ai-image-recognition-web-4.py

@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 request.method == "POST" and request.files['image']:
        imagefile = request.files["image"].read()
        image = Image.open(io.BytesIO(imagefile))
        # preprocess the image and prepare it for classification
        image = prepare_image(image, target=(224, 224))
        # classify the input image and then initialize the list
        # of predictions to return to the client
        preds = model.predict(image)
        results = imagenet_utils.decode_predictions(preds)
        data["predictions"] = []
        # loop over the results and add them to the list of
        # returned predictions
        for (imagenetID, label, prob) in results[0]:
            r = {"label": label, "probability": float(prob)}
            data["predictions"].append(r)
        # indicate that the request was a success
        data["success"] = True
        print(data)
# return the data dictionary as a JSON response
return jsonify(data)