Unified_Authentication_Platform/backend/app/services/import_service.py

import io
import pandas as pd
import random
import string
import re
from sqlalchemy.orm import Session
from sqlalchemy import or_
from app.models.user import User, UserStatus, UserRole
from app.models.import_log import ImportLog
from app.core.security import get_password_hash
# from app.core.utils import generate_password 
from typing import List, Dict, Any, Tuple
from datetime import datetime

# Helper to generate random password if utils doesn't have one
def _generate_random_password(length=8):
    chars = string.ascii_letters + string.digits
    return ''.join(random.choice(chars) for _ in range(length))

def _generate_random_string(length=6):
    chars = string.ascii_lowercase + string.digits
    return ''.join(random.choice(chars) for _ in range(length))

MOBILE_REGEX = r'^1[3-9]\d{9}$'

class UserImportService:
    
    @staticmethod
    def preview_excel(file_contents: bytes, filename: str) -> Dict[str, Any]:
        """
        Read first few rows of excel to let user map columns.
        """
        try:
            if filename.endswith('.csv'):
                df = pd.read_csv(io.BytesIO(file_contents), header=None, nrows=10)
            else:
                df = pd.read_excel(io.BytesIO(file_contents), header=None, nrows=10)
            
            # Convert to list of lists (handle NaN)
            data = df.fillna("").values.tolist()
            
            # Generate default headers like A, B, C... if no header? 
            # Or just return data and let frontend handle "Row 1 is header" logic.
            # Returning raw data is flexible.
            return {
                "preview_data": data,
                "filename": filename
            }
        except Exception as e:
            raise ValueError(f"Failed to parse file: {str(e)}")

    @staticmethod
    def process_import(
        db: Session, 
        file_contents: bytes, 
        filename: str, 
        start_row: int, # 1-based index from frontend
        mapping: Dict[str, int], # Field -> Column Index (0-based)
        user_id: int
    ) -> ImportLog:
        """
        Process the Excel file based on mapping.
        start_row: The row index where data starts (0-based in pandas logic? Frontend usually gives 1-based).
        mapping: {"mobile": 0, "name": 1, "english_name": 2}
        """
        
        # Load Data
        try:
            if filename.endswith('.csv'):
                df = pd.read_csv(io.BytesIO(file_contents), header=None)
            else:
                df = pd.read_excel(io.BytesIO(file_contents), header=None)
        except Exception as e:
            raise ValueError(f"Failed to read file: {str(e)}")

        # Adjust start_row to 0-based
        data_start_idx = start_row - 1
        if data_start_idx < 0 or data_start_idx >= len(df):
            raise ValueError("Invalid start row")

        # Slice dataframe
        df_data = df.iloc[data_start_idx:]
        
        success_count = 0
        fail_count = 0
        results = [] # Stores {row: 1, status: success/fail, msg: ..., account: ..., password: ...}

        # Cache existing mobiles to minimize DB hits (optional, but good for batch)
        # For simplicity and correctness with concurrent edits, we check DB row by row or handle IntegrityError.
        # But logic requires "Repeated mobile cannot be imported", so check first.

        for index, row in df_data.iterrows():
            row_num = index + 1 # 1-based row number for display
            
            try:
                with db.begin_nested():
                    # 1. Extract Data
                    mobile_col = mapping.get("mobile")
                    name_col = mapping.get("name")
                    en_name_col = mapping.get("english_name")
                    
                    mobile = str(row[mobile_col]).strip() if mobile_col is not None and pd.notna(row[mobile_col]) else None
                    name = str(row[name_col]).strip() if name_col is not None and pd.notna(row[name_col]) else None
                    en_name = str(row[en_name_col]).strip() if en_name_col is not None and pd.notna(row[en_name_col]) else None
                    
                    if not mobile:
                        raise ValueError("Mobile is required")
                    
                    # Validate Mobile Format
                    if not re.match(MOBILE_REGEX, mobile):
                        raise ValueError(f"Invalid mobile format: {mobile}")

                    # 2. Check Mobile Uniqueness
                    existing_user = db.query(User).filter(User.mobile == mobile).first()
                    if existing_user:
                        raise ValueError(f"Mobile {mobile} already exists")

                    # 3. Handle Name Duplicates (Auto-increment or Random)
                    if not name:
                        name = f"用户_{_generate_random_string(6)}"
                    final_name = UserImportService._resolve_duplicate_name(db, name)
                    
                    if not en_name:
                        en_name = f"user_{_generate_random_string(6)}"
                    final_en_name = UserImportService._resolve_duplicate_en_name(db, en_name)

                    # 4. Create User
                    plain_password = _generate_random_password()
                    password_hash = get_password_hash(plain_password)
                    
                    new_user = User(
                        mobile=mobile,
                        name=final_name,
                        english_name=final_en_name,
                        password_hash=password_hash,
                        status=UserStatus.ACTIVE,
                        role=UserRole.ORDINARY_USER
                    )
                    db.add(new_user)
                    db.flush()

                    success_count += 1
                    results.append({
                        "row": row_num,
                        "status": "success",
                        "mobile": mobile,
                        "name": final_name,
                        "english_name": final_en_name,
                        "initial_password": plain_password
                    })

            except Exception as e:
                fail_count += 1
                results.append({
                    "row": row_num,
                    "status": "fail",
                    "error": str(e)
                })

        # Commit successful rows
        db.commit()

        # Re-calc counts if commit failed totally? 
        # Let's refine the loop with begin_nested() for robust partial success.

        # Save Log
        log = ImportLog(
            filename=filename,
            total_count=success_count + fail_count,
            success_count=success_count,
            fail_count=fail_count,
            result_data=results, # Stores passwords! Warning to user about this.
            created_by=user_id
        )
        db.add(log)
        db.commit()
        db.refresh(log)
        
        return log

    @staticmethod
    def _resolve_duplicate_name(db: Session, base_name: str) -> str:
        # Simple heuristic: check base_name, then base_name1, base_name2...
        # Optimization: Query all matching `base_name%` and find gaps?
        # Simpler: Loop.
        
        # Check exact match first
        if not db.query(User).filter(User.name == base_name).first():
            return base_name
            
        i = 1
        while True:
            new_name = f"{base_name}{i}"
            if not db.query(User).filter(User.name == new_name).first():
                return new_name
            i += 1

    @staticmethod
    def _resolve_duplicate_en_name(db: Session, base_name: str) -> str:
        if not db.query(User).filter(User.english_name == base_name).first():
            return base_name
        i = 1
        while True:
            new_name = f"{base_name}{i}"
            if not db.query(User).filter(User.english_name == new_name).first():
                return new_name
            i += 1