Я получаю ошибку. Я перепробовал все предыдущие решения в интернете, но они не работают. Я полагаю, проблема в image = Image.open(myvar), но не в получении.
def get_model():global model
model = load_model('Plant_Village.h5')
print(" * Model loaded!")
def preprocess_image(image,target_size):
image = image.resize(target_size)
image = img_to_array(image)
image = np.expand_dims(image,axis=0)
return image
print(" * Loading Keras model...")
get_model()
@app.route("/predict",methods=['GET','POST'])
def predict():
message = request.get_json(force=True)
encoded = message['image']
decoded = base64.b64decode(encoded)
myvar=io.BytesIO(decoded)
print(myvar)
image = Image.open(myvar)
processed_image = preprocess_image(image, target_size=(256,256))
#print("image returned")
prediction = model.predict(processed_image).tolist()
print("hello")
respons={
'prediction':{
'Pepper__bell___Bacterial_spot':prediction[1][0][0][0][0][0][0][0][0][0][0][0][0][0][0],
'Pepper__bell___healthy':prediction[0][1][0][0][0][0][0][0][0][0][0][0][0][0][0],
'Potato___Early_blight':prediction[0][0][1][0][0][0][0][0][0][0][0][0][0][0][0],
'Potato___healthy':prediction[0][0][0][1][0][0][0][0][0][0][0][0][0][0][0],
'Potato___Late_blight':prediction[0][0][0][0][1][0][0][0][0][0][0][0][0][0][0],
'Tomato__Target_Spot':prediction[0][0][0][0][0][1][0][0][0][0][0][0][0][0][0],
'Tomato__Tomato_mosaic_virus':prediction[0][0][0][0][0][0][1][0][0][0][0][0][0][0][0],
'Tomato__Tomato_YellowLeaf__Curl_Virus':prediction[0][0][0][0][0][0][0][1][0][0][0][0][0][0][0],
'Tomato_Bacterial_spot':prediction[0][0][0][0][0][0][0][0][1][0][0][0][0][0][0],
'Tomato_Early_blight':prediction[0][0][0][0][0][0][0][0][0][1][0][0][0][0][0],
'Tomato_healthy':prediction[0][0][0][0][0][0][0][0][0][0][1][0][0][0][0],
'Tomato_Late_blight':prediction[0][0][0][0][0][0][0][0][0][0][0][1][0][0][0],
'Tomato_Leaf_Mold':prediction[0][0][0][0][0][0][0][0][0][0][0][0][1][0][0],
'Tomato_Septoria_leaf_spot':prediction[0][0][0][0][0][0][0][0][0][0][0][0][0][1][0],
'Tomato_Spider_mites_Two_spotted_spider_mite':prediction[0][0][0][0][0][0][0][0][0][0][0][0][0][0][1]
}
}
response=json.stringfy(respons)
return jsonify(response)
