# Serve Model predictions using REST API

# Predict and return JSON data
@app.route("/predict", methods=["POST"])
def predict():
    # initialize the data dictionary that will be returned from the view
    data = {"success": False}

    # ensure an image was properly uploaded to our endpoint
    if flask.request.method == "POST":
        if flask.request.files.get("image"):
            data["predictions"] = []

            # Read input as image
            image = flask.request.files["image"].read()
            image = Image.open(io.BytesIO(image))

            # Run inference and get prediction
            seg_mask, seg_product_category = get_model_output_json(image)

            # Add prediction results to JSON data
            r = {"label": seg_product_category}
            data["predictions"].append(r)

            # indicate that the request was a success
            data["success"] = True

    # return the data dictionary as a JSON response
    return flask.jsonify(data)