This web platform allows users to upload CSV files and train a machine learning model using the uploaded data. The platform is built using Flask and provides a simple user interface to upload files and train the model with specified parameters.
- File upload section for uploading CSV files.
- Training section for specifying model parameters and training the model.
- Display of response messages for successful or failed operations.
- API endpoints for uploading files and training models.
- Navigate to the web page by running the Flask app.
- In the file upload section, choose a CSV file and click the "Upload" button.
- In the training section, select the uploaded file from the dropdown menu.
- Specify input columns, target columns, and the number of nodes in hidden layers.
- Click the "Train model" button to start the training process.
- The response messages will display the status of the file upload and model training.
POST /data
: Uploads a CSV file to the server.GET /data
: Retrieves a list of available CSV files for training.POST /train_model
: Trains the model with the specified parameters from the payload.
Clone the repo
git clone https://github.com/bilet-13/No_code_DNN.git
cd No_code_DNN
Activate virtual environment (optional) For Mac
python3 -m venv venv
source venv/bin/activate
Install necessary packages
pip install -r build/requirements.txt
Run the src/app.py to start server
python3 src/app.py
The platform will be available at http://localhost:6060/ 🎉
- The platform uses a simple HTML and JavaScript frontend to interact with the Flask backend.
- The
ModelTrainer
andModelEvaluator
classes are used for training and evaluating the model, respectively. - The
get_data
function is used to preprocess the input data for training. - The
validate_payload
andis_file_exists
functions are used for payload validation and file existence checks. - The
is_folder
andcreate_folder
functions are used for folder management.