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oleumerSite.py
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oleumerSite.py
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from flask import Flask, render_template #imports
#functions for calling, processing the model and prediction function
def load_image(img_path):
img = image.load_img(img_path, target_size=(224, 224))
img_tensor = image.img_to_array(img)
img_tensor = np.expand_dims(img_tensor, axis=0)
img_tensor /= 255.
return img_tensor
def get_model():
global model
model = load_model('model.h5')
print("loaded!")
def prediction(img_path):
new_image = load_image(img_path)
pred = model.predict(new_image)
print(pred)
labels=np.array(pred)
labels[labels>=0.6]=1
labels[labels<0.6]=0
print(labels)
final=np.array(labels)
if final[0][0]==1:
return "Bad"
else:
return "Good"
app = Flask(__name__)
get_model()
#returning and calling web page
@app.route("/", methods=['GET', 'POST'])
def home():
return render_template('home.html')
#calling predications and print result
@app.route("/predict", methods = ['GET','POST'])
def predict():
if request.method == 'POST':
file = request.files['file']
filename = file.filename
file_path = os.path.join(r'C:/Users/nEW u/Flask/static/', filename)
file.save(file_path)
print(filename)
product = prediction(file_path)
print(product)
# you can use file_path instead of filename
return render_template('predict.html', product = product, user_image = file_path)
if __name__ == "__main__":
app.run()