Deep Learning Web Application with Python

1. Install requirements and clone this repository

Requirements include:

• Flask 1.0.2
• Flask-Cors 3.0.6
• h5py 2.8.0
• Keras 2.2.4
• Keras-Applications 1.0.6
• numpy 1.15.4
• opencv-python 3.4.3.18
• Pillow 5.3.0
• tensorflow 1.12.0

git clone https://github.com/ltephanysopez/deep-learning-web.git
pip install -r requirements.txt

2) Train model

Use the Jupyter notebook train_your_model.ipynb to train a model

2.1 Place your own image folder structure according to the given structure:

data/ 
  train/
    
    class #/ 
        img001.jpg
        img002.jpg
        ...
    
    class #/ 
        img001.jpg
        img002.jpg
        ...

  validation/
    
    class #/ 
        img001.jpg
        img002.jpg
        ...
    
    class #/
        img001.jpg
        img002.jpg
        ...

2.2 Enter the number of classes according to the number of image classes you are using

classes = <your classes>

2.3 Enter the number of training and validation samples accoridng to your dataset

number_of_images_training = <your number of training images>
number_of_images_validation = <your number of validation images>

2.4 Decide on which image size to train on

image_size = (<your size>,<your size>)

2.5 Save model and architecture

model_json = model.to_json()
with open("model.json", "w") as json_file:
    json_file.write(model_json)

model.save_weights('your_model.h5')

2.6 Evaluate the model by predicting on a new unseen image

prediction = loaded_model.predict(img_for_prediction)

3. Set up backend in app.py with the trained model from saved model.json

3.1 Define classes according to the classes you are using

classes = {'our_class_name_1': 0, 'our_class_name_2': 1, 'our_class_name_3': 2 ... }

3.2 Define same image size as network is trained on

image_size = (<your size>,<your size>)

5. Run the backend for predicitions

python app.py

4. Run frontend

cd /frontend
npm install
npm start

4.1 Visit http://localhost:3000 to test the newly trained model by uploading an image