salakojoshua1234_gmail.com
1a4e539626
- Introduced `DatabaseOperations` class for managing batch results in the database. - Added functionality to create a batch results table and save batch processing results. - Updated API endpoints to log execution time and handle batch processing errors more effectively. - Improved response handling in analysis endpoints and added batch metadata to results. - Suppressed warnings and improved logging throughout the application.
598 lines
25 KiB
Python
598 lines
25 KiB
Python
"""
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FastAPI application for salary analytics.
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"""
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from fastapi import FastAPI, HTTPException, BackgroundTasks, UploadFile, File, Depends
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from fastapi.responses import FileResponse
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from typing import Optional, Dict, List, Union
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import os
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import socket
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import logging
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import pandas as pd
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import tempfile
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from datetime import datetime
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from sqlalchemy import text, Table, Column, Integer, String, Float, DateTime, MetaData
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import numpy as np
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import warnings
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import time
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from .main import SalaryAnalyticsPipeline
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from .config import OUTPUT_PATHS, TABLE_NAME, BATCH_RESULTS_TABLE
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from .data_loader import DataLoader
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from .salary_predictor import SalaryPredictor
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from .salary_earner_analyzer import SalaryEarnerAnalyzer
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from .db_operations import DatabaseOperations
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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# Suppress warnings
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warnings.filterwarnings('ignore', category=RuntimeWarning, module='numpy')
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pd.options.mode.chained_assignment = None
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app = FastAPI(
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title="Salary Analytics API",
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description="API for analyzing and predicting salary patterns from transaction data",
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version="1.0.0"
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)
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# Add CORS middleware
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"], # Allows all origins
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allow_credentials=True,
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allow_methods=["*"], # Allows all methods
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allow_headers=["*"], # Allows all headers
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)
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# Global pipeline instance
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pipeline = SalaryAnalyticsPipeline()
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# Global variables to store loaded data and models
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data_loader = None
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df = None
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salary_predictor = None
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salary_earner_analyzer = None
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class AnalysisResponse(BaseModel):
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"""Response model for analysis endpoints."""
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message: str
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data: Optional[Dict] = None
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file_path: Optional[str] = None
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class BatchResponse(BaseModel):
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"""Response model for batch processing."""
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batch_number: int
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total_batches: int
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processed_rows: int
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results_path: str
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message: str
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def check_data_loaded():
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"""Check if data is loaded before running analytics."""
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if pipeline.df is None:
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raise HTTPException(
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status_code=400,
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detail="No data loaded. Please load data first using the /load-data endpoint."
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)
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@app.on_event("startup")
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async def startup_event():
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"""Initialize the pipeline on startup."""
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try:
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logger.info("Initializing pipeline...")
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# Print network information
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hostname = socket.gethostname()
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ip_address = socket.gethostbyname(hostname)
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logger.info(f"Server running on hostname: {hostname}")
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logger.info(f"Server IP address: {ip_address}")
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logger.info(f"Server is accessible at:")
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logger.info(f"- http://localhost:8000")
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logger.info(f"- http://127.0.0.1:8000")
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logger.info(f"- http://{ip_address}:8000")
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logger.info("Pipeline initialized successfully")
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except Exception as e:
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logger.error(f"Error during startup: {str(e)}")
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raise
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@app.get("/")
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async def root():
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"""Root endpoint."""
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start_time = time.time()
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logger.info("Root endpoint accessed")
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response = {"message": "Welcome to Salary Analytics API"}
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logger.info(f"Root endpoint completed in {time.time() - start_time:.2f} seconds")
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return response
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@app.get("/health")
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async def health_check():
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"""Health check endpoint."""
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start_time = time.time()
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logger.info("Health check endpoint accessed")
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response = {"status": "healthy"}
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logger.info(f"Health check completed in {time.time() - start_time:.2f} seconds")
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return response
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@app.post("/analyze/keyword", response_model=AnalysisResponse)
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async def analyze_keyword():
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"""Run keyword-based salary transaction analysis."""
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start_time = time.time()
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try:
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check_data_loaded()
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logger.info("Starting keyword analysis...")
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data = pipeline.run_keyword_analysis()
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logger.info(f"Keyword analysis completed. Found {len(data)} matches")
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response = AnalysisResponse(
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message="Keyword analysis completed successfully",
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data={"count": len(data)}
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)
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logger.info(f"Keyword analysis endpoint completed in {time.time() - start_time:.2f} seconds")
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return response
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except Exception as e:
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logger.error(f"Error in keyword analysis: {str(e)}")
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logger.info(f"Keyword analysis endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/analyze/consistent-amount", response_model=AnalysisResponse)
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async def analyze_consistent_amount():
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"""Run consistent amount transaction analysis."""
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start_time = time.time()
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try:
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check_data_loaded()
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logger.info("Starting consistent amount analysis...")
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data = pipeline.run_consistent_amount_analysis()
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logger.info(f"Consistent amount analysis completed. Found {len(data)} matches")
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response = AnalysisResponse(
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message="Consistent amount analysis completed successfully",
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data={"count": len(data)}
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)
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logger.info(f"Consistent amount analysis endpoint completed in {time.time() - start_time:.2f} seconds")
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return response
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except Exception as e:
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logger.error(f"Error in consistent amount analysis: {str(e)}")
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logger.info(f"Consistent amount analysis endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/analyze/transaction-type", response_model=AnalysisResponse)
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async def analyze_transaction_type():
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"""Run transaction type analysis."""
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start_time = time.time()
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try:
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check_data_loaded()
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logger.info("Starting transaction type analysis...")
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data = pipeline.run_transaction_type_analysis()
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logger.info(f"Transaction type analysis completed. Found {len(data)} matches")
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response = AnalysisResponse(
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message="Transaction type analysis completed successfully",
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data={"count": len(data)}
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)
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logger.info(f"Transaction type analysis endpoint completed in {time.time() - start_time:.2f} seconds")
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return response
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except Exception as e:
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logger.error(f"Error in transaction type analysis: {str(e)}")
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logger.info(f"Transaction type analysis endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/generate/reports", response_model=AnalysisResponse)
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async def generate_reports(background_tasks: BackgroundTasks):
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"""Generate salary earner reports."""
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start_time = time.time()
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try:
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check_data_loaded()
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logger.info("Starting report generation...")
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reports = pipeline.generate_salary_earner_reports()
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logger.info("Reports generated successfully")
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response = AnalysisResponse(
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message="Reports generated successfully",
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data={
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"verified_salary_earners": len(reports['final_table']),
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"likely_salary_earners": len(reports['likely_salary_earner']),
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"high_earners": reports['total_high_earners']
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}
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)
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logger.info(f"Report generation endpoint completed in {time.time() - start_time:.2f} seconds")
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return response
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except Exception as e:
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logger.error(f"Error in report generation: {str(e)}")
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logger.info(f"Report generation endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/train/models", response_model=AnalysisResponse)
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async def train_models():
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"""Train salary prediction models."""
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start_time = time.time()
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try:
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check_data_loaded()
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logger.info("Starting model training...")
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pipeline.train_salary_prediction_models()
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logger.info("Models trained successfully")
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response = AnalysisResponse(
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message="Models trained successfully"
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)
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logger.info(f"Model training endpoint completed in {time.time() - start_time:.2f} seconds")
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return response
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except Exception as e:
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logger.error(f"Error in model training: {str(e)}")
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logger.info(f"Model training endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/download/{report_type}")
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async def download_report(report_type: str):
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"""Download generated reports."""
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start_time = time.time()
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try:
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check_data_loaded()
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logger.info(f"Attempting to download report: {report_type}")
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file_paths = {
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"high_earners": OUTPUT_PATHS["high_earner_details"],
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"likely_earners": OUTPUT_PATHS["likely_salary_earner"],
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"final_table": OUTPUT_PATHS["final_table"],
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"consistent_plot": OUTPUT_PATHS["consistent_earners_plot"],
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"inconsistent_plot": OUTPUT_PATHS["inconsistent_earners_plot"],
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"hypothesis_plot": OUTPUT_PATHS["hypothesis_overlap_plot"]
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}
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if report_type not in file_paths:
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logger.error(f"Report type not found: {report_type}")
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logger.info(f"Download endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=404, detail="Report type not found")
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file_path = file_paths[report_type]
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if not os.path.exists(file_path):
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logger.error(f"Report file not found: {file_path}")
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logger.info(f"Download endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=404, detail="Report file not found")
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logger.info(f"Successfully found report file: {file_path}")
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response = FileResponse(
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path=file_path,
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filename=os.path.basename(file_path),
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media_type="application/octet-stream"
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)
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logger.info(f"Download endpoint completed in {time.time() - start_time:.2f} seconds")
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return response
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except Exception as e:
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logger.error(f"Error downloading report: {str(e)}")
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logger.info(f"Download endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/run/pipeline", response_model=AnalysisResponse)
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async def run_full_pipeline():
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"""Run the complete salary analytics pipeline."""
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start_time = time.time()
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try:
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check_data_loaded()
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logger.info("Starting full pipeline...")
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success = pipeline.run_full_pipeline()
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if not success:
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logger.error("Pipeline failed")
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logger.info(f"Full pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=500, detail="Pipeline failed")
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logger.info("Pipeline completed successfully")
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response = AnalysisResponse(
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message="Pipeline completed successfully"
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)
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logger.info(f"Full pipeline endpoint completed in {time.time() - start_time:.2f} seconds")
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return response
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except Exception as e:
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logger.error(f"Error in pipeline: {str(e)}")
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logger.info(f"Full pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/load-data")
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async def load_data(source: str = "db", file: Optional[UploadFile] = File(None)):
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"""
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Load data from either database or CSV file.
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Args:
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source (str): Source of data ('db' or 'csv')
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file (UploadFile, optional): CSV file to load (required if source is 'csv')
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Returns:
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dict: Status of data loading
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"""
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start_time = time.time()
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try:
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if source not in ['db', 'csv']:
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logger.error(f"Invalid source: {source}")
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logger.info(f"Load data endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=400, detail="Source must be either 'db' or 'csv'")
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if source == 'csv' and not file:
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logger.error("No file provided for CSV source")
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logger.info(f"Load data endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=400, detail="File must be provided when loading from CSV")
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if source == 'csv':
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# Save uploaded file temporarily
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with tempfile.NamedTemporaryFile(delete=False, suffix='.csv') as temp_file:
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content = await file.read()
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temp_file.write(content)
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temp_file_path = temp_file.name
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try:
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success = pipeline.load_data(source='csv', file_path=temp_file_path)
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finally:
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# Clean up temporary file
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os.unlink(temp_file_path)
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else:
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success = pipeline.load_data(source='db')
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if not success:
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logger.error("Failed to load data")
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logger.info(f"Load data endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=500, detail="Failed to load data")
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response = {
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"status": "success",
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"message": f"Successfully loaded {len(pipeline.df)} rows of data",
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"columns": pipeline.df.columns.tolist(),
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"row_count": len(pipeline.df)
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}
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logger.info(f"Load data endpoint completed in {time.time() - start_time:.2f} seconds")
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return response
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except Exception as e:
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logger.error(f"Error loading data: {str(e)}")
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logger.info(f"Load data endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=500, detail=str(e))
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async def get_file_if_csv(source: str, file: Optional[UploadFile] = File(None)):
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"""Dependency to handle file upload only when source is csv."""
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if source == 'csv' and not file:
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raise HTTPException(status_code=400, detail="File must be provided when loading from CSV")
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return file
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@app.post("/run/streaming-pipeline", response_model=List[BatchResponse])
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async def run_streaming_pipeline(
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source: str = "db",
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batch_size: int = 10000,
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file: Optional[Union[UploadFile, str]] = File(None)
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):
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"""
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Run the complete salary analytics pipeline in batches.
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Args:
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source (str): Source of data ('db' or 'csv')
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batch_size (int): Number of rows to process in each batch
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file (UploadFile, optional): CSV file to load (required if source is 'csv')
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Returns:
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List[BatchResponse]: List of responses for each batch processed
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"""
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start_time = time.time()
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try:
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if source not in ['db', 'csv']:
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logger.error(f"Invalid source: {source}")
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logger.info(f"Streaming pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=400, detail="Source must be either 'db' or 'csv'")
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if source == 'csv' and not file:
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logger.error("No file provided for CSV source")
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logger.info(f"Streaming pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=400, detail="File must be provided when loading from CSV")
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# Initialize data loader
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data_loader = DataLoader()
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data_loader.chunk_size = batch_size
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# Create output directory for batch results
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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batch_output_dir = os.path.join(os.path.dirname(OUTPUT_PATHS['final_table']), f"batch_results_{timestamp}")
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os.makedirs(batch_output_dir, exist_ok=True)
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# Initialize database operations
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if not data_loader.connect():
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logger.error("Failed to connect to database")
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logger.info(f"Streaming pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=500, detail="Failed to connect to database")
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db_ops = DatabaseOperations(data_loader.engine)
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if not db_ops.create_batch_results_table():
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logger.error("Failed to create batch results table")
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logger.info(f"Streaming pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
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raise HTTPException(status_code=500, detail="Failed to create batch results table")
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responses = []
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batch_number = 0
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batch_start_time = time.time()
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def preprocess_chunk(chunk):
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"""Preprocess a chunk of data with the same logic as DataLoader."""
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# Convert dates
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chunk['trx_start_date'] = pd.to_datetime(chunk['trx_start_date'])
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chunk['trx_end_date'] = pd.to_datetime(chunk['trx_end_date'])
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# Rename columns
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chunk = chunk.rename(columns={
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'd1': 'trx_type',
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'd2': 'trx_subtype',
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'd3': 'initiated_by',
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'd4': 'customer_id'
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})
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chunk = chunk.dropna()
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return chunk
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if source == 'csv':
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# Save uploaded file temporarily
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with tempfile.NamedTemporaryFile(delete=False, suffix='.csv') as temp_file:
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content = await file.read()
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temp_file.write(content)
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temp_file_path = temp_file.name
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try:
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# Process CSV in chunks
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for chunk in pd.read_csv(temp_file_path, chunksize=batch_size):
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batch_number += 1
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logger.info(f"Processing batch {batch_number}")
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# Preprocess chunk
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chunk = preprocess_chunk(chunk)
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# Run pipeline on chunk
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pipeline = SalaryAnalyticsPipeline()
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pipeline.df = chunk
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try:
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batch_start_time = time.time()
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# Run analyses
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pipeline.run_keyword_analysis()
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pipeline.run_consistent_amount_analysis()
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pipeline.run_transaction_type_analysis()
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# Generate reports
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reports = pipeline.generate_salary_earner_reports()
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# Add batch metadata to results
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results_df = reports['final_table'].copy()
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results_df['batch_number'] = batch_number
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results_df['total_batches'] = -1 # Unknown for CSV
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results_df['processed_at'] = datetime.now()
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# Save batch results to CSV
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batch_results_path = os.path.join(batch_output_dir, f"batch_{batch_number}_results.csv")
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results_df.to_csv(batch_results_path, index=False)
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# Save to database
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db_ops.save_batch_to_db(
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batch_number=batch_number,
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total_batches=-1, # Unknown for CSV
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results_df=results_df,
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status="success"
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)
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logger.info(f"Batch {batch_number} processed in {time.time() - batch_start_time:.2f} seconds")
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responses.append(BatchResponse(
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batch_number=batch_number,
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total_batches=-1, # Unknown for CSV
|
|
processed_rows=len(chunk),
|
|
results_path=batch_results_path,
|
|
message=f"Successfully processed batch {batch_number}"
|
|
))
|
|
except Exception as e:
|
|
error_message = str(e)
|
|
logger.error(f"Error processing batch {batch_number}: {error_message}")
|
|
|
|
# Save error to database
|
|
db_ops.save_batch_to_db(
|
|
batch_number=batch_number,
|
|
total_batches=-1,
|
|
results_df=pd.DataFrame(), # Empty DataFrame for error case
|
|
status="error"
|
|
)
|
|
|
|
responses.append(BatchResponse(
|
|
batch_number=batch_number,
|
|
total_batches=-1,
|
|
processed_rows=len(chunk),
|
|
results_path="",
|
|
message=f"Error processing batch {batch_number}: {error_message}"
|
|
))
|
|
finally:
|
|
# Clean up temporary file
|
|
os.unlink(temp_file_path)
|
|
else:
|
|
# Process database in chunks
|
|
if not data_loader.connect():
|
|
raise HTTPException(status_code=500, detail="Failed to connect to database")
|
|
|
|
# Get total row count
|
|
with data_loader.engine.connect() as conn:
|
|
count_query = text(f"SELECT COUNT(*) FROM {TABLE_NAME}")
|
|
total_rows = conn.execute(count_query).scalar()
|
|
|
|
total_batches = (total_rows + batch_size - 1) // batch_size
|
|
offset = 0
|
|
|
|
while offset < total_rows:
|
|
batch_number += 1
|
|
logger.info(f"Processing batch {batch_number} of {total_batches}")
|
|
|
|
# Load chunk from database
|
|
query = f"SELECT * FROM {TABLE_NAME} LIMIT {batch_size} OFFSET {offset}"
|
|
chunk = pd.read_sql(query, data_loader.engine)
|
|
|
|
if chunk.empty:
|
|
break
|
|
|
|
# Preprocess chunk
|
|
chunk = preprocess_chunk(chunk)
|
|
|
|
# Run pipeline on chunk
|
|
pipeline = SalaryAnalyticsPipeline()
|
|
pipeline.df = chunk
|
|
|
|
try:
|
|
batch_start_time = time.time()
|
|
# Run analyses
|
|
pipeline.run_keyword_analysis()
|
|
pipeline.run_consistent_amount_analysis()
|
|
pipeline.run_transaction_type_analysis()
|
|
|
|
# Generate reports
|
|
reports = pipeline.generate_salary_earner_reports()
|
|
|
|
# Add batch metadata to results
|
|
results_df = reports['final_table'].copy()
|
|
results_df['batch_number'] = batch_number
|
|
results_df['total_batches'] = total_batches
|
|
results_df['processed_at'] = datetime.now()
|
|
|
|
# Save batch results to CSV
|
|
batch_results_path = os.path.join(batch_output_dir, f"batch_{batch_number}_results.csv")
|
|
results_df.to_csv(batch_results_path, index=False)
|
|
|
|
# Save to database
|
|
db_ops.save_batch_to_db(
|
|
batch_number=batch_number,
|
|
total_batches=total_batches,
|
|
results_df=results_df,
|
|
status="success"
|
|
)
|
|
|
|
logger.info(f"Batch {batch_number} of {total_batches} processed in {time.time() - batch_start_time:.2f} seconds")
|
|
|
|
responses.append(BatchResponse(
|
|
batch_number=batch_number,
|
|
total_batches=total_batches,
|
|
processed_rows=len(chunk),
|
|
results_path=batch_results_path,
|
|
message=f"Successfully processed batch {batch_number} of {total_batches}"
|
|
))
|
|
except Exception as e:
|
|
error_message = str(e)
|
|
logger.error(f"Error processing batch {batch_number}: {error_message}")
|
|
|
|
# Save error to database
|
|
db_ops.save_batch_to_db(
|
|
batch_number=batch_number,
|
|
total_batches=total_batches,
|
|
results_df=pd.DataFrame(), # Empty DataFrame for error case
|
|
status="error"
|
|
)
|
|
|
|
responses.append(BatchResponse(
|
|
batch_number=batch_number,
|
|
total_batches=total_batches,
|
|
processed_rows=len(chunk),
|
|
results_path="",
|
|
message=f"Error processing batch {batch_number}: {error_message}"
|
|
))
|
|
|
|
offset += batch_size
|
|
|
|
logger.info(f"Streaming pipeline endpoint completed in {time.time() - start_time:.2f} seconds")
|
|
return responses
|
|
except Exception as e:
|
|
logger.error(f"Error in streaming pipeline: {str(e)}")
|
|
logger.info(f"Streaming pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
|
|
raise HTTPException(status_code=500, detail=str(e)) |