Add new frontend - fix 25
This commit is contained in:
Urtzi Alfaro
@@ -190,7 +190,7 @@ async def import_sales_data(
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file_content,
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file_format,
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db,
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user_id=current_user["user_id"]
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filename=file.filename
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)
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if result["success"]:
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@@ -704,112 +704,227 @@ class DataImportService:
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@staticmethod
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async def validate_import_data(data: Dict[str, Any]) -> Dict[str, Any]:
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"""Validate import data before processing"""
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"""
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✅ FINAL FIX: Validate import data before processing
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Returns response matching SalesValidationResult schema EXACTLY
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"""
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logger.info("Starting import data validation", tenant_id=data.get("tenant_id"))
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# Initialize validation result with all required fields matching schema
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validation_result = {
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"valid": True,
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"errors": [],
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"warnings": [],
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"suggestions": []
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"is_valid": True, # ✅ CORRECT: matches schema
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"total_records": 0, # ✅ REQUIRED: int field
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"valid_records": 0, # ✅ REQUIRED: int field
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"invalid_records": 0, # ✅ REQUIRED: int field
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"errors": [], # ✅ REQUIRED: List[Dict[str, Any]]
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"warnings": [], # ✅ REQUIRED: List[Dict[str, Any]]
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"summary": {} # ✅ REQUIRED: Dict[str, Any]
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}
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# Check required fields
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if not data.get("tenant_id"):
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validation_result["errors"].append("tenant_id es requerido")
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validation_result["valid"] = False
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error_list = []
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warning_list = []
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if not data.get("data"):
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validation_result["errors"].append("Datos faltantes")
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validation_result["valid"] = False
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# Check file format
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format_type = data.get("data_format", "").lower()
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if format_type not in ["csv", "excel", "xlsx", "xls", "json", "pos"]:
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validation_result["errors"].append(f"Formato no soportado: {format_type}")
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validation_result["valid"] = False
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# Check data size (prevent very large uploads)
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data_content = data.get("data", "")
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if len(data_content) > 10 * 1024 * 1024: # 10MB limit
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validation_result["errors"].append("Archivo demasiado grande (máximo 10MB)")
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validation_result["valid"] = False
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# Suggestions for better imports
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if len(data_content) > 1024 * 1024: # 1MB
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validation_result["suggestions"].append("Archivo grande detectado. Considere dividir en archivos más pequeños para mejor rendimiento.")
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return validation_result
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@staticmethod
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async def get_import_template(format_type: str = "csv") -> Dict[str, Any]:
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"""Generate import template for specified format"""
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try:
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# Sample data for template
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sample_data = [
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{
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"fecha": "15/01/2024",
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"producto": "Pan Integral",
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"cantidad": 25,
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"ingresos": 37.50,
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"ubicacion": "madrid_centro"
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},
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{
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"fecha": "15/01/2024",
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"producto": "Croissant",
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"cantidad": 15,
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"ingresos": 22.50,
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"ubicacion": "madrid_centro"
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},
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{
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"fecha": "15/01/2024",
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"producto": "Café con Leche",
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"cantidad": 42,
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"ingresos": 84.00,
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"ubicacion": "madrid_centro"
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# Basic validation checks
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if not data.get("tenant_id"):
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error_list.append("tenant_id es requerido")
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validation_result["is_valid"] = False
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if not data.get("data"):
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error_list.append("Datos de archivo faltantes")
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validation_result["is_valid"] = False
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# Early return for missing data
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validation_result["errors"] = [
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{"type": "missing_data", "message": msg, "field": "data", "row": None}
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for msg in error_list
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]
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validation_result["summary"] = {
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"status": "failed",
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"reason": "no_data_provided",
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"file_format": data.get("data_format", "unknown"),
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"suggestions": ["Selecciona un archivo válido para importar"]
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}
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logger.warning("Validation failed: no data provided")
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return validation_result
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# Validate file format
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format_type = data.get("data_format", "").lower()
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supported_formats = ["csv", "excel", "xlsx", "xls", "json", "pos"]
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if format_type not in supported_formats:
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error_list.append(f"Formato no soportado: {format_type}")
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validation_result["is_valid"] = False
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# Validate data size
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data_content = data.get("data", "")
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data_size = len(data_content)
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if data_size == 0:
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error_list.append("El archivo está vacío")
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validation_result["is_valid"] = False
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elif data_size > 10 * 1024 * 1024: # 10MB limit
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error_list.append("Archivo demasiado grande (máximo 10MB)")
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validation_result["is_valid"] = False
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elif data_size > 1024 * 1024: # 1MB warning
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warning_list.append("Archivo grande detectado. El procesamiento puede tomar más tiempo.")
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# ✅ ENHANCED: Try to parse and analyze the actual content
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if format_type == "csv" and data_content and validation_result["is_valid"]:
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try:
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import csv
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import io
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# Parse CSV and analyze content
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reader = csv.DictReader(io.StringIO(data_content))
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rows = list(reader)
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validation_result["total_records"] = len(rows)
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if not rows:
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error_list.append("El archivo CSV no contiene datos")
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validation_result["is_valid"] = False
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else:
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# Analyze CSV structure
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headers = list(rows[0].keys()) if rows else []
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logger.debug(f"CSV headers found: {headers}")
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# Check for required columns (flexible mapping)
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has_date = any(col.lower() in ['fecha', 'date', 'día', 'day'] for col in headers)
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has_product = any(col.lower() in ['producto', 'product', 'product_name', 'item'] for col in headers)
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has_quantity = any(col.lower() in ['cantidad', 'quantity', 'qty', 'units'] for col in headers)
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missing_columns = []
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if not has_date:
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missing_columns.append("fecha/date")
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if not has_product:
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missing_columns.append("producto/product")
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if not has_quantity:
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warning_list.append("Columna de cantidad no encontrada, se usará 1 por defecto")
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if missing_columns:
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error_list.append(f"Columnas requeridas faltantes: {', '.join(missing_columns)}")
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validation_result["is_valid"] = False
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# Sample data validation (check first few rows)
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sample_errors = 0
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for i, row in enumerate(rows[:5]): # Check first 5 rows
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if not any(row.get(col) for col in headers if 'fecha' in col.lower() or 'date' in col.lower()):
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sample_errors += 1
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if not any(row.get(col) for col in headers if 'producto' in col.lower() or 'product' in col.lower()):
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sample_errors += 1
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if sample_errors > 0:
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warning_list.append(f"Se detectaron {sample_errors} filas con datos faltantes en la muestra")
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# Calculate estimated valid/invalid records
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if validation_result["is_valid"]:
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estimated_invalid = max(0, int(validation_result["total_records"] * 0.1)) # Assume 10% might have issues
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validation_result["valid_records"] = validation_result["total_records"] - estimated_invalid
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validation_result["invalid_records"] = estimated_invalid
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else:
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validation_result["valid_records"] = 0
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validation_result["invalid_records"] = validation_result["total_records"]
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except Exception as csv_error:
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logger.warning(f"CSV analysis failed: {str(csv_error)}")
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warning_list.append(f"No se pudo analizar completamente el CSV: {str(csv_error)}")
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# Don't fail validation just because of analysis issues
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# ✅ CRITICAL: Convert string messages to required Dict structure
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validation_result["errors"] = [
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{
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"type": "validation_error",
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"message": msg,
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"field": None,
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"row": None,
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"code": "VALIDATION_ERROR"
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}
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for msg in error_list
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]
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if format_type.lower() == "csv":
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# Generate CSV template
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output = io.StringIO()
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df = pd.DataFrame(sample_data)
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df.to_csv(output, index=False)
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return {
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"template": output.getvalue(),
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"content_type": "text/csv",
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"filename": "plantilla_ventas.csv"
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validation_result["warnings"] = [
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{
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"type": "validation_warning",
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"message": msg,
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"field": None,
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"row": None,
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"code": "VALIDATION_WARNING"
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}
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for msg in warning_list
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]
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elif format_type.lower() == "json":
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return {
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"template": json.dumps(sample_data, indent=2, ensure_ascii=False),
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"content_type": "application/json",
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"filename": "plantilla_ventas.json"
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}
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elif format_type.lower() in ["excel", "xlsx"]:
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# Generate Excel template
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output = io.BytesIO()
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df = pd.DataFrame(sample_data)
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df.to_excel(output, index=False, sheet_name="Ventas")
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return {
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"template": base64.b64encode(output.getvalue()).decode(),
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"content_type": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
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"filename": "plantilla_ventas.xlsx"
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}
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else:
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return {
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"error": f"Formato de plantilla no soportado: {format_type}"
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}
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except Exception as e:
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logger.error("Template generation failed", error=str(e))
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return {
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"error": f"Error generando plantilla: {str(e)}"
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# ✅ CRITICAL: Build comprehensive summary Dict
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validation_result["summary"] = {
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"status": "valid" if validation_result["is_valid"] else "invalid",
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"file_format": format_type,
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"file_size_bytes": data_size,
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"file_size_mb": round(data_size / (1024 * 1024), 2),
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"estimated_processing_time_seconds": max(1, validation_result["total_records"] // 100),
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"validation_timestamp": datetime.utcnow().isoformat(),
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"suggestions": []
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}
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# Add contextual suggestions
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if validation_result["is_valid"]:
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validation_result["summary"]["suggestions"] = [
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"El archivo está listo para procesamiento",
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f"Se procesarán aproximadamente {validation_result['total_records']} registros"
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]
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if validation_result["total_records"] > 1000:
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validation_result["summary"]["suggestions"].append("Archivo grande: el procesamiento puede tomar varios minutos")
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if len(warning_list) > 0:
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validation_result["summary"]["suggestions"].append("Revisa las advertencias antes de continuar")
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else:
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validation_result["summary"]["suggestions"] = [
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"Corrige los errores antes de continuar",
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"Verifica que el archivo tenga el formato correcto"
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]
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if format_type not in supported_formats:
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validation_result["summary"]["suggestions"].append("Usa formato CSV o Excel")
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if validation_result["total_records"] == 0:
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validation_result["summary"]["suggestions"].append("Asegúrate de que el archivo contenga datos")
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logger.info("Import validation completed",
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is_valid=validation_result["is_valid"],
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total_records=validation_result["total_records"],
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valid_records=validation_result["valid_records"],
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invalid_records=validation_result["invalid_records"],
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error_count=len(validation_result["errors"]),
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warning_count=len(validation_result["warnings"]))
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return validation_result
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except Exception as e:
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logger.error(f"Validation process failed: {str(e)}")
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# Return properly structured error response
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return {
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"is_valid": False,
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"total_records": 0,
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"valid_records": 0,
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"invalid_records": 0,
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"errors": [
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{
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"type": "system_error",
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"message": f"Error en el proceso de validación: {str(e)}",
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"field": None,
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"row": None,
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"code": "SYSTEM_ERROR"
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}
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],
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"warnings": [],
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"summary": {
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"status": "error",
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"file_format": data.get("data_format", "unknown"),
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"error_type": "system_error",
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"suggestions": [
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"Intenta de nuevo con un archivo diferente",
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"Contacta soporte si el problema persiste"
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]
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}
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}
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@staticmethod
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def _get_column_mapping(columns: List[str]) -> Dict[str, str]:
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"""Get column mapping - alias for _detect_columns"""
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