            return False, "Please select file type."
        if not file or file.filename == "":
            return False, "No file selected."
        if not self.allowed_file(file.filename):
            return False, "Invalid file type! Allowed: CSV, XLSX, XLS"

        ensure_upload_folder()

        folder = os.path.join(Config.UPLOAD_FOLDER, f"sub_{subcontractor_id}")
        os.makedirs(folder, exist_ok=True)

        filename = secure_filename(file.filename)
        filepath = os.path.join(folder, filename)
        file.save(filepath)

        try:
            df = pd.read_csv(filepath) if filename.endswith(".csv") else pd.read_excel(filepath)

            print("\n=== Uploaded File Preview ===")
            print(df.head())
            print("=============================\n")

            if file_type == "trench_excavation":
                return self.process_trench_excavation(df, subcontractor_id)

            return True, "File uploaded successfully."

        except Exception as e:
            return False, f"Processing failed: {e}"



    #   CLEAN & SAVE TRENCH EXCAVATION DATA
    def process_trench_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()}"

        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(TrenchExcavation, 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 = TrenchExcavation(
                    subcontractor_id=subcontractor_id,
                    **record_data
                )

                db.session.add(record)
                saved_count += 1

            db.session.commit()
            return True, f"Trench Excavation data saved successfully. Total rows: {saved_count}"

        except Exception as e:
            db.session.rollback()
            return False, f"Trench Excavation Save Failed: {e}"