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predict_app.py
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#!/usr/bin/env python
# coding: utf-8
# In[5]:
import base64
import numpy as np
from PIL import Image
import keras
from keras.models import Sequential , load_model
from keras.preprocessing.image import ImageDataGenerator, img_to_array
from flask import request
from flask import jsonify , Flask
from keras.models import model_from_json
# In[4]:
app = Flask(__name__)
# In[7]:
def get_model():
global model
with open('model.json', 'r') as f:
model = model_from_json(f.read())
model.load_weights('model.h5')
print("model Loaded")
# In[8]:
def preprocess_image(image,target_size):
if image.mode!="RGB":
image = image.convert("RGB")
image = image.resize(target_size)
image = img_to_array(image)
image = np.expand_dims(image,axis=0)
return image
# In[ ]:
print("keras model is loaded")
get_model()
# In[16]:
@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=(512,512))
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
#img = cv2.resize(img,(64,64))
img = np.reshape(img,[1,512,512,3])
prediction = model.predict_classes(processed_image).to_list()
#prediction = model.predict(processed_image).to_list()
response = {'prediction': {'Normal':prediction[0][0], 'Weird':prediction[0][1] }}
return jsonify(response)
if __name__ == '__main__':
app.run(port = 5000, debug=True)
# In[ ]: