Update stroke-flask-docker/app.py

stroke-flask-docker/app.py

@@ -1,94 +1,95 @@
-from flask import Flask, render_template, request, jsonify
-import joblib
-import numpy as np
-import os
-
-APP_PORT = int(os.getenv("PORT", "8080"))
-
-app = Flask(__name__)
-
-MODEL_PATH = os.getenv("MODEL_PATH", "model/stroke_pipeline.joblib")
-
-# Load model pipeline at startup
-try:
-    pipeline = joblib.load(MODEL_PATH)
-except Exception as e:
-    raise RuntimeError(f"Failed to load model at {MODEL_PATH}: {e}")
-
-FEATURE_ORDER = [
-    "gender",
-    "age",
-    "hypertension",
-    "heart_disease",
-    "ever_married",
-    "work_type",
-    "Residence_type",
-    "avg_glucose_level",
-    "bmi",
-    "smoking_status",
-]
-
-# Simple healthcheck
-@app.route("/health", methods=["GET"])
-def health():
-    return jsonify({"status": "ok"}), 200
-
-@app.route("/", methods=["GET"])
-def index():
-    # Provide default values to make testing easy
-    defaults = {
-        "gender": "Female",
-        "age": 45,
-        "hypertension": 0,
-        "heart_disease": 0,
-        "ever_married": "Yes",
-        "work_type": "Private",
-        "Residence_type": "Urban",
-        "avg_glucose_level": 95.0,
-        "bmi": 28.0,
-        "smoking_status": "never smoked",
-    }
-    return render_template("index.html", defaults=defaults)
-
-@app.route("/predict", methods=["POST"])
-def predict():
-    try:
-        # Read input either from JSON (API) or form (UI)
-        if request.is_json:
-            payload = request.get_json()
-        else:
-            payload = request.form.to_dict()
-
-        # Ensure types
-        # Map numeric fields
-        numeric_fields = ["age", "avg_glucose_level", "bmi"]
-        int_fields = ["hypertension", "heart_disease"]
-
-        for k in numeric_fields:
-            if k in payload:
-                payload[k] = float(payload[k])
-        for k in int_fields:
-            if k in payload:
-                payload[k] = int(payload[k])
-
-        # Build row in fixed feature order
-        row = [[payload.get(f, None) for f in FEATURE_ORDER]]
-
-        # Predict proba (stroke = 1)
-        prob = float(pipeline.predict_proba(row)[0][1])
-        pred = int(prob >= 0.5)
-
-        result = {"stroke_probability": prob, "predicted_label": pred}
-        if request.is_json:
-            return jsonify(result)
-        else:
-            return render_template("index.html", result=result, defaults=payload)
-    except Exception as e:
-        msg = {"error": str(e)}
-        if request.is_json:
-            return jsonify(msg), 400
-        else:
-            return render_template("index.html", error=str(e), defaults=request.form), 400
-
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=APP_PORT, debug=False)
+from flask import Flask, render_template, request, jsonify
+import joblib
+import numpy as np
+import os
+
+APP_PORT = int(os.getenv("PORT", "8080"))
+
+app = Flask(__name__)
+
+MODEL_PATH = os.getenv("MODEL_PATH", "model/stroke_pipeline.joblib")
+
+# Load model pipeline at startup
+try:
+    pipeline = joblib.load(MODEL_PATH)
+except Exception as e:
+    raise RuntimeError(f"Failed to load model at {MODEL_PATH}: {e}")
+
+FEATURE_ORDER = [
+    "gender",
+    "age",
+    "hypertension",
+    "heart_disease",
+    "ever_married",
+    "work_type",
+    "Residence_type",
+    "avg_glucose_level",
+    "bmi",
+    "smoking_status",
+]
+
+# Simple healthcheck
+@app.route("/health", methods=["GET"])
+def health():
+    return jsonify({"status": "ok"}), 200
+
+@app.route("/", methods=["GET"])
+def index():
+    # Provide default values to make testing easy
+    defaults = {
+        "gender": "Female",
+        "age": 45,
+        "hypertension": 0,
+        "heart_disease": 0,
+        "ever_married": "Yes",
+        "work_type": "Private",
+        "Residence_type": "Urban",
+        "avg_glucose_level": 95.0,
+        "bmi": 28.0,
+        "smoking_status": "never smoked",
+    }
+    return render_template("index.html", defaults=defaults)
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    try:
+        # Read input either from JSON (API) or form (UI)
+        if request.is_json:
+            payload = request.get_json()
+        else:
+            payload = request.form.to_dict()
+
+        # Ensure types
+        # Map numeric fields
+        numeric_fields = ["age", "avg_glucose_level", "bmi"]
+        int_fields = ["hypertension", "heart_disease"]
+
+        for k in numeric_fields:
+            if k in payload:
+                payload[k] = float(payload[k])
+        for k in int_fields:
+            if k in payload:
+                payload[k] = int(payload[k])
+
+        # Build row in fixed feature order
+        X = pd.DataFrame([{f: payload.get(f, None) for f in FEATURE_ORDER}])[FEATURE_ORDER]
+
+
+        # Predict proba (stroke = 1)
+        prob = float(pipeline.predict_proba(X)[0][1])
+        pred = int(prob >= 0.5)
+
+        result = {"stroke_probability": prob, "predicted_label": pred}
+        if request.is_json:
+            return jsonify(result)
+        else:
+            return render_template("index.html", result=result, defaults=payload)
+    except Exception as e:
+        msg = {"error": str(e)}
+        if request.is_json:
+            return jsonify(msg), 400
+        else:
+            return render_template("index.html", error=str(e), defaults=request.form), 400
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=APP_PORT, debug=False)