Created
December 21, 2020 06:35
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Serve Model predictions using REST API
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| # 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) |
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