README.md

@@ -1 +1 @@
-# S Series Model
+**Hello world!!!**

block.py

@@ -1,38 +1,21 @@
-import logging
+@flowx_block
-from rules_processing import processing
+def example_function(request: dict) -> dict:
 
-# Configure logging
+  # Processing logic here...
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
-)
-logger = logging.getLogger(__name__)
 
-def __main__(
+  return {
-        hd_score_m1: float,
+    "meta_info": [
-        hd_score_g1: float,
+      {
-        cluster_size_users_v2: int,
+        "name": "created_date",
-        target_connected_30_sum: float,
+        "type": "string",
-        email_cnt: int,
+        "value": "2024-11-05"
-        rejected_app_count: float,
+      }
-        app_dt_day_cnt: int,
+    ],
-        hd_score_iso_m2: float,
+    "fields": [
-        hd_score_g2: float
+      {
-    ) -> dict:
+        "name": "",
-    # Create a dictionary instead of using pandas DataFrame
+        "type": "",
-    data = {
+        "value": ""
-        "hd_score_m1": hd_score_m1,
+      }
-        "hd_score_g1": hd_score_g1,
+    ]
-        "cluster_size_users_v2": cluster_size_users_v2,
-        "target_connected_30_sum": target_connected_30_sum,
-        "email_cnt": email_cnt,
-        "rejected_app_count": rejected_app_count,
-        "app_dt_day_cnt": app_dt_day_cnt,
-        "hd_score_iso_m2": hd_score_iso_m2,
-        "hd_score_g2": hd_score_g2
   }
-
-    final = processing(data)
-    logger.info(f"scores of application: {final}")
-
-    return final

request_schema.json

@@ -1,43 +1 @@
-{
+{}
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "type": "object",
-  "properties": {
-    "hd_score_m1": {
-      "type": ["number", "null"],
-      "description": "HD fraud Score M1"
-    },
-    "hd_score_g1": {
-        "type": ["number", "null"],
-        "description": "HD Fraud Score G1"
-      },
-    "hd_score_g2": {
-      "type": ["number", "null"],
-      "description": "HD Fraud Score G1"
-    },
-    "cluster_size_users_v2": {
-      "type": ["number", "null"],
-      "description": "Size of the user cluster in version 2."
-    },
-    "target_connected_30_sum": {
-      "type": ["number", "null"],
-      "description": "Sum of target connections within 30 days."
-    },
-    "email_cnt": {
-      "type": ["number", "null"],
-      "description": "Count of emails associated with the application."
-    },
-    "rejected_app_count": {
-      "type": ["number", "null"],
-      "description": "Count of rejected applications for the applicant."
-    },
-    "app_dt_day_cnt": {
-      "type": ["number", "null"],
-      "description": "Number of application days counted."
-    },
-    "hd_score_iso_m2": {
-      "type": ["number", "null"],
-      "description": "HD fraud Score M2"
-    }
-  },
-  "required": []
-}

requirements.txt

@@ -1,3 +1 @@
-
+{}
-
-

response_schema.json

@@ -1,37 +1 @@
-{
+{}
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "type": "object",
-  "properties": {
-    "hd_score_m1": {
-      "type": ["number", "null"],
-      "description": "HD Fraud Score M1"
-    },
-    "hd_score_g1": {
-      "type": ["number", "null"],
-      "description": "HD Fraud Score G1"
-    },
-    "hd_score_g2": {
-      "type": ["number", "null"],
-      "description": "HD Fraud Score G1"
-    },
-    "hd_score_s1": {
-      "type": ["number", "null"],
-      "description": "HD Fraud Score S1"
-    },
-    "hd_score_s2": {
-      "type": ["number", "null"],
-      "description": "HD Fraud Score S2"
-    },
-    "hd_score_s3": {
-      "type": ["number", "null"],
-      "description": "HD Fraud Score S3"
-    },
-    "hd_score_iso_m2": {
-      "type": ["number", "null"],
-      "description": "HD Fraud Score M2"
-    }
-  }
-}
-
-
-

rules_processing.py

@@ -1,56 +0,0 @@
-import logging
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
-)
-logger = logging.getLogger(__name__)
-
-def processing(data: dict) -> dict:
-    try:
-        hd_score_s1 = (
-            min(1225 + (data["cluster_size_users_v2"] * 5), 1390)
-            if data["cluster_size_users_v2"] >= 3 and data["hd_score_m1"] >= 1140
-            else 0
-        )
-        logger.info(f"score_s1 calculated: {hd_score_s1}")
-    except Exception as e:
-        logger.error(f"Error processing score_s1 calculations: {e}")
-        return {}
-
-    try:
-        hd_score_s2 = (
-            min(1215 + (data["cluster_size_users_v2"] * 5), 1380)
-            if data["cluster_size_users_v2"] >= 2 and data["app_dt_day_cnt"] == 1
-            else 0
-        )
-        logger.info(f"score_s2 calculated: {hd_score_s2}")
-    except Exception as e:
-        logger.error(f"Error processing score_s2 calculations: {e}")
-        return {}
-
-    try:
-        target_connected_30_sum = data.get("target_connected_30_sum", 0) or 0  # Handling None case
-        hd_score_s3 = (
-            min(1250 + (target_connected_30_sum * 5), 1400)
-            if target_connected_30_sum >= 1
-            else 0
-        )
-        logger.info(f"score_s3 calculated: {hd_score_s3}")
-    except Exception as e:
-        logger.error(f"Error processing score_s3 calculations: {e}")
-        return {}
-
-    # Return the final results as a dictionary
-    return {
-        "hd_score_m1": data["hd_score_m1"],
-        "hd_score_g1": data["hd_score_g1"],
-        "hd_score_s1": hd_score_s1,
-        "hd_score_s2": hd_score_s2,
-        "hd_score_s3": hd_score_s3,
-        "hd_score_iso_m2": data["hd_score_iso_m2"],
-        "hd_score_g2": data["hd_score_g2"]
-    }
-
-

test_block.py

@@ -1,16 +0,0 @@
-import unittest
-from block import __main__
-
-data = {'hd_score_m1': 1093.0, 'hd_score_g1': 0.0, 'cluster_size_users_v2': 1.0, 'target_connected_30_sum': 0.0, 'email_cnt': 1.0, 'rejected_app_count': 0.0, 'app_dt_day_cnt': 1.0, 'hd_score_iso_m2': 1001.0, "hd_score_g2": 0.0}
-
-class TestBlock(unittest.TestCase):
-    def test_main_success(self):
-        blockResult = __main__(**data) 
-
-        # breakpoint()
-        self.assertIsInstance(blockResult, dict, "Result should be a dictionary.")
-        self.assertIn("hd_score_s1", blockResult, "Result dictionary should contain 'hd_score_s1' if success.")
-
-
-if __name__ == "__main__":
-    unittest.main()