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from flask import Flask, request, jsonify
import joblib
import numpy as np
import os
from huggingface_hub import hf_hub_download
import joblib
# Load the trained model
model = joblib.load("model_compressed.joblib")
# Initialize app
app = Flask(__name__)
@app.route("/")
def home():
return jsonify({"message": "Supermarket Revenue Prediction API is running!"})
@app.route("/predict", methods=["POST"])
def predict():
try:
data = request.get_json(force=True)
features = np.array(data["features"])
# Case 1: single row
if features.ndim == 1:
features = features.reshape(1, -1)
prediction = model.predict(features)[0]
return jsonify({"predicted_revenue": float(prediction)})
# Case 2: multiple rows
else:
predictions = model.predict(features).tolist()
return jsonify({"predictions": predictions})
except Exception as e:
return jsonify({"error": str(e)})
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860, debug=True)
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