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import pickle
import numpy as np
from flask import Flask, request, jsonify
# Load the pickle model
MODEL_PATH = "/mnt/data/model.pkl"
app = Flask(__name__)
# Load the model
with open(MODEL_PATH, 'rb') as file:
model = pickle.load(file)
@app.route('/predict', methods=['POST'])
def predict():
try:
# Parse input JSON data
input_data = request.get_json()
if not input_data:
return jsonify({"error": "Invalid input data"}), 400
# Assuming the input data is a list of features
features = np.array(input_data['features']).reshape(1, -1) # Adjust for single input
# Make predictions
prediction = model.predict(features)
# Return the prediction as JSON
return jsonify({"prediction": prediction.tolist()}), 200
except Exception as e:
return jsonify({"error": str(e)}), 500
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000)
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