README.md

@@ -1,9 +1 @@
-## Overview
+**Hello world!!!**
-This block (`block.py`) is responsible for loading and scoring the model.
-
-## Key Inputs & Outputs
-- **Request**: Refer to `request_schema.json` for detailed input fields and validation rules.  
-- **Response**: Refer to `response_schema.json` for the returned structure and data types.
-
-## Implementation Details
-- All core logic resides in `block.py` within the `__main__` function.

block.py

@@ -1,86 +1,21 @@
-import logging
+@flowx_block
-import joblib
+def example_function(request: dict) -> dict:
-import xgboost as xgb
-import pandas as pd
-import json
-import math
 
-# Configure logging
+  # Processing logic here...
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
-)
-logger = logging.getLogger(__name__)
 
-# with open('C:/Users/abinisha/flowx/kiwi-blocks/sequence-2/fraud_v1_pre_processing/category_orders_train.json', 'r') as f:
+  return {
-with open('./category_orders_train.json', 'r') as f:
+    "meta_info": [
-    category_orders = json.load(f)
+      {
-
+        "name": "created_date",
-def __main__(user_age: int, persona_entity_confidence_score: float, persona_selfie_similarity_score_right: float,
+        "type": "string",
-             persona_selfie_similarity_score_left: float, persona_hesitation_percentage: float,
+        "value": "2024-11-05"
-             persona_hesitation_count: float, device_id_age_max: int, selfie_consistency_score_avg: float,
+      }
-             device_consistency: int, selfie_consistency_score: float, global_fs_ls: int, inquiry_frequency: int,
+    ],
-             confidence_score_min: float, contract_date_fs_sub: int, browser_os: str, user_city_ip_match: int,
+    "fields": [
-             device_id_age_avg: float, persona_distraction_events: float, sub_fs_ls: int, device_id_age_min: int,
+      {
-             confidence_score_max: float, persona_phone_risk_score: float, ip_address_risk_level: str,
+        "name": "",
-             login_frequency: float, suspect_score: int, confidence_score: float, name_consistency: int,
+        "type": "",
-             ip_location_consistency: int) -> dict:
+        "value": ""
-    
+      }
-    input_data = {
+    ]
-        "user_age": user_age,
+  }
-        "persona_entity_confidence_score": persona_entity_confidence_score,
-        "persona_selfie_similarity_score_right": persona_selfie_similarity_score_right,
-        "persona_selfie_similarity_score_left": persona_selfie_similarity_score_left,
-        "persona_hesitation_percentage": persona_hesitation_percentage,
-        "persona_hesitation_count": persona_hesitation_count,
-        "device_id_age_max": device_id_age_max,
-        "selfie_consistency_score_avg": selfie_consistency_score_avg,
-        "device_consistency": device_consistency,
-        "selfie_consistency_score": selfie_consistency_score,
-        "global_fs_ls": global_fs_ls,
-        "inquiry_frequency": inquiry_frequency,
-        "confidence_score_min": confidence_score_min,
-        "contract_date_fs_sub": contract_date_fs_sub,
-        "browser_os": browser_os,
-        "user_city_ip_match": user_city_ip_match,
-        "device_id_age_avg": device_id_age_avg,
-        "persona_distraction_events": persona_distraction_events,
-        "sub_fs_ls": sub_fs_ls,
-        "device_id_age_min": device_id_age_min,
-        "confidence_score_max": confidence_score_max,
-        "persona_phone_risk_score": persona_phone_risk_score,
-        "ip_address_risk_level": ip_address_risk_level,
-        "login_frequency": login_frequency,
-        "suspect_score": suspect_score,
-        "confidence_score": confidence_score,
-        "name_consistency": name_consistency,
-        "ip_location_consistency": ip_location_consistency
-    }
-
-    # Load the model
-    model = joblib.load("./xgboost_model.joblib") 
-    # model = joblib.load("C:/Users/abinisha/flowx/kiwi-blocks/sequence-2/fraud_v1_processing/xgboost_model.joblib")
-
-    df = pd.DataFrame(input_data, index=[False])
-
-    # Ensure categorical columns are treated as categories
-    categorical_columns = ['browser_os', 'ip_address_risk_level']
-    for col in categorical_columns:
-        if col in df.columns:
-            df[col] = df[col].str.lower().replace([None, "", "null", math.nan], "none")
-            df[col] = pd.Categorical(df[col], categories=category_orders.get(col, []))
-
-    # Ensure all columns are numeric where possible
-    for col in df.columns:
-        if col not in categorical_columns:
-            df[col] = pd.to_numeric(df[col], errors='ignore')
-
-    model_feature_names = model.feature_names
-
-    dmatrix = xgb.DMatrix(df[model_feature_names], enable_categorical=True)
-
-    prediction = model.predict(dmatrix)[0]
-
-    logger.info(f"Fraud V1 Predicted Score: {prediction}")
-
-    return {'probability': float(prediction)}

category_orders_train.json

@@ -1 +0,0 @@
-{"browser_os": ["android", "chrome os", "ios", "linux", "mac os x", "none", "windows"], "ip_address_risk_level": ["high-risk", "low-risk", "none"]}

request_schema.json

@@ -1,119 +1 @@
-{
+{}
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "type": "object",
-  "properties": {
-    "user_age": {
-      "type": ["integer", "null"],
-      "description": "Age of the user at the contract date, based on birthdate and contract date"
-    },
-    "persona_entity_confidence_score": {
-      "type": ["number", "null"],
-      "description": "Based on confidence reasons assign a score between 0 and 100"
-    },
-    "persona_selfie_similarity_score_right": {
-      "type": ["number", "null"],
-      "description": "Similarity score from the right side selfie"
-    },
-    "persona_selfie_similarity_score_left": {
-      "type": ["number", "null"],
-      "description": "Similarity score from the left side selfie"
-    },
-    "persona_hesitation_percentage": {
-      "type": ["number", "null"],
-      "description": "Percentage of time in the flow where the customer did not enter inputs"
-    },
-    "persona_hesitation_count": {
-      "type": ["number", "null"],
-      "description": "Persona hesitation count"
-    },
-    "device_id_age_max": {
-      "type": ["integer", "null"],
-      "description": "This calculates the maximum device age for a user and loan, similar to min and avg logic but for the max value."
-    },
-    "selfie_consistency_score_avg": {
-      "type": ["number", "null"],
-      "description": "Average selfie consistency score for the user's persona activity"
-    },
-    "device_consistency": {
-      "type": ["integer", "null"],
-      "description": "Number of distinct devices associated with a user and loan"
-    },
-    "selfie_consistency_score": {
-      "type": ["number", "null"],
-      "description": "Average similarity score between left and right selfie"
-    },
-    "global_fs_ls": {
-      "type": ["integer", "null"],
-      "description": "Days between the first and last global appearance of the device"
-    },
-    "inquiry_frequency": {
-      "type": ["integer", "null"],
-      "description": "Number of inquiries made by the user regarding the loan"
-    },
-    "confidence_score_min": {
-      "type": ["number", "null"],
-      "description": "The minimum recorded confidence score for the user and loan during the timeframe."
-    },
-    "contract_date_fs_sub": {
-      "type": ["integer", "null"],
-      "description": "Days between the first subscription appearance and contract date"
-    },
-    "browser_os": {
-      "type": ["string", "null"],
-      "description": "Browser OS"
-    },
-    "user_city_ip_match": {
-      "type": ["integer", "null"],
-      "description": "Checks if the user's city matches the IP city"
-    },
-    "device_id_age_avg": {
-      "type": ["number", "null"],
-      "description": "This calculates the rolling average of device_id_age for a user and loan. If no previous rows, the current value is returned."
-    },
-    "persona_distraction_events": {
-      "type": ["number", "null"],
-      "description": "Persona distraction events"
-    },
-    "sub_fs_ls": {
-      "type": ["integer", "null"],
-      "description": "Days between the first and last subscription activity"
-    },
-    "device_id_age_min": {
-      "type": ["integer", "null"],
-      "description": "This calculates the minimum device age for a user and loan, falling back to the current device age if no prior values exist."
-    },
-    "confidence_score_max": {
-      "type": ["number", "null"],
-      "description": "The maximum confidence score recorded for the user and loan combination."
-    },
-    "persona_phone_risk_score": {
-      "type": ["number", "null"],
-      "description": "Risk associated with the phone number. The risk score ranges from 0 to 100. The higher the risk score, the higher the risk level."
-    },
-    "ip_address_risk_level": {
-      "type": ["string", "null"],
-      "description": "Checks if the IP country code matches the persona country code"
-    },
-    "login_frequency": {
-      "type": ["number", "null"],
-      "description": "This counts the number of times the user logs in based on the inquiry_updated_at timestamp, providing insights into the user's login behavior throughout the loan process."
-    },
-    "suspect_score": {
-      "type": ["integer", "null"],
-      "description": "Suspect score"
-    },
-    "confidence_score": {
-      "type": ["number", "null"],
-      "description": "Confidence score"
-    },
-    "name_consistency": {
-      "type": ["integer", "null"],
-      "description": "Checks if the first name in the persona matches the user-provided first name"
-    },
-    "ip_location_consistency": {
-      "type": ["integer", "null"],
-      "description": "Number of distinct IP locations for a user and loan"
-    }
-  },
-  "required": []
-}

requirements.txt

@@ -1,5 +1 @@
-jsonschema==4.23.0
+{}
-xgboost==1.7.5
-joblib==1.3.2
-pandas==2.2.2
-numpy==1.23.5

response_schema.json

@@ -1,10 +1 @@
-{
+{}
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "type": "object",
-  "properties": {
-    "probability": {
-      "type": "number",
-      "description": "Fraud Model predicted score."
-    }
-  }
-}