create project and create model and dashboard

app/services/file_service.py

@@ -0,0 +1,103 @@
+# 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}"