Deploy Flask backend to ExtraaLeanBackend

app.py

@@ -1,13 +1,28 @@
 from flask import Flask, request, jsonify
-import joblib, pandas as pd, os
+import os
+import joblib
+import pandas as pd
 
 app = Flask(__name__)
-# Load the serialized preprocessing+model pipeline
+# Load the serialized preprocessing+model pipeline once at startup
 model = joblib.load("best_model.pkl")
 
+@app.route("/", methods=["GET"])
+def home():
+    # Prevent 404 on root; quick sanity-check endpoint
+    return jsonify({
+        "message": "✅ ExtraaLeanBackend is up and running!",
+        "routes": ["/predict", "/health"]
+    })
+
+@app.route("/health", methods=["GET"])
+def health():
+    # Health check for HF Spaces
+    return jsonify({"status": "ok"})
+
 @app.route("/predict", methods=["POST"])
 def predict():
-    # Expect JSON body with feature keys matching training data
+    # Parse JSON body into a DataFrame
     data = request.get_json()
     df = pd.DataFrame([data])
     # Predict class and probability
@@ -15,11 +30,7 @@ def predict():
     prob = float(model.predict_proba(df)[0, 1])
     return jsonify({"prediction": pred, "probability": prob})
 
-@app.route("/health", methods=["GET"])
-def health():
-    return jsonify({"status": "ok"})
-
 if __name__ == "__main__":
-    # Use PORT env var if provided, otherwise default to 7860
-    port = int(os.environ.get("PORT", 7860))
+    # Listen on PORT env var (provided by HF Spaces) or default 7860
+    port = int(os.getenv("PORT", 7860))
     app.run(host="0.0.0.0", port=port)