from flask import Flask, request, jsonify
import joblib
import pandas as pd
import numpy as np

app = Flask(__name__)

# Load the serialized model
try:
    model = joblib.load('sales_forecast_model_v1.joblib')
except Exception as e:
    print(f"Error loading model: {e}")

@app.route('/v1/sales', methods=['POST'])
def predict_single():
    """Endpoint for single prediction based on JSON input."""
    try:
        data = request.get_json()
        df = pd.DataFrame([data])
        prediction = model.predict(df)[0]
        return jsonify({'Predicted_Sales': round(float(prediction), 2)})
    except Exception as e:
        return jsonify({'error': str(e)}), 400

@app.route('/v1/salesbatch', methods=['POST'])
def predict_batch():
    """Endpoint for batch predictions from a CSV file."""
    try:
        file = request.files['file']
        df = pd.read_csv(file)
        predictions = model.predict(df)
        return jsonify({str(idx): round(float(pred), 2) for idx, pred in enumerate(predictions)})
    except Exception as e:
        return jsonify({'error': str(e)}), 400

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860, debug=False)