Removed 0.01 drift on frontend

app/api/routes.py

@@ -1,6 +1,4 @@
 # app/api/routes.py
-# /predict, /health, /dashboard, /monitoring/run
-
 from fastapi import APIRouter, BackgroundTasks, UploadFile, File, Request, HTTPException
 from fastapi.responses import JSONResponse
 from fastapi.templating import Jinja2Templates
@@ -11,7 +9,7 @@ from app.monitoring.drift import run_drift_check
 from app.monitoring.governance import run_governance_checks
 
 import pandas as pd
-import numpy as np  # needed for handling list/nested drift scores
+import numpy as np  # for numeric handling
 
 templates = Jinja2Templates(directory="app/templates")
 
@@ -21,83 +19,45 @@ predictor = Predictor()
 
 # CSV upload & prediction
 @router.post("/predict")
-async def predict_file(
-    background_tasks: BackgroundTasks,
-    file: UploadFile = File(...)
-):
+async def predict_file(background_tasks: BackgroundTasks, file: UploadFile = File(...)):
     df = pd.read_csv(file.file)
 
     # ---- STRICT MODE: schema enforcement ----
     missing = set(predictor.features) - set(df.columns)
     if missing:
-        raise HTTPException(
-            status_code=400,
-            detail=f"Invalid schema. Missing required columns: {sorted(missing)}"
-        )
+        raise HTTPException(status_code=400, detail=f"Invalid schema. Missing required columns: {sorted(missing)}")
 
     # ---- Model inference ----
     preds, probas = predictor.predict(df)
-
     results = []
     for i, (pred, proba) in enumerate(zip(preds, probas)):
         results.append({
             "row": i,
             "probability": round(float(proba), 4),
             "prediction": "Default" if pred == 1 else "No Default",
-            "risk_level": (
-                "High" if proba >= 0.75 else
-                "Medium" if proba >= 0.5 else
-                "Low"
-            )
+            "risk_level": "High" if proba >= 0.75 else "Medium" if proba >= 0.5 else "Low"
         })
 
     # ---- Drift: run once immediately to return chart data ----
     reference_df = pd.read_csv("models/v1/reference_data.csv")
 
-    # run_drift_check returns (report_path, drift_dict)
+    # Correctly get numeric drift scores per column
     _, drift_dict = run_drift_check(df[predictor.features], reference_df[predictor.features], "v1")
 
-    # Handle float, list/array, or nested dict drift scores safely
+    # Ensure minimal values for chart and safe numeric handling
     drift_for_chart = []
     for col, score in drift_dict.items():
-        score_value = 0.01  # default minimal value
-        if isinstance(score, dict):
-            numeric_values = []
-
-            def extract_numbers(d):
-                for v in d.values():
-                    if isinstance(v, (int, float)):
-                        numeric_values.append(v)
-                    elif isinstance(v, dict):
-                        extract_numbers(v)
-                    elif isinstance(v, (list, np.ndarray)):
-                        numeric_values.extend([float(x) for x in v if isinstance(x, (int, float))])
-
-            extract_numbers(score)
-            if numeric_values:
-                score_value = float(np.mean(numeric_values))
-        elif isinstance(score, (list, np.ndarray)):
-            score_value = float(np.mean([s for s in score if isinstance(s, (int, float))]))
-        elif isinstance(score, (int, float)):
+        try:
             score_value = float(score)
-
-        # ensure finite number
-        score_value = float(np.nan_to_num(score_value, nan=0.01, posinf=1.0, neginf=0.01))
-        drift_for_chart.append({"column": col, "score": max(score_value, 0.01)})
+            score_value = max(score_value, 0.01)
+        except Exception:
+            score_value = 0.01
+        drift_for_chart.append({"column": col, "score": score_value})
 
     # Schedule full drift in background as before
-    background_tasks.add_task(
-        run_drift_check,
-        df[predictor.features],
-        reference_df[predictor.features],
-        "v1"
-    )
+    background_tasks.add_task(run_drift_check, df[predictor.features], reference_df[predictor.features], "v1")
 
-    return JSONResponse({
-        "n_rows": len(results),
-        "results": results,
-        "drift": drift_for_chart
-    })
+    return JSONResponse({"n_rows": len(results), "results": results, "drift": drift_for_chart})
 
 
 # Health
@@ -111,10 +71,7 @@ def health():
 def run_drift():
     current_df = load_production_data()
     report_path = run_drift_check(current_df)
-    return {
-        "status": "drift_check_completed",
-        "report_path": report_path
-    }
+    return {"status": "drift_check_completed", "report_path": report_path}
 
 
 # Monitoring pipeline
@@ -123,28 +80,13 @@ def monitoring_run(background_tasks: BackgroundTasks, model_version: str = "v1")
     current_data = pd.read_csv("data/processed/current_data.csv")
     reference_data = pd.read_csv("data/processed/credit_default_clean.csv")
 
-    background_tasks.add_task(
-        run_drift_check,
-        current_data[predictor.features],
-        reference_data[predictor.features],
-        model_version
-    )
-    background_tasks.add_task(
-        run_governance_checks,
-        current_data,
-        model_version=model_version
-    )
+    background_tasks.add_task(run_drift_check, current_data[predictor.features], reference_data[predictor.features], model_version)
+    background_tasks.add_task(run_governance_checks, current_data, model_version=model_version)
 
-    return {
-        "status": "monitoring triggered",
-        "model_version": model_version
-    }
+    return {"status": "monitoring triggered", "model_version": model_version}
 
 
 # Dashboard
 @router.get("/dashboard")
 def dashboard(request: Request):
-    return templates.TemplateResponse(
-        "dashboard.html",
-        {"request": request}
-    )
+    return templates.TemplateResponse("dashboard.html", {"request": request})

app/inference/predictor.py

@@ -1,5 +1,4 @@
 # model.predict wrapper
-
 import json
 import joblib
 import numpy as np

app/monitoring/drift.py

@@ -1,5 +1,4 @@
 # Evidently logic
-# app/monitoring/drift.py
 import os
 import pandas as pd
 from evidently.report import Report
@@ -32,10 +31,15 @@ def run_drift_check(current_data: pd.DataFrame, reference_data: pd.DataFrame, mo
     report.run(current_data=current_data, reference_data=reference_data)
     report.save_html(REPORT_PATH)
 
-    # report.as_dict() returns a dict; in newer Evidently versions it can be a list
-    report_metrics = report.as_dict() if hasattr(report, "as_dict") else list(report)
+    # Extract numeric drift scores per column
+    report_dict = report.as_dict() if hasattr(report, "as_dict") else {}
+    drift_scores = {}
+    for metric in report_dict.get("metrics", []):
+        if metric["metric"] == "DataDriftMetric":
+            for col_name, col_data in metric["result"].get("dataset_drift", {}).items():
+                drift_scores[col_name] = col_data.get("drift_score", 0.0)
 
-    # Run governance checks
-    alerts = governance.check_metrics(report_metrics, model_version=model_version)
+    # Run governance checks (keeps existing alerts)
+    alerts = governance.check_metrics(report_dict, model_version=model_version)
 
-    return alerts, report_metrics
+    return alerts, drift_scores