I'm writing flask api using keras. However I get a lot of errors. One of them is Error 405 - method not allowed. POST http://0.0.0.0:5000/static/predict 405 (METHOD NOT ALLOWED) jquery-3.3.1.min.js I'm trying to get predictions written on the page, but they didn't show even before that error 405. I don't know which place can lead to that error.
Here is code: predict.html
<body>
<input id="image-selector" type="file">
<button id="predict-button"> Predict</button>
<p style="font-weight:bold">Predictions</p>
<p> Jablko <span id="apple-prediction"></span></p>
<p> Banan <span id="banana-prediction"></span></p>
<img id="selected-image" src=""/>
<script src="https://code.jquery.com/jquery-3.3.1.min.js"></script>
<script>
let base64Image;
$("#image-selector").change(function(){
let reader = new FileReader();
reader.onload = function(e){
let dataURL = reader.result;
$('#selected-image').attr("src", dataURL);
base64Image = dataURL.replace("data:image/jpg;base64,", "");
//console.log(base64Image);
}
reader.readAsDataURL($("#image-selector")[0].files[0]);
$("#apple-prediction").text("");
$("#banana-prediction").text("");
});
$("#predict-button").click(function(event){
let message = {
image:base64Image
}
//console.log(message);
$.post("http://0.0.0.0:5000/static/predict", function(response){
$("#apple-prediction").text(response.prediction.apple.toFixed(6));
$("#banana-prediction").text(response.prediction.banana.toFixed(6));
console.log(response);
});
});
</script>
</body>
predict.py
app = Flask(__name__)
def get_model():
global model
model=load_model('fc_model1.h5')
#model.load_weights('model_grocery.h5')
#graph = tf.get_default_graph
print("** Model loaded!")
def preprocess_image(image, target_size):
image = image.resize(target_size)
image = image.img_to_array(image)
image = np.expand_dims(image, axis=0)
return image
print("**Loading model**")
get_model()
@app.route("/predict", methods=["POST"])
def predict():
message = request.get_json(force=True)
encoded = message['image']
decoded = base64.b64decode(encoded)
image = Image.open(io.BytesIO(decoded))
processed_image = preprocess_image(image, target_size=(224, 224))
#bt_prediction = vgg16.predict(processed_image)
prediction = model.predict(processed_image).tolist()
response = {
'prediction': {
'apple': prediction[0][0],
'banana': prediction[0][1]
}
}
return jsonify(response)
The error shows in google-chrome.