diff --git a/Dockerfile b/Dockerfile
index 2109548..c74ecdb 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,20 +1,26 @@
 FROM python:3.11-slim
 
+# Set the working directory in the container
 WORKDIR /app
 
+# Copy the current directory contents into the container at /app
+COPY . /app
+
 RUN apt-get update && apt-get install -y libpq-dev && rm -rf /var/lib/apt/lists/*
 
 COPY requirements.txt .
 RUN pip install -r requirements.txt
 
-COPY salary_analytics/ ./salary_analytics/
 
 RUN mkdir -p output/csv output/plots output/models
 
-ENV PYTHONPATH=/app
-ENV HOST=0.0.0.0
-ENV PORT=8000
+
+# ENV FLASK_APP=wsgi.py
+
+ENV FLASK_APP=run.py. 
+ENV FLASK_RUN_HOST=0.0.0.0
 
 EXPOSE 8000
 
-CMD ["uvicorn", "salary_analytics.api:app", "--host", "0.0.0.0", "--port", "8000", "--reload"] 
\ No newline at end of file
+# CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:8000", "wsgi:wsgi_app"]
+CMD ["uvicorn", "run:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]
\ No newline at end of file
diff --git a/app.log b/app.log
new file mode 100644
index 0000000..f4039c5
--- /dev/null
+++ b/app.log
@@ -0,0 +1,644 @@
+2025-09-10 10:21:11,016 - INFO - Initializing pipeline...
+2025-09-10 10:21:11,019 - INFO - [2025-09-10 10:21:11] Detecting salary...
+2025-09-10 10:21:11,019 - INFO - Started autonomous salary detection loop.
+2025-09-10 10:21:11,030 - INFO - Server running on hostname: 22bad35c69c3
+2025-09-10 10:21:11,035 - INFO - Server IP address: 172.25.0.2
+2025-09-10 10:21:11,036 - INFO - Server is accessible at:
+2025-09-10 10:21:11,037 - INFO - - http://localhost:8000
+2025-09-10 10:21:11,039 - INFO - - http://127.0.0.1:8000
+2025-09-10 10:21:11,040 - INFO - - http://172.25.0.2:8000
+2025-09-10 10:21:11,041 - INFO - Pipeline initialized successfully
+2025-09-10 10:21:11,532 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:21:11,533 - INFO - [2025-09-10 10:21:11] Salary detection complete
+2025-09-10 10:21:19,451 - INFO - Initializing SalaryAnalyticsPipeline
+2025-09-10 10:21:19,452 - INFO - Starting data loading process
+2025-09-10 10:21:19,453 - INFO - No database connection. Attempting to connect...
+2025-09-10 10:21:19,455 - INFO - Attempting to connect to database...
+2025-09-10 10:21:27,271 - INFO - Table customer_account_transaction_hx exists with 5354307 rows
+2025-09-10 10:21:27,488 - INFO - Connected successfully to database!
+2025-09-10 10:21:27,715 - INFO - Loading data from table: customer_account_transaction_hx
+2025-09-10 10:21:28,614 - INFO - Total rows to process: 5354307
+2025-09-10 10:21:28,852 - INFO - Loading chunk starting at offset 0
+2025-09-10 10:21:40,251 - INFO - Loading chunk starting at offset 10000
+2025-09-10 10:21:46,981 - INFO - Loading chunk starting at offset 20000
+2025-09-10 10:21:57,010 - INFO - Loading chunk starting at offset 30000
+2025-09-10 10:22:04,792 - INFO - Loading chunk starting at offset 40000
+2025-09-10 10:22:09,599 - INFO - Loading chunk starting at offset 50000
+2025-09-10 10:22:15,016 - INFO - Loading chunk starting at offset 60000
+2025-09-10 10:22:18,613 - INFO - Loading chunk starting at offset 70000
+2025-09-10 10:22:22,007 - INFO - Loading chunk starting at offset 80000
+2025-09-10 10:22:28,397 - INFO - Loading chunk starting at offset 90000
+2025-09-10 10:22:36,081 - INFO - Loading chunk starting at offset 100000
+2025-09-10 10:22:48,738 - INFO - Loading chunk starting at offset 110000
+2025-09-10 10:23:01,521 - INFO - Loading chunk starting at offset 120000
+2025-09-10 10:23:11,532 - INFO - [2025-09-10 10:23:11] Detecting salary...
+2025-09-10 10:23:13,504 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:23:13,504 - INFO - [2025-09-10 10:23:13] Salary detection complete
+2025-09-10 10:23:16,348 - INFO - Loading chunk starting at offset 130000
+2025-09-10 10:23:23,259 - INFO - Loading chunk starting at offset 140000
+2025-09-10 10:23:29,284 - INFO - Loading chunk starting at offset 150000
+2025-09-10 10:23:41,579 - INFO - Loading chunk starting at offset 160000
+2025-09-10 10:23:54,788 - INFO - Loading chunk starting at offset 170000
+2025-09-10 10:24:19,519 - INFO - Loading chunk starting at offset 180000
+2025-09-10 10:24:31,657 - INFO - Loading chunk starting at offset 190000
+2025-09-10 10:24:46,130 - INFO - Loading chunk starting at offset 200000
+2025-09-10 10:24:57,289 - INFO - Loading chunk starting at offset 210000
+2025-09-10 10:25:07,964 - INFO - Loading chunk starting at offset 220000
+2025-09-10 10:25:13,503 - INFO - [2025-09-10 10:25:13] Detecting salary...
+2025-09-10 10:25:15,477 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:25:15,478 - INFO - [2025-09-10 10:25:15] Salary detection complete
+2025-09-10 10:25:17,793 - INFO - Loading chunk starting at offset 230000
+2025-09-10 10:25:25,026 - INFO - Loading chunk starting at offset 240000
+2025-09-10 10:25:32,079 - INFO - Loading chunk starting at offset 250000
+2025-09-10 10:25:39,990 - INFO - Loading chunk starting at offset 260000
+2025-09-10 10:25:50,492 - INFO - Loading chunk starting at offset 270000
+2025-09-10 10:26:00,181 - INFO - Loading chunk starting at offset 280000
+2025-09-10 10:26:10,138 - INFO - Loading chunk starting at offset 290000
+2025-09-10 10:26:20,437 - INFO - Loading chunk starting at offset 300000
+2025-09-10 10:26:34,962 - INFO - Loading chunk starting at offset 310000
+2025-09-10 10:26:46,248 - INFO - Loading chunk starting at offset 320000
+2025-09-10 10:26:55,275 - INFO - Loading chunk starting at offset 330000
+2025-09-10 10:27:09,733 - INFO - Loading chunk starting at offset 340000
+2025-09-10 10:27:15,480 - INFO - [2025-09-10 10:27:15] Detecting salary...
+2025-09-10 10:27:16,553 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:27:16,553 - INFO - [2025-09-10 10:27:16] Salary detection complete
+2025-09-10 10:27:26,568 - INFO - Loading chunk starting at offset 350000
+2025-09-10 10:27:36,472 - INFO - Loading chunk starting at offset 360000
+2025-09-10 10:27:44,909 - INFO - Loading chunk starting at offset 370000
+2025-09-10 10:27:54,557 - INFO - Loading chunk starting at offset 380000
+2025-09-10 10:28:00,588 - INFO - Loading chunk starting at offset 390000
+2025-09-10 10:28:05,957 - INFO - Loading chunk starting at offset 400000
+2025-09-10 10:28:12,058 - INFO - Loading chunk starting at offset 410000
+2025-09-10 10:28:19,248 - INFO - Loading chunk starting at offset 420000
+2025-09-10 10:28:29,938 - INFO - Loading chunk starting at offset 430000
+2025-09-10 10:28:51,274 - INFO - Loading chunk starting at offset 440000
+2025-09-10 10:28:57,720 - INFO - Loading chunk starting at offset 450000
+2025-09-10 10:29:02,117 - INFO - Loading chunk starting at offset 460000
+2025-09-10 10:29:11,555 - INFO - Loading chunk starting at offset 470000
+2025-09-10 10:29:16,565 - INFO - [2025-09-10 10:29:16] Detecting salary...
+2025-09-10 10:29:17,452 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:29:17,455 - INFO - [2025-09-10 10:29:17] Salary detection complete
+2025-09-10 10:29:30,145 - INFO - Loading chunk starting at offset 480000
+2025-09-10 10:29:39,048 - INFO - Loading chunk starting at offset 490000
+2025-09-10 10:29:43,412 - INFO - Loading chunk starting at offset 500000
+2025-09-10 10:29:47,415 - INFO - Loading chunk starting at offset 510000
+2025-09-10 10:29:52,149 - INFO - Loading chunk starting at offset 520000
+2025-09-10 10:29:58,331 - INFO - Loading chunk starting at offset 530000
+2025-09-10 10:30:04,933 - INFO - Loading chunk starting at offset 540000
+2025-09-10 10:30:11,934 - INFO - Loading chunk starting at offset 550000
+2025-09-10 10:30:19,315 - INFO - Loading chunk starting at offset 560000
+2025-09-10 10:30:25,683 - INFO - Loading chunk starting at offset 570000
+2025-09-10 10:30:33,577 - INFO - Loading chunk starting at offset 580000
+2025-09-10 10:30:40,363 - INFO - Loading chunk starting at offset 590000
+2025-09-10 10:30:46,205 - INFO - Loading chunk starting at offset 600000
+2025-09-10 10:30:53,957 - INFO - Loading chunk starting at offset 610000
+2025-09-10 10:31:08,083 - INFO - Loading chunk starting at offset 620000
+2025-09-10 10:31:17,454 - INFO - [2025-09-10 10:31:17] Detecting salary...
+2025-09-10 10:31:17,989 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:31:17,989 - INFO - [2025-09-10 10:31:17] Salary detection complete
+2025-09-10 10:31:18,901 - INFO - Loading chunk starting at offset 630000
+2025-09-10 10:31:27,551 - INFO - Loading chunk starting at offset 640000
+2025-09-10 10:31:33,662 - INFO - Loading chunk starting at offset 650000
+2025-09-10 10:31:40,107 - INFO - Loading chunk starting at offset 660000
+2025-09-10 10:31:49,938 - INFO - Loading chunk starting at offset 670000
+2025-09-10 10:31:57,777 - INFO - Loading chunk starting at offset 680000
+2025-09-10 10:32:06,629 - INFO - Loading chunk starting at offset 690000
+2025-09-10 10:32:15,281 - INFO - Loading chunk starting at offset 700000
+2025-09-10 10:32:25,328 - INFO - Loading chunk starting at offset 710000
+2025-09-10 10:32:40,639 - INFO - Loading chunk starting at offset 720000
+2025-09-10 10:32:51,078 - INFO - Loading chunk starting at offset 730000
+2025-09-10 10:33:03,924 - INFO - Loading chunk starting at offset 740000
+2025-09-10 10:33:13,007 - INFO - Loading chunk starting at offset 750000
+2025-09-10 10:33:17,989 - INFO - [2025-09-10 10:33:17] Detecting salary...
+2025-09-10 10:33:19,698 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:33:20,039 - INFO - [2025-09-10 10:33:20] Salary detection complete
+2025-09-10 10:33:20,857 - INFO - Loading chunk starting at offset 760000
+2025-09-10 10:33:30,803 - INFO - Loading chunk starting at offset 770000
+2025-09-10 10:33:38,162 - INFO - Loading chunk starting at offset 780000
+2025-09-10 10:33:44,504 - INFO - Loading chunk starting at offset 790000
+2025-09-10 10:33:50,063 - INFO - Loading chunk starting at offset 800000
+2025-09-10 10:33:57,957 - INFO - Loading chunk starting at offset 810000
+2025-09-10 10:34:05,256 - INFO - Loading chunk starting at offset 820000
+2025-09-10 10:34:12,212 - INFO - Loading chunk starting at offset 830000
+2025-09-10 10:34:20,478 - INFO - Loading chunk starting at offset 840000
+2025-09-10 10:34:28,018 - INFO - Loading chunk starting at offset 850000
+2025-09-10 10:34:36,413 - INFO - Loading chunk starting at offset 860000
+2025-09-10 10:34:50,168 - INFO - Loading chunk starting at offset 870000
+2025-09-10 10:35:13,867 - INFO - Loading chunk starting at offset 880000
+2025-09-10 10:35:20,391 - INFO - [2025-09-10 10:35:20] Detecting salary...
+2025-09-10 10:35:21,791 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:35:21,794 - INFO - [2025-09-10 10:35:21] Salary detection complete
+2025-09-10 10:35:28,918 - INFO - Loading chunk starting at offset 890000
+2025-09-10 10:35:40,300 - INFO - Loading chunk starting at offset 900000
+2025-09-10 10:36:05,763 - INFO - Loading chunk starting at offset 910000
+2025-09-10 10:36:15,199 - INFO - Loading chunk starting at offset 920000
+2025-09-10 10:36:46,036 - INFO - Loading chunk starting at offset 930000
+2025-09-10 10:36:53,221 - INFO - Loading chunk starting at offset 940000
+2025-09-10 10:37:06,754 - INFO - Loading chunk starting at offset 950000
+2025-09-10 10:37:15,558 - INFO - Loading chunk starting at offset 960000
+2025-09-10 10:37:21,798 - INFO - [2025-09-10 10:37:21] Detecting salary...
+2025-09-10 10:37:22,954 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:37:22,955 - INFO - [2025-09-10 10:37:22] Salary detection complete
+2025-09-10 10:37:29,088 - INFO - Loading chunk starting at offset 970000
+2025-09-10 10:37:54,730 - INFO - Loading chunk starting at offset 980000
+2025-09-10 10:38:13,667 - INFO - Loading chunk starting at offset 990000
+2025-09-10 10:38:27,880 - INFO - Loading chunk starting at offset 1000000
+2025-09-10 10:39:07,546 - INFO - Loading chunk starting at offset 1010000
+2025-09-10 10:39:17,369 - INFO - Loading chunk starting at offset 1020000
+2025-09-10 10:39:23,176 - INFO - [2025-09-10 10:39:23] Detecting salary...
+2025-09-10 10:39:24,444 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:39:24,445 - INFO - [2025-09-10 10:39:24] Salary detection complete
+2025-09-10 10:39:26,048 - INFO - Loading chunk starting at offset 1030000
+2025-09-10 10:39:45,494 - INFO - Loading chunk starting at offset 1040000
+2025-09-10 10:39:50,480 - INFO - Loading chunk starting at offset 1050000
+2025-09-10 10:39:54,793 - INFO - Loading chunk starting at offset 1060000
+2025-09-10 10:39:59,415 - INFO - Loading chunk starting at offset 1070000
+2025-09-10 10:40:04,306 - INFO - Loading chunk starting at offset 1080000
+2025-09-10 10:40:10,517 - INFO - Loading chunk starting at offset 1090000
+2025-09-10 10:40:15,671 - INFO - Loading chunk starting at offset 1100000
+2025-09-10 10:40:21,662 - INFO - Loading chunk starting at offset 1110000
+2025-09-10 10:40:30,551 - INFO - Loading chunk starting at offset 1120000
+2025-09-10 10:40:47,391 - INFO - Loading chunk starting at offset 1130000
+2025-09-10 10:40:55,609 - INFO - Loading chunk starting at offset 1140000
+2025-09-10 10:41:04,496 - INFO - Loading chunk starting at offset 1150000
+2025-09-10 10:41:14,608 - INFO - Loading chunk starting at offset 1160000
+2025-09-10 10:41:24,447 - INFO - [2025-09-10 10:41:24] Detecting salary...
+2025-09-10 10:41:28,537 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:41:28,548 - INFO - [2025-09-10 10:41:28] Salary detection complete
+2025-09-10 10:42:00,867 - INFO - Loading chunk starting at offset 1170000
+2025-09-10 10:42:13,069 - INFO - Loading chunk starting at offset 1180000
+2025-09-10 10:42:21,593 - INFO - Loading chunk starting at offset 1190000
+2025-09-10 10:42:32,011 - INFO - Loading chunk starting at offset 1200000
+2025-09-10 10:42:37,982 - INFO - Loading chunk starting at offset 1210000
+2025-09-10 10:42:45,458 - INFO - Loading chunk starting at offset 1220000
+2025-09-10 10:42:54,545 - INFO - Loading chunk starting at offset 1230000
+2025-09-10 10:43:15,705 - INFO - Loading chunk starting at offset 1240000
+2025-09-10 10:43:28,549 - INFO - [2025-09-10 10:43:28] Detecting salary...
+2025-09-10 10:43:31,640 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:43:31,641 - INFO - [2025-09-10 10:43:31] Salary detection complete
+2025-09-10 10:43:43,344 - INFO - Loading chunk starting at offset 1250000
+2025-09-10 10:43:50,004 - INFO - Loading chunk starting at offset 1260000
+2025-09-10 10:43:59,430 - INFO - Loading chunk starting at offset 1270000
+2025-09-10 10:44:07,478 - INFO - Loading chunk starting at offset 1280000
+2025-09-10 10:44:18,927 - INFO - Loading chunk starting at offset 1290000
+2025-09-10 10:44:28,523 - INFO - Loading chunk starting at offset 1300000
+2025-09-10 10:44:38,629 - INFO - Loading chunk starting at offset 1310000
+2025-09-10 10:44:56,977 - INFO - Loading chunk starting at offset 1320000
+2025-09-10 10:45:09,203 - INFO - Loading chunk starting at offset 1330000
+2025-09-10 10:45:18,657 - INFO - Loading chunk starting at offset 1340000
+2025-09-10 10:45:27,201 - INFO - Loading chunk starting at offset 1350000
+2025-09-10 10:45:31,641 - INFO - [2025-09-10 10:45:31] Detecting salary...
+2025-09-10 10:45:32,261 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:45:32,262 - INFO - [2025-09-10 10:45:32] Salary detection complete
+2025-09-10 10:45:35,308 - INFO - Loading chunk starting at offset 1360000
+2025-09-10 10:45:44,783 - INFO - Loading chunk starting at offset 1370000
+2025-09-10 10:45:52,048 - INFO - Loading chunk starting at offset 1380000
+2025-09-10 10:45:58,491 - INFO - Loading chunk starting at offset 1390000
+2025-09-10 10:46:13,270 - INFO - Loading chunk starting at offset 1400000
+2025-09-10 10:46:25,221 - INFO - Loading chunk starting at offset 1410000
+2025-09-10 10:46:34,358 - INFO - Loading chunk starting at offset 1420000
+2025-09-10 10:46:38,777 - INFO - Loading chunk starting at offset 1430000
+2025-09-10 10:46:42,534 - INFO - Loading chunk starting at offset 1440000
+2025-09-10 10:46:46,147 - INFO - Loading chunk starting at offset 1450000
+2025-09-10 10:46:50,727 - INFO - Loading chunk starting at offset 1460000
+2025-09-10 10:46:55,939 - INFO - Loading chunk starting at offset 1470000
+2025-09-10 10:47:01,696 - INFO - Loading chunk starting at offset 1480000
+2025-09-10 10:47:07,411 - INFO - Loading chunk starting at offset 1490000
+2025-09-10 10:47:13,449 - INFO - Loading chunk starting at offset 1500000
+2025-09-10 10:47:23,058 - INFO - Loading chunk starting at offset 1510000
+2025-09-10 10:47:30,097 - INFO - Loading chunk starting at offset 1520000
+2025-09-10 10:47:32,259 - INFO - [2025-09-10 10:47:32] Detecting salary...
+2025-09-10 10:47:32,923 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:47:32,924 - INFO - [2025-09-10 10:47:32] Salary detection complete
+2025-09-10 10:47:36,423 - INFO - Loading chunk starting at offset 1530000
+2025-09-10 10:47:45,086 - INFO - Loading chunk starting at offset 1540000
+2025-09-10 10:47:57,079 - INFO - Loading chunk starting at offset 1550000
+2025-09-10 10:48:19,741 - INFO - Loading chunk starting at offset 1560000
+2025-09-10 10:48:41,300 - INFO - Loading chunk starting at offset 1570000
+2025-09-10 10:48:51,349 - INFO - Loading chunk starting at offset 1580000
+2025-09-10 10:48:58,892 - INFO - Loading chunk starting at offset 1590000
+2025-09-10 10:49:04,857 - INFO - Loading chunk starting at offset 1600000
+2025-09-10 10:49:10,299 - INFO - Loading chunk starting at offset 1610000
+2025-09-10 10:49:16,650 - INFO - Loading chunk starting at offset 1620000
+2025-09-10 10:49:23,107 - INFO - Loading chunk starting at offset 1630000
+2025-09-10 10:49:32,320 - INFO - Loading chunk starting at offset 1640000
+2025-09-10 10:49:32,927 - INFO - [2025-09-10 10:49:32] Detecting salary...
+2025-09-10 10:49:33,484 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:49:33,485 - INFO - [2025-09-10 10:49:33] Salary detection complete
+2025-09-10 10:49:40,707 - INFO - Loading chunk starting at offset 1650000
+2025-09-10 10:49:50,677 - INFO - Loading chunk starting at offset 1660000
+2025-09-10 10:49:59,664 - INFO - Loading chunk starting at offset 1670000
+2025-09-10 10:50:07,452 - INFO - Loading chunk starting at offset 1680000
+2025-09-10 10:50:13,583 - INFO - Loading chunk starting at offset 1690000
+2025-09-10 10:50:20,858 - INFO - Loading chunk starting at offset 1700000
+2025-09-10 10:50:27,519 - INFO - Loading chunk starting at offset 1710000
+2025-09-10 10:50:34,012 - INFO - Loading chunk starting at offset 1720000
+2025-09-10 10:50:39,628 - INFO - Loading chunk starting at offset 1730000
+2025-09-10 10:50:50,012 - INFO - Loading chunk starting at offset 1740000
+2025-09-10 10:51:06,647 - INFO - Loading chunk starting at offset 1750000
+2025-09-10 10:51:17,401 - INFO - Loading chunk starting at offset 1760000
+2025-09-10 10:51:26,360 - INFO - Loading chunk starting at offset 1770000
+2025-09-10 10:51:33,485 - INFO - [2025-09-10 10:51:33] Detecting salary...
+2025-09-10 10:51:55,392 - INFO - Initializing pipeline...
+2025-09-10 10:51:55,395 - INFO - [2025-09-10 10:51:55] Detecting salary...
+2025-09-10 10:51:55,395 - INFO - Started autonomous salary detection loop.
+2025-09-10 10:51:55,409 - INFO - Server running on hostname: 22bad35c69c3
+2025-09-10 10:51:55,416 - INFO - Server IP address: 172.25.0.2
+2025-09-10 10:51:55,424 - INFO - Server is accessible at:
+2025-09-10 10:51:55,426 - INFO - - http://localhost:8000
+2025-09-10 10:51:55,444 - INFO - - http://127.0.0.1:8000
+2025-09-10 10:51:55,455 - INFO - - http://172.25.0.2:8000
+2025-09-10 10:51:55,456 - INFO - Pipeline initialized successfully
+2025-09-10 10:51:56,024 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:51:56,024 - INFO - [2025-09-10 10:51:56] Salary detection complete
+2025-09-10 10:52:36,450 - INFO - Initializing SalaryAnalyticsPipeline
+2025-09-10 10:52:36,451 - INFO - Starting data loading process
+2025-09-10 10:52:36,452 - INFO - No database connection. Attempting to connect...
+2025-09-10 10:52:36,452 - INFO - Attempting to connect to database...
+2025-09-10 10:52:36,538 - ERROR - Error connecting to database: No module named 'oracledb'
+2025-09-10 10:52:36,539 - ERROR - Failed to establish database connection
+2025-09-10 10:52:36,539 - ERROR - Failed to load data
+2025-09-10 10:52:36,540 - ERROR - Failed to load data
+2025-09-10 10:52:36,541 - INFO - Load data endpoint failed after 0.09 seconds
+2025-09-10 10:52:36,541 - ERROR - Error loading data: 500: Failed to load data
+2025-09-10 10:52:36,542 - INFO - Load data endpoint failed after 0.09 seconds
+2025-09-10 10:52:58,149 - INFO - Shutting down Salary Analytics API...
+2025-09-10 10:53:10,697 - INFO - generated new fontManager
+2025-09-10 10:53:16,039 - INFO - Initializing pipeline...
+2025-09-10 10:53:16,041 - INFO - [2025-09-10 10:53:16] Detecting salary...
+2025-09-10 10:53:16,042 - INFO - Started autonomous salary detection loop.
+2025-09-10 10:53:16,055 - INFO - Server running on hostname: 1c5d2376fb2a
+2025-09-10 10:53:16,056 - INFO - Server IP address: 172.25.0.2
+2025-09-10 10:53:16,057 - INFO - Server is accessible at:
+2025-09-10 10:53:16,058 - INFO - - http://localhost:8000
+2025-09-10 10:53:16,059 - INFO - - http://127.0.0.1:8000
+2025-09-10 10:53:16,060 - INFO - - http://172.25.0.2:8000
+2025-09-10 10:53:16,062 - INFO - Pipeline initialized successfully
+2025-09-10 10:53:16,812 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 10:53:16,813 - INFO - [2025-09-10 10:53:16] Salary detection complete
+2025-09-10 10:53:25,477 - INFO - Initializing SalaryAnalyticsPipeline
+2025-09-10 10:53:25,482 - INFO - Starting data loading process
+2025-09-10 10:53:25,483 - INFO - No database connection. Attempting to connect...
+2025-09-10 10:53:25,484 - INFO - Attempting to connect to database...
+2025-09-10 10:53:25,679 - ERROR - Error connecting to database: No module named 'oracledb'
+2025-09-10 10:53:25,679 - ERROR - Failed to establish database connection
+2025-09-10 10:53:25,680 - ERROR - Failed to load data
+2025-09-10 10:53:25,680 - ERROR - Failed to load data
+2025-09-10 10:53:25,681 - INFO - Load data endpoint failed after 0.20 seconds
+2025-09-10 10:53:25,682 - ERROR - Error loading data: 500: Failed to load data
+2025-09-10 10:53:25,682 - INFO - Load data endpoint failed after 0.20 seconds
+2025-09-10 10:53:46,324 - INFO - Shutting down Salary Analytics API...
+2025-09-10 11:07:49,841 - INFO - generated new fontManager
+2025-09-10 11:07:59,771 - INFO - Initializing pipeline...
+2025-09-10 11:07:59,774 - INFO - [2025-09-10 11:07:59] Detecting salary...
+2025-09-10 11:07:59,774 - INFO - Started autonomous salary detection loop.
+2025-09-10 11:07:59,893 - INFO - Server running on hostname: 0b21809edf52
+2025-09-10 11:07:59,894 - INFO - Server IP address: 172.25.0.2
+2025-09-10 11:07:59,895 - INFO - Server is accessible at:
+2025-09-10 11:07:59,915 - INFO - - http://localhost:8000
+2025-09-10 11:07:59,917 - INFO - - http://127.0.0.1:8000
+2025-09-10 11:07:59,918 - INFO - - http://172.25.0.2:8000
+2025-09-10 11:07:59,919 - INFO - Pipeline initialized successfully
+2025-09-10 11:08:01,268 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 11:08:01,269 - INFO - [2025-09-10 11:08:01] Salary detection complete
+2025-09-10 11:08:15,668 - INFO - Initializing SalaryAnalyticsPipeline
+2025-09-10 11:08:15,669 - INFO - Starting data loading process
+2025-09-10 11:08:15,671 - INFO - No database connection. Attempting to connect...
+2025-09-10 11:08:15,672 - INFO - Attempting to connect to database...
+2025-09-10 11:08:20,076 - ERROR - Error connecting to database: (oracledb.exceptions.DatabaseError) ORA-00936: missing expression
+Help: https://docs.oracle.com/error-help/db/ora-00936/
+[SQL: SELECT EXISTS (SELECT FROM information_schema.tables WHERE table_name = 'customer_account_transaction_hx')]
+(Background on this error at: https://sqlalche.me/e/20/4xp6)
+2025-09-10 11:08:20,086 - ERROR - Failed to establish database connection
+2025-09-10 11:08:20,087 - ERROR - Failed to load data
+2025-09-10 11:08:20,092 - ERROR - Failed to load data
+2025-09-10 11:08:20,119 - INFO - Load data endpoint failed after 4.45 seconds
+2025-09-10 11:08:20,123 - ERROR - Error loading data: 500: Failed to load data
+2025-09-10 11:08:20,124 - INFO - Load data endpoint failed after 4.46 seconds
+2025-09-10 11:10:01,280 - INFO - [2025-09-10 11:10:01] Detecting salary...
+2025-09-10 11:10:02,367 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 11:10:02,368 - INFO - [2025-09-10 11:10:02] Salary detection complete
+2025-09-10 11:12:02,395 - INFO - [2025-09-10 11:12:02] Detecting salary...
+2025-09-10 11:12:03,234 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 11:12:03,256 - INFO - [2025-09-10 11:12:03] Salary detection complete
+2025-09-10 11:14:03,279 - INFO - [2025-09-10 11:14:03] Detecting salary...
+2025-09-10 11:14:04,266 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 11:14:04,267 - INFO - [2025-09-10 11:14:04] Salary detection complete
+2025-09-10 11:16:04,268 - INFO - [2025-09-10 11:16:04] Detecting salary...
+2025-09-10 11:16:05,133 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 11:16:05,134 - INFO - [2025-09-10 11:16:05] Salary detection complete
+2025-09-10 11:18:05,134 - INFO - [2025-09-10 11:18:05] Detecting salary...
+2025-09-10 11:18:05,955 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 11:18:05,956 - INFO - [2025-09-10 11:18:05] Salary detection complete
+2025-09-10 11:20:05,855 - INFO - [2025-09-10 11:20:05] Detecting salary...
+2025-09-10 11:20:06,453 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 11:20:06,463 - INFO - [2025-09-10 11:20:06] Salary detection complete
+2025-09-10 11:22:06,512 - INFO - [2025-09-10 11:22:06] Detecting salary...
+2025-09-10 11:22:07,087 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 11:22:07,088 - INFO - [2025-09-10 11:22:07] Salary detection complete
+2025-09-10 11:24:07,092 - INFO - [2025-09-10 11:24:07] Detecting salary...
+2025-09-10 11:24:08,235 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 11:24:08,236 - INFO - [2025-09-10 11:24:08] Salary detection complete
+2025-09-10 11:25:13,551 - INFO - Shutting down Salary Analytics API...
+2025-09-10 11:25:52,852 - INFO - Initializing pipeline...
+2025-09-10 11:25:52,857 - INFO - [2025-09-10 11:25:52] Detecting salary...
+2025-09-10 11:25:52,857 - INFO - Started autonomous salary detection loop.
+2025-09-10 11:25:52,866 - INFO - Server running on hostname: 0b21809edf52
+2025-09-10 11:25:52,876 - INFO - Server IP address: 172.25.0.2
+2025-09-10 11:25:52,881 - INFO - Server is accessible at:
+2025-09-10 11:25:52,882 - INFO - - http://localhost:8000
+2025-09-10 11:25:52,883 - INFO - - http://127.0.0.1:8000
+2025-09-10 11:25:52,884 - INFO - - http://172.25.0.2:8000
+2025-09-10 11:25:52,884 - INFO - Pipeline initialized successfully
+2025-09-10 11:25:53,580 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 11:25:53,581 - INFO - [2025-09-10 11:25:53] Salary detection complete
+2025-09-10 11:27:53,578 - INFO - [2025-09-10 11:27:53] Detecting salary...
+2025-09-10 11:27:54,235 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 11:27:54,235 - INFO - [2025-09-10 11:27:54] Salary detection complete
+2025-09-10 11:29:54,235 - INFO - [2025-09-10 11:29:54] Detecting salary...
+2025-09-10 11:29:54,827 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-10 11:29:54,827 - INFO - [2025-09-10 11:29:54] Salary detection complete
+2025-09-10 11:30:20,979 - INFO - Shutting down Salary Analytics API...
+2025-09-10 11:31:18,351 - INFO - Initializing pipeline...
+2025-09-10 11:31:18,375 - INFO - [2025-09-10 11:31:18] Detecting salary...
+2025-09-10 11:31:18,376 - INFO - Started autonomous salary detection loop.
+2025-09-10 11:31:18,405 - INFO - Server running on hostname: 0b21809edf52
+2025-09-10 11:31:18,447 - INFO - Server IP address: 172.25.0.2
+2025-09-10 11:31:18,528 - INFO - Server is accessible at:
+2025-09-10 11:31:18,552 - INFO - - http://localhost:8000
+2025-09-10 11:31:18,552 - INFO - - http://127.0.0.1:8000
+2025-09-10 11:31:18,553 - INFO - - http://172.25.0.2:8000
+2025-09-10 11:31:18,554 - INFO - Pipeline initialized successfully
+2025-09-19 11:27:20,722 - INFO - Initializing pipeline...
+2025-09-19 11:27:20,724 - INFO - [2025-09-19 11:27:20] Detecting salary...
+2025-09-19 11:27:20,724 - INFO - Started autonomous salary detection loop.
+2025-09-19 11:27:20,750 - INFO - Server running on hostname: 0b21809edf52
+2025-09-19 11:27:20,752 - INFO - Server IP address: 172.25.0.2
+2025-09-19 11:27:20,753 - INFO - Server is accessible at:
+2025-09-19 11:27:20,759 - INFO - - http://localhost:8000
+2025-09-19 11:27:20,761 - INFO - - http://127.0.0.1:8000
+2025-09-19 11:27:20,766 - INFO - - http://172.25.0.2:8000
+2025-09-19 11:27:20,771 - INFO - Pipeline initialized successfully
+2025-09-19 11:27:24,622 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-19 11:27:24,628 - INFO - [2025-09-19 11:27:24] Salary detection complete
+2025-09-19 11:29:13,095 - INFO - Shutting down Salary Analytics API...
+2025-09-24 09:47:38,012 - INFO - Initializing pipeline...
+2025-09-24 09:47:38,017 - INFO - [2025-09-24 09:47:38] Detecting salary...
+2025-09-24 09:47:38,024 - INFO - Started autonomous salary detection loop.
+2025-09-24 09:47:38,077 - INFO - Server running on hostname: 0b21809edf52
+2025-09-24 09:47:38,079 - INFO - Server IP address: 172.25.0.2
+2025-09-24 09:47:38,080 - INFO - Server is accessible at:
+2025-09-24 09:47:38,081 - INFO - - http://localhost:8000
+2025-09-24 09:47:38,082 - INFO - - http://127.0.0.1:8000
+2025-09-24 09:47:38,083 - INFO - - http://172.25.0.2:8000
+2025-09-24 09:47:38,084 - INFO - Pipeline initialized successfully
+2025-09-24 09:47:41,272 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-24 09:47:41,273 - INFO - [2025-09-24 09:47:41] Salary detection complete
+2025-09-24 09:49:41,290 - INFO - [2025-09-24 09:49:41] Detecting salary...
+2025-09-24 09:49:43,791 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-24 09:49:43,791 - INFO - [2025-09-24 09:49:43] Salary detection complete
+2025-09-24 09:50:21,300 - INFO - Shutting down Salary Analytics API...
+2025-09-24 09:59:27,300 - INFO - generated new fontManager
+2025-09-24 09:59:30,871 - INFO - Initializing pipeline...
+2025-09-24 09:59:30,872 - INFO - [2025-09-24 09:59:30] Detecting salary...
+2025-09-24 09:59:30,872 - INFO - Started autonomous salary detection loop.
+2025-09-24 09:59:30,877 - INFO - Server running on hostname: bfe07c2f7da2
+2025-09-24 09:59:30,878 - INFO - Server IP address: 172.25.0.2
+2025-09-24 09:59:30,878 - INFO - Server is accessible at:
+2025-09-24 09:59:30,883 - INFO - - http://localhost:8000
+2025-09-24 09:59:30,884 - INFO - - http://127.0.0.1:8000
+2025-09-24 09:59:30,887 - INFO - - http://172.25.0.2:8000
+2025-09-24 09:59:30,889 - INFO - Pipeline initialized successfully
+2025-09-24 09:59:32,676 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
+  "data": [],
+  "error": {},
+  "message": "AutoCall Add Salary Successful",
+  "status": true,
+  "statusCode": 200
+}
+
+2025-09-24 09:59:32,705 - INFO - [2025-09-24 09:59:32] Salary detection complete
diff --git a/salary_analytics/app/__init__.py b/app/api/__init__.py
similarity index 59%
rename from salary_analytics/app/__init__.py
rename to app/api/__init__.py
index b286361..dde0fd0 100644
--- a/salary_analytics/app/__init__.py
+++ b/app/api/__init__.py
@@ -1,17 +1,21 @@
 from flask import Flask
 import os
-from .extensions import db, migrate
+from app.extensions import db, migrate
+
+
 
 def create_app():
     app = Flask(__name__)
-    app.config.from_object('salary_analytics.config')
+    
+    # Load configuration from config.py
+    app.config.from_object('app.config')
 
     # Initialize extensions
     db.init_app(app)
     migrate.init_app(app, db)
 
     # Register blueprints or CLI commands here if needed
-    from . import commands
+    from app.api.commands import commands
     app.cli.add_command(commands.upload_xls_cli)
 
     return app 
\ No newline at end of file
diff --git a/salary_analytics/app/commands.py b/app/api/commands/commands.py
similarity index 94%
rename from salary_analytics/app/commands.py
rename to app/api/commands/commands.py
index b90b73e..d055a5b 100644
--- a/salary_analytics/app/commands.py
+++ b/app/api/commands/commands.py
@@ -2,8 +2,8 @@ import click
 import pandas as pd
 from datetime import datetime
 from flask.cli import with_appcontext
-from salary_analytics.app.extensions import db
-from salary_analytics.app.models import RawTransaction
+from app.extensions import db
+from app.models import RawTransaction
 
 @click.group()
 def commands():
diff --git a/salary_analytics/config.py b/app/config.py
similarity index 77%
rename from salary_analytics/config.py
rename to app/config.py
index fccdb37..32a2569 100644
--- a/salary_analytics/config.py
+++ b/app/config.py
@@ -22,20 +22,32 @@ os.makedirs(PLOTS_DIR, exist_ok=True)
 os.makedirs(CSV_DIR, exist_ok=True)
 os.makedirs(MODEL_DIR, exist_ok=True)
 
+
 # Database Configuration
 DB_CONFIG = {
-    "user": os.getenv("DB_USER"),  # Default value as fallback
-    "password": os.getenv("DB_PASSWORD"),
-    "name": os.getenv("DB_NAME"),
-    "port": os.getenv("DB_PORT"),
-    "host": os.getenv("DB_HOST")
+    "user": os.getenv("DATABASE_USER"), 
+    "password": os.getenv("DATABASE_PASSWORD"),
+    "name": os.getenv("DATABASE_NAME"),
+    "port": os.getenv("DATABASE_PORT", 10532),
+    "host": os.getenv("DATABASE_HOST", "firstadvancedev"),
+    "sid": os.getenv("DATABASE_SID", "FREE"),
+    "service_name": os.getenv("DATABASE_SERVICE_NAME", "firstadv")
 }
 
+
+# DNS =  f"(DESCRIPTION=(ADDRESS=(PROTOCOL=TCP)(HOST={DB_CONFIG['host']})(PORT={DB_CONFIG['port']}))(CONNECT_DATA=(SID={DB_CONFIG['sid']})))"
+    
+# Database Connection
+# SQLALCHEMY_DATABASE_URI = (f"oracle+oracledb://{DB_CONFIG['user']}:{DB_CONFIG['password']}@{DNS}")
+SQLALCHEMY_DATABASE_URI = ( f"oracle+oracledb://{DB_CONFIG['user']}:{DB_CONFIG['password']}@{DB_CONFIG['host']}:{DB_CONFIG['port']}/?service_name={DB_CONFIG['service_name']}")
+
+
 # SQLAlchemy Configuration
-SQLALCHEMY_DATABASE_URI = (
-    f"postgresql://{DB_CONFIG['user']}:{DB_CONFIG['password']}@"
-    f"{DB_CONFIG['host']}:{DB_CONFIG['port']}/{DB_CONFIG['name']}"
-)
+# SQLALCHEMY_DATABASE_URI = (
+#     f"postgresql://{DB_CONFIG['user']}:{DB_CONFIG['password']}@"
+#     f"{DB_CONFIG['host']}:{DB_CONFIG['port']}/{DB_CONFIG['name']}"
+# )
+
 SQLALCHEMY_TRACK_MODIFICATIONS = False
 
 # Table Configuration
@@ -80,7 +92,7 @@ OUTPUT_PATHS = {
 } 
 
 SIMBRELLA_BASE_URL = os.getenv("SIMBRELLA_BASE_URL", "http://127.0.0.1:6337")
-SIMBRELLA_ENDPOINT_RAC_CHECKS = os.getenv("SIMBRELLA_ENDPOINT_RAC_CHECKS","api/rac-check")
+SIMBRELLA_ENDPOINT_RAC_CHECKS = os.getenv("SIMBRELLA_ENDPOINT_RAC_CHECKS", "api/rac-check")
 
 # Salary Detect Endpoint Config
 SALARY_DETECT_URL = "http://www.simbrellang.net:5000/autocall/analytic-salary-detect"
diff --git a/salary_analytics/app/extensions.py b/app/extensions.py
similarity index 100%
rename from salary_analytics/app/extensions.py
rename to app/extensions.py
diff --git a/app/models/__init__.py b/app/models/__init__.py
new file mode 100644
index 0000000..62baa90
--- /dev/null
+++ b/app/models/__init__.py
@@ -0,0 +1,4 @@
+from .raw_transaction import RawTransaction
+
+
+__all__ = ['RawTransaction']
\ No newline at end of file
diff --git a/app/models/account.py b/app/models/account.py
new file mode 100644
index 0000000..2f08f72
--- /dev/null
+++ b/app/models/account.py
@@ -0,0 +1,18 @@
+from app.extensions import db
+
+
+class Account(db.Model):
+    __tablename__ = "accounts"
+
+    customerid = db.Column(db.String(50), primary_key=True)
+    accountid = db.Column(db.String(11), nullable=False)
+    registrationdate = db.Column(db.DateTime)
+    accountcurrencycode = db.Column(db.String(3))
+    schemecode = db.Column(db.String(5))
+    lastinflowtransactiondate = db.Column(db.DateTime)
+    accountstatus = db.Column(db.String(50))
+    accountstatusdate = db.Column(db.DateTime)
+
+
+    def __repr__(self):
+        return f"<Account {self.accountid}>"
\ No newline at end of file
diff --git a/app/models/batch_results.py b/app/models/batch_results.py
new file mode 100644
index 0000000..fcfdd91
--- /dev/null
+++ b/app/models/batch_results.py
@@ -0,0 +1,97 @@
+from sqlalchemy import Column, Integer, String, DateTime, Numeric, Boolean, func
+from sqlalchemy.orm import declarative_base, Session
+from datetime import datetime
+from app.utils.logger import logger
+from app.extensions import db
+
+
+class BatchResult(db.Model):
+    __tablename__ = "salary_analytics_batch_results"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    batch_number = Column(Integer, nullable=False)
+    total_batches = Column(Integer, nullable=False)
+    processed_at = Column(DateTime, default=datetime.utcnow)
+    accountid = Column(String, nullable=False)
+    num_months = Column(Integer)
+    least_inflow_6m = Column(Numeric)
+    avg_monthly_salary = Column(Numeric)
+    estimated_next_amount = Column(Numeric)
+    estimated_next_date = Column(DateTime)
+    is_45day_salary = Column(Boolean, default=False)
+    is_2months_salary = Column(Boolean, default=False)
+    status = Column(String, default="success")
+
+
+    @classmethod
+    def save_batch(cls, batch_number, total_batches, results_df, status="success"):
+        """Save batch results into DB using ORM bulk insert."""
+        try:
+            results_df["batch_number"] = batch_number
+            results_df["total_batches"] = total_batches
+            results_df["processed_at"] = datetime.utcnow()
+            results_df["status"] = status
+
+            # Normalize boolean columns
+            results_df["is_45day_salary"] = results_df.get("45daysalary", False)
+            results_df["is_2months_salary"] = results_df.get("2monthssalary", False)
+
+            # Convert to list of ORM objects
+            records = [
+                cls(**row)
+                for row in results_df.to_dict("records")
+            ]
+
+            db.session.bulk_save_objects(records)
+            db.session.commit()
+            logger.info(f"Saved batch {batch_number} successfully.")
+            return True
+        except Exception as e:
+            db.session.rollback()
+            logger.error(f"Error saving batch {batch_number}: {str(e)}")
+            return False
+
+    @classmethod
+    def get_batch_status(cls, batch_number: int):
+        """Return summary info about one batch."""
+        try:
+            result = (
+                db.session.query(
+                    cls.batch_number,
+                    cls.total_batches,
+                    cls.processed_at,
+                    func.count().label("total_records"),
+                    func.sum(func.case((cls.status == "success", 1), else_=0)).label("successful_records"),
+                    func.sum(func.case((cls.status == "error", 1), else_=0)).label("failed_records"),
+                )
+                .filter(cls.batch_number == batch_number)
+                .group_by(cls.batch_number, cls.total_batches, cls.processed_at)
+                .order_by(cls.processed_at.desc())
+                .first()
+            )
+            return dict(result._mapping) if result else None
+        except Exception as e:
+            logger.error(f"Error fetching batch {batch_number} status: {str(e)}")
+            return None
+
+    @classmethod
+    def get_all_batches(cls):
+        """Return summaries for all batches."""
+        try:
+            results = (
+                db.session.query(
+                    cls.batch_number,
+                    cls.total_batches,
+                    cls.processed_at,
+                    func.count().label("total_records"),
+                    func.sum(func.case((cls.status == "success", 1), else_=0)).label("successful_records"),
+                    func.sum(func.case((cls.status == "error", 1), else_=0)).label("failed_records"),
+                )
+                .group_by(cls.batch_number, cls.total_batches, cls.processed_at)
+                .order_by(cls.batch_number)
+                .all()
+            )
+            return [dict(r._mapping) for r in results]
+        except Exception as e:
+            logger.error(f"Error fetching all batches: {str(e)}")
+            return []
diff --git a/app/models/customer_account_transaction_hx.py b/app/models/customer_account_transaction_hx.py
new file mode 100644
index 0000000..ac3e006
--- /dev/null
+++ b/app/models/customer_account_transaction_hx.py
@@ -0,0 +1,100 @@
+from venv import logger
+from sqlalchemy import Column, Integer, String, Float, DateTime, ForeignKey
+from sqlalchemy.orm import relationship
+from app.extensions import db 
+import pandas as pd
+
+class CustomerAccountTransactionHx(db.Model):
+    __tablename__ = "customer_account_transaction_hx"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    accountid = Column(String(64), nullable=False, index=True)
+    trx_type = Column(String(50), nullable=False)
+    amount = Column(Float, nullable=False)
+    description = Column(String(255))
+    customer_id = Column(String(64))
+    trx_start_date = Column(DateTime, nullable=False)
+    trx_end_date = Column(DateTime)
+    is_salary_related = Column(Integer, default=0)  
+    is_consistent_amount = Column(Integer, default=0)
+    is_salary_type = Column(Integer, default=0)
+
+
+
+    @classmethod
+    def get_all(cls):
+        """Fetch all transactions."""
+        return db.session.query(cls).all()
+
+    @classmethod
+    def get_rows_count(cls):
+        """Return total number of transaction rows."""
+        try:
+            count = db.session.query(db.func.count(cls.id)).scalar()
+            return count
+        except Exception as e:
+            logger.error(f"Error getting row count: {str(e)}")
+            return None
+
+    @classmethod
+    def get_by_account(cls, accountid: str):
+        """Fetch transactions for a given account."""
+        return db.session.query(cls).filter_by(accountid=accountid).all()
+
+    @classmethod
+    def get_accounts(cls, limit=None):
+        """Fetch distinct account IDs."""
+        query = db.session.query(cls.accountid).distinct()
+        if limit:
+            query = query.limit(limit)
+        return [row.accountid for row in query.all()]
+
+    @classmethod
+    def insert_transaction(cls, **kwargs):
+        """Insert a new transaction."""
+        trx = cls(**kwargs)
+        try:
+            db.session.add(trx)
+            db.session.commit()
+        except Exception as e:
+            logger.error(f"Error inserting transaction: {str(e)}")
+            return None
+        return trx
+
+    @classmethod
+    def bulk_insert(cls, transactions: list[dict]):
+        """Insert multiple transactions at once."""
+        objs = [cls(**trx) for trx in transactions]
+
+        try:
+            db.session.bulk_save_objects(objs)
+            db.session.commit()
+        except Exception as e:
+            logger.error(f"Error in bulk insert: {str(e)}")
+            return None
+        return objs
+
+    @classmethod
+    def get_transactions_df(cls, accountids: list[str] = None):
+        """Return a Pandas DataFrame for ML model preparation."""
+        query = db.session.query(cls)
+        if accountids:
+            query = query.filter(cls.accountid.in_(accountids))
+        rows = query.all()
+
+
+        df = pd.DataFrame([{
+            "id": trx.id,
+            "accountid": trx.accountid,
+            "trx_type": trx.trx_type,
+            "amount": trx.amount,
+            "description": trx.description,
+            "customer_id": trx.customer_id,
+            "trx_start_date": trx.trx_start_date,
+            "trx_end_date": trx.trx_end_date,
+            "is_salary_related": trx.is_salary_related,
+            "is_consistent_amount": trx.is_consistent_amount,
+            "is_salary_type": trx.is_salary_type,
+        } for trx in rows])
+
+        return df
diff --git a/salary_analytics/db_operations.py b/app/models/db_operations.py
similarity index 98%
rename from salary_analytics/db_operations.py
rename to app/models/db_operations.py
index 9cb317e..6a20389 100644
--- a/salary_analytics/db_operations.py
+++ b/app/models/db_operations.py
@@ -2,12 +2,10 @@
 Database operations module for salary analytics.
 """
 
-import logging
 from sqlalchemy import text
-from .config import BATCH_RESULTS_TABLE
+from app.config import BATCH_RESULTS_TABLE
 from datetime import datetime
-
-logger = logging.getLogger(__name__)
+from app.utils.logger import logger
 
 class DatabaseOperations:
     def __init__(self, engine):
diff --git a/salary_analytics/app/models.py b/app/models/raw_transaction.py
similarity index 91%
rename from salary_analytics/app/models.py
rename to app/models/raw_transaction.py
index 72615f9..a34b7d8 100644
--- a/salary_analytics/app/models.py
+++ b/app/models/raw_transaction.py
@@ -1,4 +1,4 @@
-from .extensions import db
+from app.extensions import db
 
 class RawTransaction(db.Model):
     __tablename__ = 'analytics_raw_transactions'
diff --git a/app/models/simbrella_customer.py b/app/models/simbrella_customer.py
new file mode 100644
index 0000000..2e16424
--- /dev/null
+++ b/app/models/simbrella_customer.py
@@ -0,0 +1,36 @@
+from app.extensions import db
+
+class SimbrellaCustomer(db.Model):
+    __tablename__ = "simbrella_customers"
+
+    customerid = db.Column(db.String(500), primary_key=True)
+    acct_opn_date = db.Column(db.DateTime)
+    gender = db.Column(db.String(20))
+    birth_date = db.Column(db.DateTime)
+    msisdn = db.Column(db.String(100))
+    isbvnvalid = db.Column(db.String(100))
+    datebvnvalidated = db.Column(db.DateTime)
+    iscrmsvalid = db.Column(db.String(100))
+    datecrmsvalidated = db.Column(db.DateTime)
+    iscrcvalid = db.Column(db.String(100))
+    datecrcvalidated = db.Column(db.DateTime)
+
+
+    def __repr__(self):
+        return f"<SimbrellaCustomer {self.customerid}>"
+    
+
+    def to_dict(self):
+        return {
+            "customerid": self.customerid,
+            "acct_opn_date": self.acct_opn_date,
+            "gender": self.gender,
+            "birth_date": self.birth_date,
+            "msisdn": self.msisdn,
+            "isbvnvalid": self.isbvnvalid,
+            "datebvnvalidated": self.datebvnvalidated,
+            "iscrmsvalid": self.iscrmsvalid,
+            "datecrmsvalidated": self.datecrmsvalidated,
+            "iscrcvalid": self.iscrcvalid,
+            "datecrcvalidated": self.datecrcvalidated,
+        }
\ No newline at end of file
diff --git a/app/models/temp_simbrella_lien.py b/app/models/temp_simbrella_lien.py
new file mode 100644
index 0000000..5449120
--- /dev/null
+++ b/app/models/temp_simbrella_lien.py
@@ -0,0 +1,29 @@
+from app.extensions import db
+
+
+class TempSimbrellaLien(db.Model):
+    __tablename__ = "tmp_simbrella_lien"
+
+    lienid = db.Column(db.String(33), primary_key=True)
+    customerid = db.Column(db.String(500))
+    accountid = db.Column(db.String(11))
+    lien_start_date = db.Column(db.DateTime)
+    action = db.Column(db.String(5))
+    restriction_nature = db.Column(db.String(150))
+    claim_amount = db.Column(db.Numeric(20, 4))
+
+
+    def __repr__(self):
+        return f"<TempSimbrellaLien {self.lienid}>"
+    
+
+    def to_dict(self):
+        return {
+            "lienid": self.lienid,
+            "customerid": self.customerid,
+            "accountid": self.accountid,
+            "lien_start_date": self.lien_start_date,
+            "action": self.action,
+            "restriction_nature": self.restriction_nature,
+            "claim_amount": float(self.claim_amount) if self.claim_amount is not None else None,
+        }
\ No newline at end of file
diff --git a/app/models/transaction.py b/app/models/transaction.py
new file mode 100644
index 0000000..f2c1de0
--- /dev/null
+++ b/app/models/transaction.py
@@ -0,0 +1,45 @@
+from app.extensions import db
+
+
+class Transaction(db.Model):
+    __tablename__ = "transactions"
+
+    tran_id = db.Column(db.String(50), primary_key=True)
+    cif_id = db.Column(db.String(500))
+    foracid = db.Column(db.String(150))
+    acid = db.Column(db.String(150))
+    tran_date = db.Column(db.DateTime)
+    value_date = db.Column(db.DateTime)
+    pstd_date = db.Column(db.DateTime)
+    tran_sub_type = db.Column(db.String(50))
+    part_tran_type = db.Column(db.String(50))
+    tran_crncy_code = db.Column(db.String(50))
+    tran_amt = db.Column(db.Numeric(38, 0))
+    tran_particular = db.Column(db.String(250))
+    origination_channel = db.Column(db.String(150))
+    reversal_tran_id = db.Column(db.String(50))
+    isreversal = db.Column(db.String(50))
+
+
+def __repr__(self):
+    return f"<Transaction {self.tran_id}>" 
+
+
+def to_dict(self):
+    return {
+        "tran_id": self.tran_id,
+        "cif_id": self.cif_id,
+        "foracid": self.foracid,
+        "acid": self.acid,
+        "tran_date": self.tran_date,
+        "value_date": self.value_date,
+        "pstd_date": self.pstd_date,
+        "tran_sub_type": self.tran_sub_type,
+        "part_tran_type": self.part_tran_type,
+        "tran_crncy_code": self.tran_crncy_code,
+        "tran_amt": float(self.tran_amt) if self.tran_amt is not None else None,
+        "tran_particular": self.tran_particular,
+        "origination_channel": self.origination_channel,
+        "reversal_tran_id": self.reversal_tran_id,
+        "isreversal": self.isreversal,
+    }
\ No newline at end of file
diff --git a/app/models/transaction_stg.py b/app/models/transaction_stg.py
new file mode 100644
index 0000000..9fa1939
--- /dev/null
+++ b/app/models/transaction_stg.py
@@ -0,0 +1,45 @@
+from app.extensions import db
+
+
+class TransactionStg(db.Model):
+    __tablename__ = "transaction_stg"
+
+    tran_id = db.Column(db.String(50), primary_key=True)
+    cif_id = db.Column(db.String(500))
+    foracid = db.Column(db.String(150))
+    acid = db.Column(db.String(150))
+    tran_date = db.Column(db.String(50))  # staging table keeps as string
+    value_date = db.Column(db.String(50))
+    pstd_date = db.Column(db.String(50))
+    tran_sub_type = db.Column(db.String(50))
+    part_tran_type = db.Column(db.String(50))
+    tran_crncy_code = db.Column(db.String(50))
+    tran_amt = db.Column(db.Numeric(38, 0))
+    tran_particular = db.Column(db.String(250))
+    origination_channel = db.Column(db.String(150))
+    reversal_tran_id = db.Column(db.String(50))
+    isreversal = db.Column(db.String(50))
+
+
+    def __repr__(self):
+        return f"<TransactionStg {self.tran_id}>"
+    
+
+    def to_dict(self):
+        return {
+            "tran_id": self.tran_id,
+            "cif_id": self.cif_id,
+            "foracid": self.foracid,
+            "acid": self.acid,
+            "tran_date": self.tran_date,
+            "value_date": self.value_date,
+            "pstd_date": self.pstd_date,
+            "tran_sub_type": self.tran_sub_type,
+            "part_tran_type": self.part_tran_type,
+            "tran_crncy_code": self.tran_crncy_code,
+            "tran_amt": float(self.tran_amt) if self.tran_amt is not None else None,
+            "tran_particular": self.tran_particular,
+            "origination_channel": self.origination_channel,
+            "reversal_tran_id": self.reversal_tran_id,
+            "isreversal": self.isreversal,
+        }
\ No newline at end of file
diff --git a/app/salary_analytics/__init__.py b/app/salary_analytics/__init__.py
new file mode 100644
index 0000000..62f8ac0
--- /dev/null
+++ b/app/salary_analytics/__init__.py
@@ -0,0 +1,38 @@
+from fastapi import FastAPI
+from app.salary_analytics.routes import analysis, reports, pipeline, load, base, train
+from app.salary_analytics.middlewares.middleware import add_middlewares
+from app.salary_analytics.events.lifecycle import register_events
+from app.utils.logger import logger
+import socket
+
+"""
+Salary Analytics Package
+A package for analyzing and predicting salary patterns from transaction data.
+"""
+
+__version__ = "0.1.0" 
+
+
+def create_app() -> FastAPI:
+    app = FastAPI(
+        title="Salary Analytics API",
+        description="API for analyzing and predicting salary patterns from transaction data",
+        version="1.0.0"
+    )
+
+    # Middlewares
+    add_middlewares(app)
+
+    # Events
+    register_events(app)
+
+    # Routers
+    app.include_router(base.router, tags=["Base"])
+    app.include_router(analysis.router, prefix="/analyze", tags=["Analysis"])
+    app.include_router(reports.router, tags=["Reports"])
+    app.include_router(pipeline.router, tags=["Pipeline"])
+    app.include_router(load.router, tags=["Data"])
+    app.include_router(train.router, tags=["Model Training"])
+
+
+    return app
diff --git a/app/salary_analytics/core/state.py b/app/salary_analytics/core/state.py
new file mode 100644
index 0000000..4e59c2f
--- /dev/null
+++ b/app/salary_analytics/core/state.py
@@ -0,0 +1,64 @@
+from app.salary_analytics.services.main import SalaryAnalyticsPipeline
+from app.salary_analytics.services.data_loader import DataLoader
+
+
+class GlobalState:
+    def __init__(self):
+        self._pipeline = None
+        self._data_loader = None
+        self.df = None
+        self.salary_predictor = None
+        self.salary_earner_analyzer = None
+
+    # ---- Pipeline ----
+    @property
+    def pipeline(self):
+        if self._pipeline is None:
+            self._pipeline = SalaryAnalyticsPipeline()
+        return self._pipeline
+
+    @pipeline.setter
+    def pipeline(self, value):
+        self._pipeline = value
+
+    # ---- Data Loader ----
+    @property
+    def data_loader(self):
+        if self._data_loader is None:
+            self._data_loader = DataLoader()
+        return self._data_loader
+
+    @data_loader.setter
+    def data_loader(self, value):
+        self._data_loader = value
+
+    # ---- DataFrame ----
+    @property
+    def df(self):
+        return self._df
+
+    @df.setter
+    def df(self, value):
+        self._df = value
+
+    # ---- Salary Predictor ----
+    @property
+    def salary_predictor(self):
+        return self._salary_predictor
+
+    @salary_predictor.setter
+    def salary_predictor(self, value):
+        self._salary_predictor = value
+
+    # ---- Salary Earner Analyzer ----
+    @property
+    def salary_earner_analyzer(self):
+        return self._salary_earner_analyzer
+
+    @salary_earner_analyzer.setter
+    def salary_earner_analyzer(self, value):
+        self._salary_earner_analyzer = value
+
+
+state = GlobalState()
+
diff --git a/app/salary_analytics/events/lifecycle.py b/app/salary_analytics/events/lifecycle.py
new file mode 100644
index 0000000..635ab73
--- /dev/null
+++ b/app/salary_analytics/events/lifecycle.py
@@ -0,0 +1,36 @@
+import socket
+from fastapi import FastAPI
+from app.salary_analytics.integrations.salary_detect import SalaryDetect
+from app.utils.logger import logger
+
+salary_detect = SalaryDetect()
+
+
+def register_events(app: FastAPI):
+    @app.on_event("startup")
+    async def startup_event():
+        """Initialize the pipeline on startup."""
+        try:
+            logger.info("Initializing pipeline...")
+            
+            # Start autonomous salary detection loop
+            salary_detect.start()
+            logger.info("Started autonomous salary detection loop.")
+            
+            # Print network information
+            hostname = socket.gethostname()
+            ip_address = socket.gethostbyname(hostname)
+            logger.info(f"Server running on hostname: {hostname}")
+            logger.info(f"Server IP address: {ip_address}")
+            logger.info(f"Server is accessible at:")
+            logger.info(f"- http://localhost:8000")
+            logger.info(f"- http://127.0.0.1:8000")
+            logger.info(f"- http://{ip_address}:8000")
+            logger.info("Pipeline initialized successfully")
+        except Exception as e:
+            logger.error(f"Error during startup: {str(e)}")
+            raise
+
+    @app.on_event("shutdown")
+    async def shutdown_event():
+        logger.info("Shutting down Salary Analytics API...")
diff --git a/app/salary_analytics/helpers/data_checks.py b/app/salary_analytics/helpers/data_checks.py
new file mode 100644
index 0000000..47caa4a
--- /dev/null
+++ b/app/salary_analytics/helpers/data_checks.py
@@ -0,0 +1,12 @@
+from fastapi import HTTPException
+from app.salary_analytics.core.state import state
+
+
+def check_data_loaded():
+    """Raise HTTP 400 if no data is loaded into the pipeline."""
+    if state.pipeline.df is None:
+        raise HTTPException(
+            status_code=400,
+            detail="No data loaded. Please load data first using the /load-data endpoint."
+        )
+    return True
diff --git a/app/salary_analytics/helpers/response_helpers.py b/app/salary_analytics/helpers/response_helpers.py
new file mode 100644
index 0000000..8485611
--- /dev/null
+++ b/app/salary_analytics/helpers/response_helpers.py
@@ -0,0 +1,17 @@
+from typing import Optional, Dict, List, Union
+from pydantic import BaseModel
+
+
+class AnalysisResponse(BaseModel):
+    """Response model for analysis endpoints."""
+    message: str
+    data: Optional[Dict] = None
+    file_path: Optional[str] = None
+
+class BatchResponse(BaseModel):
+    """Response model for batch processing."""
+    batch_number: int
+    total_batches: int
+    processed_rows: int
+    results_path: str
+    message: str
\ No newline at end of file
diff --git a/salary_analytics/rac_check.py b/app/salary_analytics/integrations/rac_check.py
similarity index 90%
rename from salary_analytics/rac_check.py
rename to app/salary_analytics/integrations/rac_check.py
index d8f16d3..86c21ff 100644
--- a/salary_analytics/rac_check.py
+++ b/app/salary_analytics/integrations/rac_check.py
@@ -1,10 +1,8 @@
 from django.conf import settings
 import httpx
 import json
-from salary_analytics.config import SIMBRELLA_BASE_URL, SIMBRELLA_ENDPOINT_RAC_CHECKS
-import logging
-
-logger = logging.getLogger(__name__)
+from app.config import SIMBRELLA_BASE_URL, SIMBRELLA_ENDPOINT_RAC_CHECKS
+from app.utils.logger import logger
 
 class SimbrellaIntegration:
     BASE_URL = SIMBRELLA_BASE_URL
diff --git a/salary_analytics/salary_detect.py b/app/salary_analytics/integrations/salary_detect.py
similarity index 85%
rename from salary_analytics/salary_detect.py
rename to app/salary_analytics/integrations/salary_detect.py
index 0824ae2..441dd02 100644
--- a/salary_analytics/salary_detect.py
+++ b/app/salary_analytics/integrations/salary_detect.py
@@ -1,11 +1,8 @@
 import time
-import logging
 import threading
 import requests
-from .config import SALARY_DETECT_URL, SALARY_DETECT_HEADERS, get_random_salary_payload
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+from app.config import SALARY_DETECT_URL, SALARY_DETECT_HEADERS, get_random_salary_payload
+from app.utils.logger import logger
 
 class SalaryDetect:
     def __init__(self):
diff --git a/app/salary_analytics/middlewares/middleware.py b/app/salary_analytics/middlewares/middleware.py
new file mode 100644
index 0000000..afd95a3
--- /dev/null
+++ b/app/salary_analytics/middlewares/middleware.py
@@ -0,0 +1,11 @@
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi import FastAPI
+
+def add_middlewares(app: FastAPI):
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
diff --git a/app/salary_analytics/routes/analysis.py b/app/salary_analytics/routes/analysis.py
new file mode 100644
index 0000000..e6c6688
--- /dev/null
+++ b/app/salary_analytics/routes/analysis.py
@@ -0,0 +1,75 @@
+from fastapi import APIRouter, HTTPException
+from app.salary_analytics.helpers.response_helpers import AnalysisResponse
+from app.salary_analytics.helpers.data_checks import check_data_loaded
+from app.utils.logger import logger
+import time
+from app.salary_analytics.core.state import state
+
+router = APIRouter()
+
+
+@router.post("/keyword", response_model=AnalysisResponse)
+async def analyze_keyword():
+    """Run keyword-based salary transaction analysis."""
+    start_time = time.time()
+    try:
+        check_data_loaded()
+        logger.info("Starting keyword analysis...")
+        data = state.pipeline.run_keyword_analysis()
+        logger.info(f"Keyword analysis completed. Found {len(data)} matches")
+        response = AnalysisResponse(
+            message="Keyword analysis completed successfully",
+            data={"count": len(data)}
+        )
+        logger.info(f"Keyword analysis endpoint completed in {time.time() - start_time:.2f} seconds")
+        return response
+    except Exception as e:
+        logger.error(f"Error in keyword analysis: {str(e)}")
+        logger.info(f"Keyword analysis endpoint failed after {time.time() - start_time:.2f} seconds")
+        raise HTTPException(status_code=500, detail=str(e))
+ 
+
+
+@router.post("/consistent-amount", response_model=AnalysisResponse)
+async def analyze_consistent_amount():
+    """Run consistent amount transaction analysis."""
+    start_time = time.time()
+    try:
+        check_data_loaded()
+        logger.info("Starting consistent amount analysis...")
+        data = state.pipeline.run_consistent_amount_analysis()
+        logger.info(f"Consistent amount analysis completed. Found {len(data)} matches")
+        response = AnalysisResponse(
+            message="Consistent amount analysis completed successfully",
+            data={"count": len(data)}
+        )
+        logger.info(f"Consistent amount analysis endpoint completed in {time.time() - start_time:.2f} seconds")
+        return response
+    except Exception as e:
+        logger.error(f"Error in consistent amount analysis: {str(e)}")
+        logger.info(f"Consistent amount analysis endpoint failed after {time.time() - start_time:.2f} seconds")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+
+
+@router.post("/transaction-type", response_model=AnalysisResponse)
+async def analyze_transaction_type():
+    """Run transaction type analysis."""
+    start_time = time.time()
+    try:
+        check_data_loaded()
+        logger.info("Starting transaction type analysis...")
+        data = state.pipeline.run_transaction_type_analysis()
+        logger.info(f"Transaction type analysis completed. Found {len(data)} matches")
+        response = AnalysisResponse(
+            message="Transaction type analysis completed successfully",
+            data={"count": len(data)}
+        )
+        logger.info(f"Transaction type analysis endpoint completed in {time.time() - start_time:.2f} seconds")
+        return response
+    except Exception as e:
+        logger.error(f"Error in transaction type analysis: {str(e)}")
+        logger.info(f"Transaction type analysis endpoint failed after {time.time() - start_time:.2f} seconds")
+        raise HTTPException(status_code=500, detail=str(e))
+
diff --git a/app/salary_analytics/routes/base.py b/app/salary_analytics/routes/base.py
new file mode 100644
index 0000000..c1319fb
--- /dev/null
+++ b/app/salary_analytics/routes/base.py
@@ -0,0 +1,28 @@
+from fastapi import APIRouter
+from app.utils.logger import logger
+import time
+
+
+router = APIRouter()
+
+@router.get("/")
+async def root():
+    """Root endpoint."""
+    start_time = time.time()
+    logger.info("Root endpoint accessed")
+    response = {"message": "Welcome to Salary Analytics API"}
+    logger.info(f"Root endpoint completed in {time.time() - start_time:.2f} seconds")
+    return response
+
+
+@router.get("/health")
+async def health_check():
+    """Health check endpoint."""
+    start_time = time.time()
+    logger.info("Health check endpoint accessed")
+    response = {"status": "healthy"}
+    logger.info(f"Health check completed in {time.time() - start_time:.2f} seconds")
+    return response
+
+
+
diff --git a/app/salary_analytics/routes/load.py b/app/salary_analytics/routes/load.py
new file mode 100644
index 0000000..ac6802c
--- /dev/null
+++ b/app/salary_analytics/routes/load.py
@@ -0,0 +1,74 @@
+from fastapi import APIRouter, HTTPException, UploadFile, File
+from app.salary_analytics.core.state import state
+from app.utils.logger import logger
+import tempfile, os, time
+from typing import Optional
+
+router = APIRouter()
+
+
+
+@router.post("/load-data")
+async def load_data(source: str = "db", file: Optional[UploadFile] = File(None)):
+    """
+    Load data from either database or CSV file.
+    
+    Args:
+        source (str): Source of data ('db' or 'csv')
+        file (UploadFile, optional): CSV file to load (required if source is 'csv')
+    
+    Returns:
+        dict: Status of data loading
+    """
+    start_time = time.time()
+    try:
+        if source not in ['db', 'csv']:
+            logger.error(f"Invalid source: {source}")
+            logger.info(f"Load data endpoint failed after {time.time() - start_time:.2f} seconds")
+            raise HTTPException(status_code=400, detail="Source must be either 'db' or 'csv'")
+        
+        if source == 'csv' and not file:
+            logger.error("No file provided for CSV source")
+            logger.info(f"Load data endpoint failed after {time.time() - start_time:.2f} seconds")
+            raise HTTPException(status_code=400, detail="File must be provided when loading from CSV")
+        
+        if source == 'csv':
+            # Save uploaded file temporarily
+            with tempfile.NamedTemporaryFile(delete=False, suffix='.csv') as temp_file:
+                content = await file.read()
+                temp_file.write(content)
+                temp_file_path = temp_file.name
+            
+            try:
+                success = state.pipeline.load_data(source='csv', file_path=temp_file_path)
+            finally:
+                # Clean up temporary file
+                os.unlink(temp_file_path)
+        else:
+            success = state.pipeline.load_data(source='db')
+        
+        if not success:
+            logger.error("Failed to load data")
+            logger.info(f"Load data endpoint failed after {time.time() - start_time:.2f} seconds")
+            raise HTTPException(status_code=500, detail="Failed to load data")
+        
+        response = {
+            "status": "success",
+            "message": f"Successfully loaded {len(state.pipeline.df)} rows of data",
+            "columns": state.pipeline.df.columns.tolist(),
+            "row_count": len(state.pipeline.df)
+        }
+        logger.info(f"Load data endpoint completed in {time.time() - start_time:.2f} seconds")
+        return response
+    except Exception as e:
+        logger.error(f"Error loading data: {str(e)}")
+        logger.info(f"Load data endpoint failed after {time.time() - start_time:.2f} seconds")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post("/load-data-with-file")
+async def get_file_if_csv(source: str, file: Optional[UploadFile] = File(None)):
+    """Dependency to handle file upload only when source is csv."""
+    if source == 'csv' and not file:
+        raise HTTPException(status_code=400, detail="File must be provided when loading from CSV")
+    return file
diff --git a/app/salary_analytics/routes/pipeline.py b/app/salary_analytics/routes/pipeline.py
new file mode 100644
index 0000000..db08518
--- /dev/null
+++ b/app/salary_analytics/routes/pipeline.py
@@ -0,0 +1,292 @@
+from fastapi import APIRouter, HTTPException
+from app.salary_analytics.services.main import SalaryAnalyticsPipeline
+from app.salary_analytics.helpers.response_helpers import AnalysisResponse, BatchResponse
+from app.salary_analytics.helpers.data_checks import check_data_loaded
+from app.salary_analytics.services.data_loader import DataLoader
+from app.salary_analytics.core.state import state
+from app.models.db_operations import DatabaseOperations
+from app.config import OUTPUT_PATHS, TABLE_NAME
+from app.utils.logger import logger
+from typing import Optional, List, Union
+from sqlalchemy import text
+from datetime import datetime
+import pandas as pd, os, tempfile, time
+from typing import Optional, Union
+from fastapi import UploadFile, File
+
+router = APIRouter()
+
+
+@router.post("/run/pipeline", response_model=AnalysisResponse)
+async def run_full_pipeline():
+    """Run the complete salary analytics pipeline."""
+    start_time = time.time()
+    try:
+        check_data_loaded()
+        logger.info("Starting full pipeline...")
+        success = state.pipeline.run_full_pipeline()
+        if not success:
+            logger.error("Pipeline failed")
+            logger.info(f"Full pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
+            raise HTTPException(status_code=500, detail="Pipeline failed")
+        
+        logger.info("Pipeline completed successfully")
+        response = AnalysisResponse(
+            message="Pipeline completed successfully"
+        )
+        logger.info(f"Full pipeline endpoint completed in {time.time() - start_time:.2f} seconds")
+        return response
+    except Exception as e:
+        logger.error(f"Error in pipeline: {str(e)}")
+        logger.info(f"Full pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post("/run/streaming-pipeline", response_model=List[BatchResponse])
+async def run_streaming_pipeline(
+    source: str = "db",
+    batch_size: int = 10000,
+    file: Optional[Union[UploadFile, str]] = File(None)
+):
+    """
+    Run the complete salary analytics pipeline in batches.
+    
+    Args:
+        source (str): Source of data ('db' or 'csv')
+        batch_size (int): Number of rows to process in each batch
+        file (UploadFile, optional): CSV file to load (required if source is 'csv')
+    
+    Returns:
+        List[BatchResponse]: List of responses for each batch processed
+    """
+    start_time = time.time()
+    try:
+        if source not in ['db', 'csv']:
+            logger.error(f"Invalid source: {source}")
+            logger.info(f"Streaming pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
+            raise HTTPException(status_code=400, detail="Source must be either 'db' or 'csv'")
+        
+        if source == 'csv' and not file:
+            logger.error("No file provided for CSV source")
+            logger.info(f"Streaming pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
+            raise HTTPException(status_code=400, detail="File must be provided when loading from CSV")
+        
+        # Initialize data loader
+        state.data_loader = DataLoader()
+        state.data_loader.chunk_size = batch_size
+        
+        # Create output directory for batch results
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        batch_output_dir = os.path.join(os.path.dirname(OUTPUT_PATHS['final_table']), f"batch_results_{timestamp}")
+        os.makedirs(batch_output_dir, exist_ok=True)
+        
+        # Initialize database operations
+        if not state.data_loader.connect():
+            logger.error("Failed to connect to database")
+            logger.info(f"Streaming pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
+            raise HTTPException(status_code=500, detail="Failed to connect to database")
+        
+        db_ops = DatabaseOperations(state.data_loader.engine)
+        if not db_ops.create_batch_results_table():
+            logger.error("Failed to create batch results table")
+            logger.info(f"Streaming pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
+            raise HTTPException(status_code=500, detail="Failed to create batch results table")
+        
+        responses = []
+        batch_number = 0
+        batch_start_time = time.time()
+
+        def preprocess_chunk(chunk):
+            """Preprocess a chunk of data with the same logic as DataLoader."""
+            # Convert dates
+            chunk['trx_start_date'] = pd.to_datetime(chunk['trx_start_date'])
+            chunk['trx_end_date'] = pd.to_datetime(chunk['trx_end_date'])
+            
+            # Rename columns
+            chunk = chunk.rename(columns={
+                'd1': 'trx_type',
+                'd2': 'trx_subtype',
+                'd3': 'initiated_by',
+                'd4': 'customer_id'
+            })
+            
+            chunk = chunk.dropna()
+            
+            return chunk
+        
+        if source == 'csv':
+            # Save uploaded file temporarily
+            with tempfile.NamedTemporaryFile(delete=False, suffix='.csv') as temp_file:
+                content = await file.read()
+                temp_file.write(content)
+                temp_file_path = temp_file.name
+            
+            try:
+                # Process CSV in chunks
+                for chunk in pd.read_csv(temp_file_path, chunksize=batch_size):
+                    batch_number += 1
+                    logger.info(f"Processing batch {batch_number}")
+                    
+                    # Preprocess chunk
+                    chunk = preprocess_chunk(chunk)
+                    
+                    # Run pipeline on chunk
+                    state.pipeline = SalaryAnalyticsPipeline()
+                    state.pipeline.df = chunk
+                    
+                    try:
+                        batch_start_time = time.time()
+                        # Run analyses
+                        state.pipeline.run_keyword_analysis()
+                        state.pipeline.run_consistent_amount_analysis()
+                        state.pipeline.run_transaction_type_analysis()
+                        
+                        # Generate reports
+                        reports = state.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'] = -1  # Unknown for CSV
+                        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=-1,  # Unknown for CSV
+                            results_df=results_df,
+                            status="success"
+                        )
+                        
+                        logger.info(f"Batch {batch_number} processed in {time.time() - batch_start_time:.2f} seconds")
+                        
+                        responses.append(BatchResponse(
+                            batch_number=batch_number,
+                            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 state.data_loader.connect():
+                raise HTTPException(status_code=500, detail="Failed to connect to database")
+            
+            # Get total row count
+            with state.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, state.data_loader.engine)
+                
+                if chunk.empty:
+                    break
+                
+                # Preprocess chunk
+                chunk = preprocess_chunk(chunk)
+                
+                # Run pipeline on chunk
+                pipeline = SalaryAnalyticsPipeline()
+                state.pipeline.df = chunk
+                
+                try:
+                    batch_start_time = time.time()
+                    # Run analyses
+                    state.pipeline.run_keyword_analysis()
+                    state.pipeline.run_consistent_amount_analysis()
+                    state.pipeline.run_transaction_type_analysis()
+                    
+                    # Generate reports
+                    reports = state.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)) 
+    
\ No newline at end of file
diff --git a/app/salary_analytics/routes/reports.py b/app/salary_analytics/routes/reports.py
new file mode 100644
index 0000000..24f9477
--- /dev/null
+++ b/app/salary_analytics/routes/reports.py
@@ -0,0 +1,78 @@
+from fastapi import APIRouter, HTTPException, BackgroundTasks
+from fastapi.responses import FileResponse
+from app.salary_analytics.helpers.response_helpers import AnalysisResponse
+from app.salary_analytics.helpers.data_checks import check_data_loaded
+from app.salary_analytics.core.state import state
+from app.config import OUTPUT_PATHS
+from app.utils.logger import logger
+import os, time
+
+router = APIRouter()
+
+
+@router.post("/generate/reports", response_model=AnalysisResponse)
+async def generate_reports(background_tasks: BackgroundTasks):
+    """Generate salary earner reports."""
+    start_time = time.time()
+    try:
+        check_data_loaded()
+        logger.info("Starting report generation...")
+        reports = state.pipeline.generate_salary_earner_reports()
+        logger.info("Reports generated successfully")
+        response = AnalysisResponse(
+            message="Reports generated successfully",
+            data={
+                "verified_salary_earners": len(reports['final_table']),
+                "likely_salary_earners": len(reports['likely_salary_earner']),
+                "high_earners": reports['total_high_earners']
+            }
+        )
+        logger.info(f"Report generation endpoint completed in {time.time() - start_time:.2f} seconds")
+        return response
+    except Exception as e:
+        logger.error(f"Error in report generation: {str(e)}")
+        logger.info(f"Report generation endpoint failed after {time.time() - start_time:.2f} seconds")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+
+@router.get("/download/{report_type}")
+async def download_report(report_type: str):
+    """Download generated reports."""
+    start_time = time.time()
+    try:
+        check_data_loaded()
+        logger.info(f"Attempting to download report: {report_type}")
+        file_paths = {
+            "high_earners": OUTPUT_PATHS["high_earner_details"],
+            "likely_earners": OUTPUT_PATHS["likely_salary_earner"],
+            "final_table": OUTPUT_PATHS["final_table"],
+            "consistent_plot": OUTPUT_PATHS["consistent_earners_plot"],
+            "inconsistent_plot": OUTPUT_PATHS["inconsistent_earners_plot"],
+            "hypothesis_plot": OUTPUT_PATHS["hypothesis_overlap_plot"]
+        }
+
+        if report_type not in file_paths:
+            logger.error(f"Report type not found: {report_type}")
+            logger.info(f"Download endpoint failed after {time.time() - start_time:.2f} seconds")
+            raise HTTPException(status_code=404, detail="Report type not found")
+
+        file_path = file_paths[report_type]
+        if not os.path.exists(file_path):
+            logger.error(f"Report file not found: {file_path}")
+            logger.info(f"Download endpoint failed after {time.time() - start_time:.2f} seconds")
+            raise HTTPException(status_code=404, detail="Report file not found")
+
+        logger.info(f"Successfully found report file: {file_path}")
+        response = FileResponse(
+            path=file_path,
+            filename=os.path.basename(file_path),
+            media_type="application/octet-stream"
+        )
+        logger.info(f"Download endpoint completed in {time.time() - start_time:.2f} seconds")
+        return response
+    except Exception as e:
+        logger.error(f"Error downloading report: {str(e)}")
+        logger.info(f"Download endpoint failed after {time.time() - start_time:.2f} seconds")
+        raise HTTPException(status_code=500, detail=str(e))
+
diff --git a/app/salary_analytics/routes/train.py b/app/salary_analytics/routes/train.py
new file mode 100644
index 0000000..94320cb
--- /dev/null
+++ b/app/salary_analytics/routes/train.py
@@ -0,0 +1,33 @@
+import time
+import logging
+from fastapi import APIRouter, HTTPException
+from app.salary_analytics.services.main import SalaryAnalyticsPipeline
+from app.salary_analytics.helpers.data_checks import check_data_loaded
+from app.salary_analytics.helpers.response_helpers import AnalysisResponse
+from app.salary_analytics.core.state import state
+from app.utils.logger import logger
+
+
+
+router = APIRouter()
+
+
+@router.post("/train/models", response_model=AnalysisResponse)
+async def train_models():
+    """Train salary prediction models."""
+    start_time = time.time()
+    try:
+        check_data_loaded()
+        logger.info("Starting model training...")
+        state.pipeline.train_salary_prediction_models()
+        logger.info("Models trained successfully")
+        response = AnalysisResponse(
+            message="Models trained successfully"
+        )
+        logger.info(f"Model training endpoint completed in {time.time() - start_time:.2f} seconds")
+        return response
+    except Exception as e:
+        logger.error(f"Error in model training: {str(e)}")
+        logger.info(f"Model training endpoint failed after {time.time() - start_time:.2f} seconds")
+        raise HTTPException(status_code=500, detail=str(e))
+
diff --git a/app/salary_analytics/services/__init__.py b/app/salary_analytics/services/__init__.py
new file mode 100644
index 0000000..0f844f8
--- /dev/null
+++ b/app/salary_analytics/services/__init__.py
@@ -0,0 +1,24 @@
+from .main import SalaryAnalyticsPipeline
+from .data_loader import DataLoader
+from .keyword_analyzer import KeywordAnalyzer
+from .consistent_amount_analyzer import ConsistentAmountAnalyzer
+from .transaction_type_analyzer import TransactionTypeAnalyzer
+from .salary_earner_analyzer import SalaryEarnerAnalyzer
+from .salary_predictor import SalaryPredictor
+
+
+"""
+Salary Analytics Package
+A package for analyzing and predicting salary patterns from transaction data.
+"""
+
+__version__ = "0.1.0" 
+__all__ = [
+    "SalaryAnalyticsPipeline",
+    "DataLoader",
+    "KeywordAnalyzer",
+    "ConsistentAmountAnalyzer",
+    "TransactionTypeAnalyzer",
+    "SalaryEarnerAnalyzer",
+    "SalaryPredictor"
+]
diff --git a/salary_analytics/consistent_amount_analyzer.py b/app/salary_analytics/services/consistent_amount_analyzer.py
similarity index 97%
rename from salary_analytics/consistent_amount_analyzer.py
rename to app/salary_analytics/services/consistent_amount_analyzer.py
index 3b74afc..119adae 100644
--- a/salary_analytics/consistent_amount_analyzer.py
+++ b/app/salary_analytics/services/consistent_amount_analyzer.py
@@ -3,7 +3,7 @@ Consistent amount transaction analysis module.
 """
 
 import pandas as pd
-from .config import MODEL_CONFIG
+from app.config import MODEL_CONFIG
 
 class ConsistentAmountAnalyzer:
     def __init__(self, df):
diff --git a/salary_analytics/data_loader.py b/app/salary_analytics/services/data_loader.py
similarity index 95%
rename from salary_analytics/data_loader.py
rename to app/salary_analytics/services/data_loader.py
index ec2da46..406608c 100644
--- a/salary_analytics/data_loader.py
+++ b/app/salary_analytics/services/data_loader.py
@@ -7,9 +7,8 @@ import pandas as pd
 from datetime import datetime
 import logging
 import os
-from .config import DB_CONFIG, TABLE_NAME
-
-logger = logging.getLogger(__name__)
+from app.config import SQLALCHEMY_DATABASE_URI, TABLE_NAME
+from app.utils.logger import logger
 
 class DataLoader:
     def __init__(self):
@@ -21,8 +20,7 @@ class DataLoader:
         """Establish database connection."""
         try:
             logger.info("Attempting to connect to database...")
-            DATABASE_URL = f"postgresql://{DB_CONFIG['user']}:{DB_CONFIG['password']}@{DB_CONFIG['host']}:{DB_CONFIG['port']}/{DB_CONFIG['name']}"
-            self.engine = create_engine(DATABASE_URL)
+            self.engine = create_engine(SQLALCHEMY_DATABASE_URI)
             with self.engine.connect() as conn:
                 # First check if table exists
                 check_table = text(f"SELECT EXISTS (SELECT FROM information_schema.tables WHERE table_name = '{TABLE_NAME}')")
diff --git a/salary_analytics/keyword_analyzer.py b/app/salary_analytics/services/keyword_analyzer.py
similarity index 96%
rename from salary_analytics/keyword_analyzer.py
rename to app/salary_analytics/services/keyword_analyzer.py
index 5c45b0f..b9db479 100644
--- a/salary_analytics/keyword_analyzer.py
+++ b/app/salary_analytics/services/keyword_analyzer.py
@@ -4,7 +4,7 @@ Keyword-based salary transaction analysis module.
 
 import re
 import pandas as pd
-from .config import SALARY_KEYWORDS
+from app.config import SALARY_KEYWORDS
 
 class KeywordAnalyzer:
     def __init__(self, df):
diff --git a/salary_analytics/main.py b/app/salary_analytics/services/main.py
similarity index 99%
rename from salary_analytics/main.py
rename to app/salary_analytics/services/main.py
index e2781e8..87482c7 100644
--- a/salary_analytics/main.py
+++ b/app/salary_analytics/services/main.py
@@ -9,8 +9,7 @@ from .consistent_amount_analyzer import ConsistentAmountAnalyzer
 from .transaction_type_analyzer import TransactionTypeAnalyzer
 from .salary_earner_analyzer import SalaryEarnerAnalyzer
 from .salary_predictor import SalaryPredictor
-
-logger = logging.getLogger(__name__)
+from app.utils.logger import logger
 
 class SalaryAnalyticsPipeline:
     def __init__(self):
diff --git a/salary_analytics/salary_earner_analyzer.py b/app/salary_analytics/services/salary_earner_analyzer.py
similarity index 96%
rename from salary_analytics/salary_earner_analyzer.py
rename to app/salary_analytics/services/salary_earner_analyzer.py
index b32c995..7302cbc 100644
--- a/salary_analytics/salary_earner_analyzer.py
+++ b/app/salary_analytics/services/salary_earner_analyzer.py
@@ -6,15 +6,8 @@ import pandas as pd
 import matplotlib.pyplot as plt
 from matplotlib_venn import venn3
 from datetime import datetime, timedelta
-import logging
-from .config import MODEL_CONFIG, OUTPUT_PATHS
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-)
-logger = logging.getLogger(__name__)
+from app.config import MODEL_CONFIG, OUTPUT_PATHS
+from app.utils.logger import logger
 
 class SalaryEarnerAnalyzer:
     def __init__(self, df):
diff --git a/salary_analytics/salary_predictor.py b/app/salary_analytics/services/salary_predictor.py
similarity index 99%
rename from salary_analytics/salary_predictor.py
rename to app/salary_analytics/services/salary_predictor.py
index b74dfd5..19fc531 100644
--- a/salary_analytics/salary_predictor.py
+++ b/app/salary_analytics/services/salary_predictor.py
@@ -9,7 +9,7 @@ from sklearn.preprocessing import StandardScaler, OneHotEncoder
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
 from joblib import dump
-from .config import OUTPUT_PATHS
+from app.config import OUTPUT_PATHS
 
 class SalaryPredictor:
     def __init__(self, df):
diff --git a/salary_analytics/transaction_type_analyzer.py b/app/salary_analytics/services/transaction_type_analyzer.py
similarity index 93%
rename from salary_analytics/transaction_type_analyzer.py
rename to app/salary_analytics/services/transaction_type_analyzer.py
index 2d1249c..0597aea 100644
--- a/salary_analytics/transaction_type_analyzer.py
+++ b/app/salary_analytics/services/transaction_type_analyzer.py
@@ -3,7 +3,7 @@ Transaction type analysis module.
 """
 
 import pandas as pd
-from .config import MODEL_CONFIG
+from app.config import MODEL_CONFIG
 
 class TransactionTypeAnalyzer:
     def __init__(self, df):
diff --git a/app/utils/logger.py b/app/utils/logger.py
new file mode 100644
index 0000000..4ee0e98
--- /dev/null
+++ b/app/utils/logger.py
@@ -0,0 +1,13 @@
+import logging
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(message)s",
+    handlers=[
+        # logging.StreamHandler(),  
+        logging.FileHandler("app.log", mode='a')  # Log to file
+    ]
+)
+
+logger = logging.getLogger("DetectionService")
diff --git a/docker-compose.yml b/docker-compose.yml
index b5a05e3..9205cb3 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -1,16 +1,17 @@
 services:
   digifi-analytics:
     build: .
+    env_file:
+      - .env
     ports:
       - "${APP_PORT:-4800}:8000"
-    volumes:
-      - ./output:/app/output
     environment:
-      - DB_USER=salaryloan
-      - DB_PASSWORD=salaryloan
-      - DB_NAME=salaryloan
-      - DB_PORT=10532
-      - DB_HOST=dev-data.simbrellang.net
+      - FLASK_APP=${FLASK_APP}
+      - FLASK_ENV=${FLASK_ENV}
+      - DATABASE_URL=postgresql+psycopg2://${DATABASE_USER}:${DATABASE_PASSWORD}@${DATABASE_HOST}:${DATABASE_PORT}/${DATABASE_NAME}
+    volumes:
+      - .:/app
+      - ./output:/app/output
     restart: unless-stopped
     networks:
       - salary_network
diff --git a/migrations/env.py b/migrations/env.py
index 79a3e8d..fa86225 100644
--- a/migrations/env.py
+++ b/migrations/env.py
@@ -19,7 +19,7 @@ if config.config_file_name is not None:
 # from myapp import Base
 # target_metadata = Base.metadata
 from flask import current_app
-from salary_analytics.app.extensions import db
+from app.extensions import db
 
 config.set_main_option('sqlalchemy.url',
                        current_app.config.get('SQLALCHEMY_DATABASE_URI'))
diff --git a/requirements.txt b/requirements.txt
index 68fa9fc..461ceb2 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,8 @@
+# Database and ORM
 sqlalchemy>=2.0.0
+oracledb>=1.0.0
+
+
 pandas>=1.5.0
 numpy>=1.21.0
 matplotlib>=3.5.0
@@ -17,4 +21,6 @@ openpyxl>=3.0.10
 Flask>=2.0.0
 Flask-SQLAlchemy>=3.0.0
 Flask-Migrate>=4.0.0
-alembic>=1.8.0
\ No newline at end of file
+alembic>=1.8.0
+requests>=2.26.0
+gunicorn
\ No newline at end of file
diff --git a/run.py b/run.py
index 77e5bde..c4932c4 100644
--- a/run.py
+++ b/run.py
@@ -1,4 +1,4 @@
 import os
-from salary_analytics.app import create_app
+from app.salary_analytics import create_app
 
 app = create_app() 
\ No newline at end of file
diff --git a/salary_analytics/__init__.py b/salary_analytics/__init__.py
deleted file mode 100644
index 1825412..0000000
--- a/salary_analytics/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""
-Salary Analytics Package
-A package for analyzing and predicting salary patterns from transaction data.
-"""
-
-__version__ = "0.1.0" 
\ No newline at end of file
diff --git a/salary_analytics/api.py b/salary_analytics/api.py
deleted file mode 100644
index cb41a30..0000000
--- a/salary_analytics/api.py
+++ /dev/null
@@ -1,605 +0,0 @@
-"""
-FastAPI application for salary analytics.
-"""
-
-from fastapi import FastAPI, HTTPException, BackgroundTasks, UploadFile, File, Depends
-from fastapi.responses import FileResponse
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel
-from typing import Optional, Dict, List, Union
-import os
-import socket
-import logging
-import pandas as pd
-import tempfile
-from datetime import datetime
-from sqlalchemy import text, Table, Column, Integer, String, Float, DateTime, MetaData
-import numpy as np
-import warnings
-import time
-from .main import SalaryAnalyticsPipeline
-from .config import OUTPUT_PATHS, TABLE_NAME, BATCH_RESULTS_TABLE
-from .data_loader import DataLoader
-from .salary_predictor import SalaryPredictor
-from .salary_earner_analyzer import SalaryEarnerAnalyzer
-from .db_operations import DatabaseOperations
-from .salary_detect import SalaryDetect
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-)
-logger = logging.getLogger(__name__)
-
-# Suppress warnings
-warnings.filterwarnings('ignore', category=RuntimeWarning, module='numpy')
-pd.options.mode.chained_assignment = None
-
-app = FastAPI(
-    title="Salary Analytics API",
-    description="API for analyzing and predicting salary patterns from transaction data",
-    version="1.0.0"
-)
-
-# Add CORS middleware
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # Allows all origins
-    allow_credentials=True,
-    allow_methods=["*"],  # Allows all methods
-    allow_headers=["*"],  # Allows all headers
-)
-
-# Global pipeline instance
-pipeline = SalaryAnalyticsPipeline()
-
-# Global variables to store loaded data and models
-data_loader = None
-df = None
-salary_predictor = None
-salary_earner_analyzer = None
-
-salary_detect = SalaryDetect()
-
-class AnalysisResponse(BaseModel):
-    """Response model for analysis endpoints."""
-    message: str
-    data: Optional[Dict] = None
-    file_path: Optional[str] = None
-
-class BatchResponse(BaseModel):
-    """Response model for batch processing."""
-    batch_number: int
-    total_batches: int
-    processed_rows: int
-    results_path: str
-    message: str
-
-def check_data_loaded():
-    """Check if data is loaded before running analytics."""
-    if pipeline.df is None:
-        raise HTTPException(
-            status_code=400,
-            detail="No data loaded. Please load data first using the /load-data endpoint."
-        )
-
-@app.on_event("startup")
-async def startup_event():
-    """Initialize the pipeline on startup."""
-    try:
-        logger.info("Initializing pipeline...")
-        
-        # Start autonomous salary detection loop
-        salary_detect.start()
-        logger.info("Started autonomous salary detection loop.")
-        
-        # Print network information
-        hostname = socket.gethostname()
-        ip_address = socket.gethostbyname(hostname)
-        logger.info(f"Server running on hostname: {hostname}")
-        logger.info(f"Server IP address: {ip_address}")
-        logger.info(f"Server is accessible at:")
-        logger.info(f"- http://localhost:8000")
-        logger.info(f"- http://127.0.0.1:8000")
-        logger.info(f"- http://{ip_address}:8000")
-        logger.info("Pipeline initialized successfully")
-    except Exception as e:
-        logger.error(f"Error during startup: {str(e)}")
-        raise
-
-@app.get("/")
-async def root():
-    """Root endpoint."""
-    start_time = time.time()
-    logger.info("Root endpoint accessed")
-    response = {"message": "Welcome to Salary Analytics API"}
-    logger.info(f"Root endpoint completed in {time.time() - start_time:.2f} seconds")
-    return response
-
-@app.get("/health")
-async def health_check():
-    """Health check endpoint."""
-    start_time = time.time()
-    logger.info("Health check endpoint accessed")
-    response = {"status": "healthy"}
-    logger.info(f"Health check completed in {time.time() - start_time:.2f} seconds")
-    return response
-
-@app.post("/analyze/keyword", response_model=AnalysisResponse)
-async def analyze_keyword():
-    """Run keyword-based salary transaction analysis."""
-    start_time = time.time()
-    try:
-        check_data_loaded()
-        logger.info("Starting keyword analysis...")
-        data = pipeline.run_keyword_analysis()
-        logger.info(f"Keyword analysis completed. Found {len(data)} matches")
-        response = AnalysisResponse(
-            message="Keyword analysis completed successfully",
-            data={"count": len(data)}
-        )
-        logger.info(f"Keyword analysis endpoint completed in {time.time() - start_time:.2f} seconds")
-        return response
-    except Exception as e:
-        logger.error(f"Error in keyword analysis: {str(e)}")
-        logger.info(f"Keyword analysis endpoint failed after {time.time() - start_time:.2f} seconds")
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/analyze/consistent-amount", response_model=AnalysisResponse)
-async def analyze_consistent_amount():
-    """Run consistent amount transaction analysis."""
-    start_time = time.time()
-    try:
-        check_data_loaded()
-        logger.info("Starting consistent amount analysis...")
-        data = pipeline.run_consistent_amount_analysis()
-        logger.info(f"Consistent amount analysis completed. Found {len(data)} matches")
-        response = AnalysisResponse(
-            message="Consistent amount analysis completed successfully",
-            data={"count": len(data)}
-        )
-        logger.info(f"Consistent amount analysis endpoint completed in {time.time() - start_time:.2f} seconds")
-        return response
-    except Exception as e:
-        logger.error(f"Error in consistent amount analysis: {str(e)}")
-        logger.info(f"Consistent amount analysis endpoint failed after {time.time() - start_time:.2f} seconds")
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/analyze/transaction-type", response_model=AnalysisResponse)
-async def analyze_transaction_type():
-    """Run transaction type analysis."""
-    start_time = time.time()
-    try:
-        check_data_loaded()
-        logger.info("Starting transaction type analysis...")
-        data = pipeline.run_transaction_type_analysis()
-        logger.info(f"Transaction type analysis completed. Found {len(data)} matches")
-        response = AnalysisResponse(
-            message="Transaction type analysis completed successfully",
-            data={"count": len(data)}
-        )
-        logger.info(f"Transaction type analysis endpoint completed in {time.time() - start_time:.2f} seconds")
-        return response
-    except Exception as e:
-        logger.error(f"Error in transaction type analysis: {str(e)}")
-        logger.info(f"Transaction type analysis endpoint failed after {time.time() - start_time:.2f} seconds")
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/generate/reports", response_model=AnalysisResponse)
-async def generate_reports(background_tasks: BackgroundTasks):
-    """Generate salary earner reports."""
-    start_time = time.time()
-    try:
-        check_data_loaded()
-        logger.info("Starting report generation...")
-        reports = pipeline.generate_salary_earner_reports()
-        logger.info("Reports generated successfully")
-        response = AnalysisResponse(
-            message="Reports generated successfully",
-            data={
-                "verified_salary_earners": len(reports['final_table']),
-                "likely_salary_earners": len(reports['likely_salary_earner']),
-                "high_earners": reports['total_high_earners']
-            }
-        )
-        logger.info(f"Report generation endpoint completed in {time.time() - start_time:.2f} seconds")
-        return response
-    except Exception as e:
-        logger.error(f"Error in report generation: {str(e)}")
-        logger.info(f"Report generation endpoint failed after {time.time() - start_time:.2f} seconds")
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/train/models", response_model=AnalysisResponse)
-async def train_models():
-    """Train salary prediction models."""
-    start_time = time.time()
-    try:
-        check_data_loaded()
-        logger.info("Starting model training...")
-        pipeline.train_salary_prediction_models()
-        logger.info("Models trained successfully")
-        response = AnalysisResponse(
-            message="Models trained successfully"
-        )
-        logger.info(f"Model training endpoint completed in {time.time() - start_time:.2f} seconds")
-        return response
-    except Exception as e:
-        logger.error(f"Error in model training: {str(e)}")
-        logger.info(f"Model training endpoint failed after {time.time() - start_time:.2f} seconds")
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.get("/download/{report_type}")
-async def download_report(report_type: str):
-    """Download generated reports."""
-    start_time = time.time()
-    try:
-        check_data_loaded()
-        logger.info(f"Attempting to download report: {report_type}")
-        file_paths = {
-            "high_earners": OUTPUT_PATHS["high_earner_details"],
-            "likely_earners": OUTPUT_PATHS["likely_salary_earner"],
-            "final_table": OUTPUT_PATHS["final_table"],
-            "consistent_plot": OUTPUT_PATHS["consistent_earners_plot"],
-            "inconsistent_plot": OUTPUT_PATHS["inconsistent_earners_plot"],
-            "hypothesis_plot": OUTPUT_PATHS["hypothesis_overlap_plot"]
-        }
-
-        if report_type not in file_paths:
-            logger.error(f"Report type not found: {report_type}")
-            logger.info(f"Download endpoint failed after {time.time() - start_time:.2f} seconds")
-            raise HTTPException(status_code=404, detail="Report type not found")
-
-        file_path = file_paths[report_type]
-        if not os.path.exists(file_path):
-            logger.error(f"Report file not found: {file_path}")
-            logger.info(f"Download endpoint failed after {time.time() - start_time:.2f} seconds")
-            raise HTTPException(status_code=404, detail="Report file not found")
-
-        logger.info(f"Successfully found report file: {file_path}")
-        response = FileResponse(
-            path=file_path,
-            filename=os.path.basename(file_path),
-            media_type="application/octet-stream"
-        )
-        logger.info(f"Download endpoint completed in {time.time() - start_time:.2f} seconds")
-        return response
-    except Exception as e:
-        logger.error(f"Error downloading report: {str(e)}")
-        logger.info(f"Download endpoint failed after {time.time() - start_time:.2f} seconds")
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/run/pipeline", response_model=AnalysisResponse)
-async def run_full_pipeline():
-    """Run the complete salary analytics pipeline."""
-    start_time = time.time()
-    try:
-        check_data_loaded()
-        logger.info("Starting full pipeline...")
-        success = pipeline.run_full_pipeline()
-        if not success:
-            logger.error("Pipeline failed")
-            logger.info(f"Full pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
-            raise HTTPException(status_code=500, detail="Pipeline failed")
-        
-        logger.info("Pipeline completed successfully")
-        response = AnalysisResponse(
-            message="Pipeline completed successfully"
-        )
-        logger.info(f"Full pipeline endpoint completed in {time.time() - start_time:.2f} seconds")
-        return response
-    except Exception as e:
-        logger.error(f"Error in pipeline: {str(e)}")
-        logger.info(f"Full pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.post("/load-data")
-async def load_data(source: str = "db", file: Optional[UploadFile] = File(None)):
-    """
-    Load data from either database or CSV file.
-    
-    Args:
-        source (str): Source of data ('db' or 'csv')
-        file (UploadFile, optional): CSV file to load (required if source is 'csv')
-    
-    Returns:
-        dict: Status of data loading
-    """
-    start_time = time.time()
-    try:
-        if source not in ['db', 'csv']:
-            logger.error(f"Invalid source: {source}")
-            logger.info(f"Load data endpoint failed after {time.time() - start_time:.2f} seconds")
-            raise HTTPException(status_code=400, detail="Source must be either 'db' or 'csv'")
-        
-        if source == 'csv' and not file:
-            logger.error("No file provided for CSV source")
-            logger.info(f"Load data endpoint failed after {time.time() - start_time:.2f} seconds")
-            raise HTTPException(status_code=400, detail="File must be provided when loading from CSV")
-        
-        if source == 'csv':
-            # Save uploaded file temporarily
-            with tempfile.NamedTemporaryFile(delete=False, suffix='.csv') as temp_file:
-                content = await file.read()
-                temp_file.write(content)
-                temp_file_path = temp_file.name
-            
-            try:
-                success = pipeline.load_data(source='csv', file_path=temp_file_path)
-            finally:
-                # Clean up temporary file
-                os.unlink(temp_file_path)
-        else:
-            success = pipeline.load_data(source='db')
-        
-        if not success:
-            logger.error("Failed to load data")
-            logger.info(f"Load data endpoint failed after {time.time() - start_time:.2f} seconds")
-            raise HTTPException(status_code=500, detail="Failed to load data")
-        
-        response = {
-            "status": "success",
-            "message": f"Successfully loaded {len(pipeline.df)} rows of data",
-            "columns": pipeline.df.columns.tolist(),
-            "row_count": len(pipeline.df)
-        }
-        logger.info(f"Load data endpoint completed in {time.time() - start_time:.2f} seconds")
-        return response
-    except Exception as e:
-        logger.error(f"Error loading data: {str(e)}")
-        logger.info(f"Load data endpoint failed after {time.time() - start_time:.2f} seconds")
-        raise HTTPException(status_code=500, detail=str(e))
-
-async def get_file_if_csv(source: str, file: Optional[UploadFile] = File(None)):
-    """Dependency to handle file upload only when source is csv."""
-    if source == 'csv' and not file:
-        raise HTTPException(status_code=400, detail="File must be provided when loading from CSV")
-    return file
-
-@app.post("/run/streaming-pipeline", response_model=List[BatchResponse])
-async def run_streaming_pipeline(
-    source: str = "db",
-    batch_size: int = 10000,
-    file: Optional[Union[UploadFile, str]] = File(None)
-):
-    """
-    Run the complete salary analytics pipeline in batches.
-    
-    Args:
-        source (str): Source of data ('db' or 'csv')
-        batch_size (int): Number of rows to process in each batch
-        file (UploadFile, optional): CSV file to load (required if source is 'csv')
-    
-    Returns:
-        List[BatchResponse]: List of responses for each batch processed
-    """
-    start_time = time.time()
-    try:
-        if source not in ['db', 'csv']:
-            logger.error(f"Invalid source: {source}")
-            logger.info(f"Streaming pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
-            raise HTTPException(status_code=400, detail="Source must be either 'db' or 'csv'")
-        
-        if source == 'csv' and not file:
-            logger.error("No file provided for CSV source")
-            logger.info(f"Streaming pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
-            raise HTTPException(status_code=400, detail="File must be provided when loading from CSV")
-        
-        # Initialize data loader
-        data_loader = DataLoader()
-        data_loader.chunk_size = batch_size
-        
-        # Create output directory for batch results
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        batch_output_dir = os.path.join(os.path.dirname(OUTPUT_PATHS['final_table']), f"batch_results_{timestamp}")
-        os.makedirs(batch_output_dir, exist_ok=True)
-        
-        # Initialize database operations
-        if not data_loader.connect():
-            logger.error("Failed to connect to database")
-            logger.info(f"Streaming pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
-            raise HTTPException(status_code=500, detail="Failed to connect to database")
-        
-        db_ops = DatabaseOperations(data_loader.engine)
-        if not db_ops.create_batch_results_table():
-            logger.error("Failed to create batch results table")
-            logger.info(f"Streaming pipeline endpoint failed after {time.time() - start_time:.2f} seconds")
-            raise HTTPException(status_code=500, detail="Failed to create batch results table")
-        
-        responses = []
-        batch_number = 0
-        batch_start_time = time.time()
-
-        def preprocess_chunk(chunk):
-            """Preprocess a chunk of data with the same logic as DataLoader."""
-            # Convert dates
-            chunk['trx_start_date'] = pd.to_datetime(chunk['trx_start_date'])
-            chunk['trx_end_date'] = pd.to_datetime(chunk['trx_end_date'])
-            
-            # Rename columns
-            chunk = chunk.rename(columns={
-                'd1': 'trx_type',
-                'd2': 'trx_subtype',
-                'd3': 'initiated_by',
-                'd4': 'customer_id'
-            })
-            
-            chunk = chunk.dropna()
-            
-            return chunk
-        
-        if source == 'csv':
-            # Save uploaded file temporarily
-            with tempfile.NamedTemporaryFile(delete=False, suffix='.csv') as temp_file:
-                content = await file.read()
-                temp_file.write(content)
-                temp_file_path = temp_file.name
-            
-            try:
-                # Process CSV in chunks
-                for chunk in pd.read_csv(temp_file_path, chunksize=batch_size):
-                    batch_number += 1
-                    logger.info(f"Processing batch {batch_number}")
-                    
-                    # 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'] = -1  # Unknown for CSV
-                        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=-1,  # Unknown for CSV
-                            results_df=results_df,
-                            status="success"
-                        )
-                        
-                        logger.info(f"Batch {batch_number} processed in {time.time() - batch_start_time:.2f} seconds")
-                        
-                        responses.append(BatchResponse(
-                            batch_number=batch_number,
-                            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)) 
\ No newline at end of file
diff --git a/wsgi.py b/wsgi.py
new file mode 100644
index 0000000..49f70e1
--- /dev/null
+++ b/wsgi.py
@@ -0,0 +1,7 @@
+from app import create_app
+
+app = create_app()
+
+if __name__ != "__main__":
+    # Expose WSGI app instance for Gunicorn
+    wsgi_app = app