Fresh backend setup with Flask, Docker, and correct API

Dockerfile

@@ -1,11 +1,6 @@
-FROM python:3.11-slim
 
+FROM python:3.9-slim
 WORKDIR /app
-
 COPY . .
-
-RUN pip install --no-cache-dir -r requirements.txt
-
-EXPOSE 7860
-
+RUN pip install -r requirements.txt
 CMD ["python", "app.py"]

app.py

@@ -1,21 +1,31 @@
-from flask import Flask, jsonify
-import xgboost as xgb
-import pandas as pd
+
+from flask import Flask, request, jsonify
+import pickle
 import numpy as np
 
 app = Flask(__name__)
 
 # Load model
-model = xgb.XGBRegressor()
-model.load_model("model/xgb_superkart_model.pkl")
+with open("model/xgb_superkart_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+@app.route("/", methods=["GET"])
+def root():
+    return jsonify({"message": "SuperKart Backend is live!"})
 
-# Load test data
-X_test = pd.read_csv("data/X_test.csv")
+@app.route("/predict", methods=["POST"])
+def predict():
+    try:
+        content = request.json
+        data = content.get("data")
+        if not data or not isinstance(data, list):
+            return jsonify({"error": "Invalid input format. Expecting { 'data': [[...]] }"}), 400
 
-@app.route("/predict", methods=["GET"])
-def get_forecast():
-    preds = model.predict(X_test)
-    return jsonify({"forecast": np.round(preds[:5], 2).tolist()})
+        arr = np.array(data)
+        preds = model.predict(arr)
+        return jsonify({"prediction": preds.tolist()})
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
 
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=7860)

requirements.txt

@@ -1,5 +1,4 @@
 flask
-pandas
-xgboost
 numpy
-scikit-learn
+scikit-learn
+xgboost