[add]: database configuration fix
This commit is contained in:
VivianDee
+1
-1
@@ -17,7 +17,7 @@ RUN mkdir -p output/csv output/plots output/models
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# ENV FLASK_APP=wsgi.py
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ENV FLASK_APP=run.py
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ENV FLASK_APP=run.py.
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ENV FLASK_RUN_HOST=0.0.0.0
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EXPOSE 8000
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@@ -29,3 +29,185 @@
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2025-09-09 10:25:42,100 - INFO - [2025-09-09 10:25:42] Salary detection complete
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2025-09-09 10:27:03,741 - INFO - Shutting down Salary Analytics API...
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2025-09-09 10:29:59,503 - INFO - Initializing pipeline...
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2025-09-09 10:29:59,506 - INFO - [2025-09-09 10:29:59] Detecting salary...
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2025-09-09 10:29:59,506 - INFO - Started autonomous salary detection loop.
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2025-09-09 10:29:59,534 - INFO - Server running on hostname: 1c3f3ceb2429
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2025-09-09 10:29:59,535 - INFO - Server IP address: 172.25.0.2
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2025-09-09 10:29:59,535 - INFO - Server is accessible at:
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2025-09-09 10:29:59,536 - INFO - - http://localhost:8000
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2025-09-09 10:29:59,537 - INFO - - http://127.0.0.1:8000
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2025-09-09 10:29:59,539 - INFO - - http://172.25.0.2:8000
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2025-09-09 10:29:59,541 - INFO - Pipeline initialized successfully
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2025-09-09 10:30:04,484 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
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"data": [],
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"error": {},
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"message": "AutoCall Add Salary Successful",
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"status": true,
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"statusCode": 200
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}
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2025-09-09 10:30:04,485 - INFO - [2025-09-09 10:30:04] Salary detection complete
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2025-09-09 10:30:47,978 - INFO - Shutting down Salary Analytics API...
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2025-09-09 10:41:41,451 - INFO - Initializing pipeline...
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2025-09-09 10:41:41,456 - INFO - [2025-09-09 10:41:41] Detecting salary...
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2025-09-09 10:41:41,457 - INFO - Started autonomous salary detection loop.
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2025-09-09 10:41:41,481 - INFO - Server running on hostname: 1c3f3ceb2429
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2025-09-09 10:41:41,485 - INFO - Server IP address: 172.25.0.2
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2025-09-09 10:41:41,486 - INFO - Server is accessible at:
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2025-09-09 10:41:41,486 - INFO - - http://localhost:8000
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2025-09-09 10:41:41,488 - INFO - - http://127.0.0.1:8000
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2025-09-09 10:41:41,490 - INFO - - http://172.25.0.2:8000
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2025-09-09 10:41:41,491 - INFO - Pipeline initialized successfully
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2025-09-09 10:41:42,431 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
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"data": [],
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"error": {},
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"message": "AutoCall Add Salary Successful",
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"status": true,
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"statusCode": 200
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}
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2025-09-09 10:41:42,432 - INFO - [2025-09-09 10:41:42] Salary detection complete
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2025-09-09 10:43:42,431 - INFO - [2025-09-09 10:43:42] Detecting salary...
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2025-09-09 10:43:43,092 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
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"data": [],
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"error": {},
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"message": "AutoCall Add Salary Successful",
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"status": true,
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"statusCode": 200
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}
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2025-09-09 10:43:43,093 - INFO - [2025-09-09 10:43:43] Salary detection complete
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2025-09-09 10:45:43,093 - INFO - [2025-09-09 10:45:43] Detecting salary...
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2025-09-09 10:45:43,818 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
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"data": [],
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"error": {},
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"message": "AutoCall Add Salary Successful",
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"status": true,
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"statusCode": 200
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}
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2025-09-09 10:45:43,819 - INFO - [2025-09-09 10:45:43] Salary detection complete
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2025-09-09 10:47:16,454 - INFO - Shutting down Salary Analytics API...
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2025-09-09 10:47:30,172 - INFO - Initializing pipeline...
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2025-09-09 10:47:30,174 - INFO - [2025-09-09 10:47:30] Detecting salary...
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2025-09-09 10:47:30,175 - INFO - Started autonomous salary detection loop.
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2025-09-09 10:47:30,185 - INFO - Server running on hostname: 1c3f3ceb2429
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2025-09-09 10:47:30,188 - INFO - Server IP address: 172.25.0.2
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2025-09-09 10:47:30,188 - INFO - Server is accessible at:
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2025-09-09 10:47:30,189 - INFO - - http://localhost:8000
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2025-09-09 10:47:30,190 - INFO - - http://127.0.0.1:8000
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2025-09-09 10:47:30,191 - INFO - - http://172.25.0.2:8000
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2025-09-09 10:47:30,191 - INFO - Pipeline initialized successfully
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2025-09-09 10:47:31,032 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
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"data": [],
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"error": {},
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"message": "AutoCall Add Salary Successful",
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"status": true,
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"statusCode": 200
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}
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2025-09-09 10:47:31,033 - INFO - [2025-09-09 10:47:31] Salary detection complete
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2025-09-09 10:47:38,286 - INFO - Shutting down Salary Analytics API...
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2025-09-09 10:47:47,645 - INFO - generated new fontManager
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2025-09-09 10:48:19,231 - INFO - generated new fontManager
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2025-09-09 10:48:24,426 - INFO - Initializing pipeline...
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2025-09-09 10:48:24,429 - INFO - [2025-09-09 10:48:24] Detecting salary...
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2025-09-09 10:48:24,429 - INFO - Started autonomous salary detection loop.
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2025-09-09 10:48:24,441 - INFO - Server running on hostname: 349f9fd0c78b
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2025-09-09 10:48:24,442 - INFO - Server IP address: 172.25.0.2
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2025-09-09 10:48:24,444 - INFO - Server is accessible at:
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2025-09-09 10:48:24,445 - INFO - - http://localhost:8000
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2025-09-09 10:48:24,448 - INFO - - http://127.0.0.1:8000
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2025-09-09 10:48:24,450 - INFO - - http://172.25.0.2:8000
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2025-09-09 10:48:24,451 - INFO - Pipeline initialized successfully
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2025-09-09 10:48:25,094 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
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"data": [],
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"error": {},
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"message": "AutoCall Add Salary Successful",
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"status": true,
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"statusCode": 200
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}
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2025-09-09 10:48:25,095 - INFO - [2025-09-09 10:48:25] Salary detection complete
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2025-09-09 10:49:03,380 - INFO - Shutting down Salary Analytics API...
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2025-09-09 10:49:18,345 - INFO - Initializing pipeline...
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2025-09-09 10:49:18,346 - INFO - [2025-09-09 10:49:18] Detecting salary...
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2025-09-09 10:49:18,347 - INFO - Started autonomous salary detection loop.
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2025-09-09 10:49:18,352 - INFO - Server running on hostname: 349f9fd0c78b
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2025-09-09 10:49:18,353 - INFO - Server IP address: 172.25.0.2
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2025-09-09 10:49:18,353 - INFO - Server is accessible at:
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2025-09-09 10:49:18,354 - INFO - - http://localhost:8000
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2025-09-09 10:49:18,355 - INFO - - http://127.0.0.1:8000
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2025-09-09 10:49:18,365 - INFO - - http://172.25.0.2:8000
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2025-09-09 10:49:18,366 - INFO - Pipeline initialized successfully
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2025-09-09 10:50:37,994 - INFO - generated new fontManager
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2025-09-09 10:50:45,235 - INFO - Initializing pipeline...
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2025-09-09 10:50:45,238 - INFO - [2025-09-09 10:50:45] Detecting salary...
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2025-09-09 10:50:45,238 - INFO - Started autonomous salary detection loop.
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2025-09-09 10:50:45,244 - INFO - Server running on hostname: 087fb63cb9f0
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2025-09-09 10:50:45,244 - INFO - Server IP address: 172.25.0.2
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2025-09-09 10:50:45,245 - INFO - Server is accessible at:
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2025-09-09 10:50:45,245 - INFO - - http://localhost:8000
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2025-09-09 10:50:45,246 - INFO - - http://127.0.0.1:8000
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2025-09-09 10:50:45,247 - INFO - - http://172.25.0.2:8000
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2025-09-09 10:50:45,248 - INFO - Pipeline initialized successfully
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2025-09-09 10:50:46,400 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 200, response: {
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"data": [],
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"error": {},
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"message": "AutoCall Add Salary Successful",
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"status": true,
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"statusCode": 200
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}
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2025-09-09 10:50:46,401 - INFO - [2025-09-09 10:50:46] Salary detection complete
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2025-09-09 10:51:51,570 - INFO - Shutting down Salary Analytics API...
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2025-09-09 11:01:38,522 - INFO - generated new fontManager
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2025-09-09 11:01:45,459 - INFO - Initializing pipeline...
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2025-09-09 11:01:45,463 - INFO - [2025-09-09 11:01:45] Detecting salary...
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2025-09-09 11:01:45,464 - INFO - Started autonomous salary detection loop.
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2025-09-09 11:01:45,483 - INFO - Server running on hostname: 5d4fdd4232a7
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2025-09-09 11:01:45,484 - INFO - Server IP address: 172.25.0.2
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2025-09-09 11:01:45,485 - INFO - Server is accessible at:
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2025-09-09 11:01:45,491 - INFO - - http://localhost:8000
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2025-09-09 11:01:45,493 - INFO - - http://127.0.0.1:8000
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2025-09-09 11:01:45,495 - INFO - - http://172.25.0.2:8000
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2025-09-09 11:01:45,496 - INFO - Pipeline initialized successfully
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2025-09-09 11:02:00,358 - INFO - Shutting down Salary Analytics API...
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2025-09-09 11:02:15,204 - INFO - Initializing pipeline...
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2025-09-09 11:02:15,208 - INFO - [2025-09-09 11:02:15] Detecting salary...
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2025-09-09 11:02:15,208 - INFO - Started autonomous salary detection loop.
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2025-09-09 11:02:15,395 - INFO - Server running on hostname: 5d4fdd4232a7
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2025-09-09 11:02:15,397 - INFO - Server IP address: 172.25.0.2
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2025-09-09 11:02:15,415 - INFO - Server is accessible at:
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2025-09-09 11:02:15,417 - INFO - - http://localhost:8000
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2025-09-09 11:02:15,417 - INFO - - http://127.0.0.1:8000
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2025-09-09 11:02:15,418 - INFO - - http://172.25.0.2:8000
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2025-09-09 11:02:15,419 - INFO - Pipeline initialized successfully
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2025-09-09 11:04:18,780 - INFO - POST http://www.simbrellang.net:5000/autocall/analytic-salary-detect status: 500, response: <html>
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<head>
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<title>Internal Server Error</title>
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</head>
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<body>
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<h1><p>Internal Server Error</p></h1>
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</body>
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</html>
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2025-09-09 11:04:18,781 - INFO - [2025-09-09 11:04:18] Salary detection complete
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2025-09-09 11:04:41,264 - INFO - Initializing SalaryAnalyticsPipeline
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2025-09-09 11:04:41,265 - INFO - Starting data loading process
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2025-09-09 11:04:41,265 - INFO - No database connection. Attempting to connect...
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2025-09-09 11:04:41,266 - INFO - Attempting to connect to database...
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2025-09-09 11:05:42,201 - ERROR - Error connecting to database: (psycopg2.OperationalError) connection to server at "dev-data.simbrellang.net" (209.195.2.27), port 1521 failed: server closed the connection unexpectedly
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This probably means the server terminated abnormally
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before or while processing the request.
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(Background on this error at: https://sqlalche.me/e/20/e3q8)
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2025-09-09 11:05:42,202 - ERROR - Failed to establish database connection
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2025-09-09 11:05:42,202 - ERROR - Failed to load data
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2025-09-09 11:05:42,203 - ERROR - Failed to load data
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2025-09-09 11:05:42,203 - INFO - Load data endpoint failed after 60.94 seconds
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2025-09-09 11:05:42,204 - ERROR - Error loading data: 500: Failed to load data
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2025-09-09 11:05:42,206 - INFO - Load data endpoint failed after 60.94 seconds
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2025-09-09 11:06:18,783 - INFO - [2025-09-09 11:06:18] Detecting salary...
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-601
@@ -1,601 +0,0 @@
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"""
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FastAPI application for salary analytics.
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"""
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from fastapi import FastAPI, HTTPException, BackgroundTasks, UploadFile, File, Depends
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from fastapi.responses import FileResponse
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import os
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import socket
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from typing import Optional, List, Union
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import pandas as pd
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import tempfile
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from datetime import datetime
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from sqlalchemy import text
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import warnings
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import time
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from app.salary_analytics.services.main import SalaryAnalyticsPipeline
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from app.config import OUTPUT_PATHS, TABLE_NAME
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from app.salary_analytics.services.data_loader import DataLoader
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from app.salary_analytics.middlewares.middleware import add_middlewares
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from app.models.db_operations import DatabaseOperations
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from app.salary_analytics.integrations.salary_detect import SalaryDetect
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from app.utils.logger import logger
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from app.salary_analytics.helpers.response_helpers import AnalysisResponse, BatchResponse
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# Suppress warnings
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warnings.filterwarnings('ignore', category=RuntimeWarning, module='numpy')
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pd.options.mode.chained_assignment = None
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app = FastAPI(
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title="Salary Analytics API",
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description="API for analyzing and predicting salary patterns from transaction data",
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version="1.0.0"
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)
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# Add CORS middleware
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add_middlewares(app)
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# Global pipeline instance
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pipeline = SalaryAnalyticsPipeline()
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# Global variables to store loaded data and models
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data_loader = None
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df = None
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salary_predictor = None
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salary_earner_analyzer = None
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# salary_detect = SalaryDetect()
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# def check_data_loaded():
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# """Check if data is loaded before running analytics."""
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# if pipeline.df is None:
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# raise HTTPException(
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# status_code=400,
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# detail="No data loaded. Please load data first using the /load-data endpoint."
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# )
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# @app.on_event("startup")
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# async def startup_event():
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# """Initialize the pipeline on startup."""
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# try:
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# logger.info("Initializing pipeline...")
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# # Start autonomous salary detection loop
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# salary_detect.start()
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# logger.info("Started autonomous salary detection loop.")
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# # Print network information
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# hostname = socket.gethostname()
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# ip_address = socket.gethostbyname(hostname)
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# logger.info(f"Server running on hostname: {hostname}")
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# logger.info(f"Server IP address: {ip_address}")
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# logger.info(f"Server is accessible at:")
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# logger.info(f"- http://localhost:8000")
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# logger.info(f"- http://127.0.0.1:8000")
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# logger.info(f"- http://{ip_address}:8000")
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# logger.info("Pipeline initialized successfully")
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# except Exception as e:
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# logger.error(f"Error during startup: {str(e)}")
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# raise
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# @app.get("/")
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# async def root():
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# """Root endpoint."""
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# start_time = time.time()
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# logger.info("Root endpoint accessed")
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# response = {"message": "Welcome to Salary Analytics API"}
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# logger.info(f"Root endpoint completed in {time.time() - start_time:.2f} seconds")
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# return response
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# @app.get("/health")
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# async def health_check():
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# """Health check endpoint."""
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# start_time = time.time()
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# logger.info("Health check endpoint accessed")
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# response = {"status": "healthy"}
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# logger.info(f"Health check completed in {time.time() - start_time:.2f} seconds")
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# return response
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# @app.post("/analyze/keyword", response_model=AnalysisResponse)
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# async def analyze_keyword():
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# """Run keyword-based salary transaction analysis."""
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# start_time = time.time()
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# try:
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# check_data_loaded()
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# logger.info("Starting keyword analysis...")
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# data = pipeline.run_keyword_analysis()
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# logger.info(f"Keyword analysis completed. Found {len(data)} matches")
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# response = AnalysisResponse(
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# message="Keyword analysis completed successfully",
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# data={"count": len(data)}
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# )
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# logger.info(f"Keyword analysis endpoint completed in {time.time() - start_time:.2f} seconds")
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# return response
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# except Exception as e:
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# logger.error(f"Error in keyword analysis: {str(e)}")
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# logger.info(f"Keyword analysis endpoint failed after {time.time() - start_time:.2f} seconds")
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# raise HTTPException(status_code=500, detail=str(e))
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# @app.post("/analyze/consistent-amount", response_model=AnalysisResponse)
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# async def analyze_consistent_amount():
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# """Run consistent amount transaction analysis."""
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# start_time = time.time()
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# try:
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# check_data_loaded()
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# logger.info("Starting consistent amount analysis...")
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# data = pipeline.run_consistent_amount_analysis()
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# logger.info(f"Consistent amount analysis completed. Found {len(data)} matches")
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# response = AnalysisResponse(
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# message="Consistent amount analysis completed successfully",
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# data={"count": len(data)}
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# )
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# logger.info(f"Consistent amount analysis endpoint completed in {time.time() - start_time:.2f} seconds")
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# return response
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# except Exception as e:
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# logger.error(f"Error in consistent amount analysis: {str(e)}")
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# logger.info(f"Consistent amount analysis endpoint failed after {time.time() - start_time:.2f} seconds")
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# raise HTTPException(status_code=500, detail=str(e))
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# @app.post("/analyze/transaction-type", response_model=AnalysisResponse)
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# async def analyze_transaction_type():
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# """Run transaction type analysis."""
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# start_time = time.time()
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# try:
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# check_data_loaded()
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# logger.info("Starting transaction type analysis...")
|
||||
# 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))
|
||||
|
||||
+21
-10
@@ -25,18 +25,29 @@ os.makedirs(MODEL_DIR, exist_ok=True)
|
||||
|
||||
# Database Configuration
|
||||
DB_CONFIG = {
|
||||
"user": os.getenv("DB_USER"),
|
||||
"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")
|
||||
}
|
||||
|
||||
|
||||
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_INTERNAL = (f"oracle+oracledb://{DB_CONFIG['user']}:{DB_CONFIG['password']}@{DNS}")
|
||||
SQLALCHEMY_DATABASE_URI = os.getenv("SQLALCHEMY_DATABASE_URI_FULL", SQLALCHEMY_DATABASE_URI_INTERNAL)
|
||||
|
||||
#SQLALCHEMY_DATABASE_URI_FULL = 'oracle+oracledb://FIRSTADVSTG:Pchanged_56789@10.2.110.30:1521/?service_name=firstadv'
|
||||
|
||||
# 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
|
||||
@@ -81,7 +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"
|
||||
|
||||
Reference in New Issue
Block a user