main.py

@@ -17,7 +17,6 @@ from services.geometry_detector import attach_polygon_geometry_auto
 from database.connection import engine
 from database.models import Base
 import time
-from datetime import datetime, timedelta
 import pathlib
 from fastapi.middleware.cors import CORSMiddleware
 
@@ -29,7 +28,7 @@ from sqlalchemy import text
 
 UPLOAD_FOLDER.mkdir(parents=True, exist_ok=True)
 
-apiVersion = "2.1.3"
+apiVersion = "2.1.0"
 app = FastAPI(
     title="ETL Geo Upload Service",
     version=apiVersion,
@@ -204,19 +203,17 @@ def process_data(df: pd.DataFrame, ext: str):
 
 
 
+
 from datetime import datetime
 @app.get("/status", tags=["System"])
 async def server_status():
-    utc_time = datetime.utcnow()
-    wib_time = utc_time + timedelta(hours=7)
-    formatted_time = wib_time.strftime("%d-%m-%Y %H:%M:%S")
     response = {
         "status": "success",
         "message": "Server is running smoothly ✅",
         "data": {
             "service": "upload_automation",
             "status_code": 200,
-            "timestamp": f"{formatted_time} WIB"
+            "timestamp": datetime.utcnow().isoformat() + "Z",
         },
         "meta": {
             "version": apiVersion,
@@ -228,7 +225,7 @@ async def server_status():
 
 
 @app.post("/upload")
-async def upload_file(file: UploadFile = File(...), page: Optional[str] = Form(""), sheet: Optional[str] = Form("")):
+async def upload_file(file: UploadFile = File(...), page: Optional[str] = Form("")):
     fname = file.filename
     ext = os.path.splitext(fname)[1].lower()
     contents = await file.read()
@@ -245,7 +242,7 @@ async def upload_file(file: UploadFile = File(...), page: Optional[str] = Form("
         print('ext', ext)
 
         if ext == ".csv":
-            df = read_csv(str(tmp_path), sheet)
+            df = read_csv(str(tmp_path))
         elif ext == ".xlsx":
             df = read_csv(str(tmp_path))
         elif ext == ".pdf":

services/geometry_detector.py

@@ -68,8 +68,6 @@ def normalize_name(name: str, level: str = None):
     name = name.strip()
     if not name:
         return None
-    
-    name = re.sub(r'\s*\([^)]*\)\s*', '', name)
 
     raw = name.lower()
     raw = re.sub(r'^(desa|kelurahan|kel|dusun|kampung)\s+', '', raw)
@@ -119,6 +117,7 @@ def normalize_name(name: str, level: str = None):
 
 
 
+
 def is_geom_empty(g):
     """True jika geometry None, NaN, atau geometry Shapely kosong."""
     if g is None:
@@ -135,7 +134,7 @@ def is_geom_empty(g):
 
 import math
 
-def normalize_lon(val, is_lat=False):
+def normalize_dynamic(val, is_lat=False):
     if pd.isna(val):
         return None
     try:
@@ -195,7 +194,7 @@ def detect_and_build_geometry(df: pd.DataFrame, master_polygons: gpd.GeoDataFram
         df[lat_col] = pd.to_numeric(df[lat_col], errors='coerce')
         df[lon_col] = pd.to_numeric(df[lon_col], errors='coerce')
 
-        df[lon_col] = df[lon_col].apply(lambda x: normalize_lon(x, is_lat=False))
+        df[lon_col] = df[lon_col].apply(lambda x: normalize_dynamic(x, is_lat=False))
         df[lat_col] = df[lat_col].apply(normalize_lat)
 
         gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df[lon_col], df[lat_col]), crs="EPSG:4326")

services/reader_csv.py

@@ -117,149 +117,57 @@ def detect_delimiter(path, sample_size=2048):
                 return delim
         return ','
 
-# def read_csv(path: str):
-#     ext = os.path.splitext(path)[1].lower()
-
-#     try:
-#         if ext in ['.csv']:
-#             # === Baca file CSV ===
-#             header_line = detect_header_line(path)
-#             delimiter = detect_delimiter(path)
-#             print(f"[INFO] Detected header line: {header_line + 1}, delimiter: '{delimiter}'")
-
-#             df = pd.read_csv(path, header=header_line, sep=delimiter, encoding='utf-8', low_memory=False, thousands=',')
-
-#         elif ext in ['.xlsx', '.xls']:
-#             # === Baca file Excel ===
-#             print(f"[INFO] Membaca file Excel: {os.path.basename(path)}")
-#             xls = pd.ExcelFile(path)
-
-#             print(f"[INFO] Ditemukan {len(xls.sheet_names)} sheet: {xls.sheet_names}")
-
-#             # Evaluasi tiap sheet untuk mencari yang paling relevan
-#             best_sheet = None
-#             best_score = -1
-#             best_df = None
-
-#             for sheet_name in xls.sheet_names:
-#                 try:
-#                     df = pd.read_excel(xls, sheet_name=sheet_name, header=0, dtype=str)
-#                     df = df.dropna(how='all').dropna(axis=1, how='all')
-
-#                     if len(df) == 0 or len(df.columns) < 2:
-#                         continue
-
-#                     # hitung "skor relevansi"
-#                     text_ratio = df.applymap(lambda x: isinstance(x, str)).sum().sum() / (df.size or 1)
-#                     row_score = len(df)
-#                     score = (row_score * 0.7) + (text_ratio * 100)
-
-#                     if score > best_score:
-#                         best_score = score
-#                         best_sheet = sheet_name
-#                         best_df = df
-
-#                 except Exception as e:
-#                     print(f"[WARN] Gagal membaca sheet {sheet_name}: {e}")
-#                     continue
-
-#             if best_df is not None:
-#                 print(f"[INFO] Sheet terpilih: '{best_sheet}' dengan skor {best_score:.2f}")
-#                 df = best_df
-#             else:
-#                 raise ValueError("Tidak ada sheet valid yang dapat dibaca.")
-
-#             # Konversi tipe numerik jika ada
-#             for col in df.columns:
-#                 if df[col].astype(str).str.replace(',', '', regex=False).str.match(r'^-?\d+(\.\d+)?$').any():
-#                     df[col] = df[col].astype(str).str.replace(',', '', regex=False)
-#                     df[col] = pd.to_numeric(df[col], errors='ignore')
-
-#         else:
-#             raise ValueError("Format file tidak dikenali (hanya .csv, .xlsx, .xls)")
-
-#     except Exception as e:
-#         print(f"[WARN] Gagal membaca file ({e}), fallback ke default reader.")
-#         df = pd.read_csv(path, encoding='utf-8', low_memory=False, thousands=',')
-
-#     # Bersihkan kolom dan baris kosong
-#     df = df.loc[:, ~df.columns.astype(str).str.contains('^Unnamed')]
-#     df.columns = [str(c).strip() for c in df.columns]
-#     df = df.dropna(how='all')
-
-#     return df
-
-
-
-
-
-def read_csv(path: str, sheet: str = None):
+def read_csv(path: str):
     ext = os.path.splitext(path)[1].lower()
 
     try:
-        if ext in ['.csv']:
+        if ext in ['.csv', '.txt']:
             # === Baca file CSV ===
             header_line = detect_header_line(path)
             delimiter = detect_delimiter(path)
             print(f"[INFO] Detected header line: {header_line + 1}, delimiter: '{delimiter}'")
 
-            df = pd.read_csv(
-                path,
-                header=header_line,
-                sep=delimiter,
-                encoding='utf-8',
-                low_memory=False,
-                thousands=','
-            )
+            df = pd.read_csv(path, header=header_line, sep=delimiter, encoding='utf-8', low_memory=False, thousands=',')
 
         elif ext in ['.xlsx', '.xls']:
             # === Baca file Excel ===
             print(f"[INFO] Membaca file Excel: {os.path.basename(path)}")
             xls = pd.ExcelFile(path)
+
             print(f"[INFO] Ditemukan {len(xls.sheet_names)} sheet: {xls.sheet_names}")
 
-            # === Jika user memberikan nama sheet ===
-            if sheet:
-                if sheet not in xls.sheet_names:
-                    raise ValueError(f"Sheet '{sheet}' tidak ditemukan dalam file {os.path.basename(path)}")
-                print(f"[INFO] Membaca sheet yang ditentukan: '{sheet}'")
-                df = pd.read_excel(xls, sheet_name=sheet, header=0, dtype=str)
-                df = df.dropna(how='all').dropna(axis=1, how='all')
+            # Evaluasi tiap sheet untuk mencari yang paling relevan
+            best_sheet = None
+            best_score = -1
+            best_df = None
 
-            else:
-                # === Auto-detect sheet terbaik ===
-                print("[INFO] Tidak ada sheet yang ditentukan, mencari sheet paling relevan...")
-                best_sheet = None
-                best_score = -1
-                best_df = None
+            for sheet_name in xls.sheet_names:
+                try:
+                    df = pd.read_excel(xls, sheet_name=sheet_name, header=0, dtype=str)
+                    df = df.dropna(how='all').dropna(axis=1, how='all')
 
-                for sheet_name in xls.sheet_names:
-                    try:
-                        temp_df = pd.read_excel(xls, sheet_name=sheet_name, header=0, dtype=str)
-                        temp_df = temp_df.dropna(how='all').dropna(axis=1, how='all')
-
-                        if len(temp_df) == 0 or len(temp_df.columns) < 2:
-                            continue
-
-                        # hitung skor relevansi
-                        text_ratio = temp_df.applymap(lambda x: isinstance(x, str)).sum().sum() / (temp_df.size or 1)
-                        row_score = len(temp_df)
-                        score = (row_score * 0.7) + (text_ratio * 100)
-
-                        if score > best_score:
-                            best_score = score
-                            best_sheet = sheet_name
-                            best_df = temp_df
-
-                    except Exception as e:
-                        print(f"[WARN] Gagal membaca sheet {sheet_name}: {e}")
+                    if len(df) == 0 or len(df.columns) < 2:
                         continue
 
-                if best_df is not None:
-                    print(f"[INFO] Sheet terpilih: '{best_sheet}' dengan skor {best_score:.2f}")
-                    df = best_df
-                else:
-                    raise ValueError("Tidak ada sheet valid yang dapat dibaca.")
+                    # hitung "skor relevansi"
+                    text_ratio = df.applymap(lambda x: isinstance(x, str)).sum().sum() / (df.size or 1)
+                    row_score = len(df)
+                    score = (row_score * 0.7) + (text_ratio * 100)
+
+                    if score > best_score:
+                        best_score = score
+                        best_sheet = sheet_name
+                        best_df = df
+
+                except Exception as e:
+                    print(f"[WARN] Gagal membaca sheet {sheet_name}: {e}")
+                    continue
+
+            if best_df is not None:
+                print(f"[INFO] Sheet terpilih: '{best_sheet}' dengan skor {best_score:.2f}")
+                df = best_df
+            else:
+                raise ValueError("Tidak ada sheet valid yang dapat dibaca.")
 
             # Konversi tipe numerik jika ada
             for col in df.columns:
@@ -268,7 +176,7 @@ def read_csv(path: str, sheet: str = None):
                     df[col] = pd.to_numeric(df[col], errors='ignore')
 
         else:
-            raise ValueError("Format file tidak dikenali (hanya .csv, .xlsx, .xls)")
+            raise ValueError("Format file tidak dikenali (hanya .csv, .txt, .xlsx, .xls)")
 
     except Exception as e:
         print(f"[WARN] Gagal membaca file ({e}), fallback ke default reader.")
@@ -280,4 +188,3 @@ def read_csv(path: str, sheet: str = None):
     df = df.dropna(how='all')
 
     return df
-