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app.py

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+import os
+import joblib
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+
+app = Flask(__name__)
+CORS(app)
+
+# Define the model path
+MODEL_PATH = Path("backend_files/final_model.joblib")
+
+# Load the model once at startup
+try:
+    if not MODEL_PATH.is_file():
+        raise FileNotFoundError(f"Model file not found at: {MODEL_PATH.resolve()}")
+    model = joblib.load(MODEL_PATH)
+    print("Model loaded successfully.")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    model = None
+
+@app.get("/")
+def health():
+    return {"status": "ok", "model_loaded": model is not None}, 200
+
+@app.post("/predict")
+def predict():
+    if model is None:
+        return jsonify({"error": "Model not loaded. Check startup logs."}), 500
+    try:
+        # Get data from POST request
+        data = request.get_json(force=True)
+        data_df = pd.DataFrame([data])
+
+        # Make prediction (in log scale) and inverse transform
+        prediction_log = model.predict(data_df)
+        prediction = np.expm1(prediction_log)
+
+        return jsonify({'prediction': prediction.tolist()})
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)

final_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ee7dc45fc8af07fdadf3312a26df344ea2a4f213be39cb48bb5a10629afd951f
+size 54531050

requirements.txt

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+numpy==2.0.2
+pandas==2.2.2
+scikit-learn==1.6.1
+xgboost==2.1.0
+joblib==1.4.2
+Flask==3.0.3
+flask-cors==4.0.1