# Jupyter Notebooks

This page contains the most important methods for using Rasa in a Jupyter notebook.

Running asynchronous Rasa code in Jupyter Notebooks requires an extra requirement, since Jupyter Notebooks already run on event loops. Install this requirement in the command line before launching jupyter:

pip install nest_asyncio
    
    Then in the first cell of your notebook, include:

```python
import nest_asyncio
```

```python
nest_asyncio.apply()
```

```python
print("Event loop ready.")
```

To create a project if you don’t already have one, run this cell, which will create the `test-project` directory and make it your working directory:

```python
from rasa.cli.scaffold import create_initial_project
import os
project = "test-project"
create_initial_project(project)

# move into project directory and show files
os.chdir(project)
print(os.listdir("."))
```

To train a model, you will have to tell the `train` function where to find the relevant files. Define variables that contain these paths, run:

```python
config = "config.yml"
training_files = "data/"
domain = "domain.yml"
output = "models/"
print(config, training_files, domain, output)
```

## Train a Model

Now we can train a model by passing in the paths to the `rasa.train` function. Note that the training files are passed as a list. When training has finished, `rasa.train` returns the path where the trained model has been saved.

```python
import rasa
model_path = rasa.train(domain, config, [training_files], output)
print(model_path)
```

## Chat with your assistant

To start chatting to an assistant, call the `chat` function, passing in the path to your saved model:

```python
from rasa.jupyter import chat
chat(model_path)
```

## Evaluate your model against test data

Rasa has a convenience function for getting your training data. Rasa’s `get_core_nlu_directories` is a function that recursively finds all the stories and NLU data files in a directory and copies them into two temporary directories. The return values are the paths to these newly created directories.

```python
import rasa.data as data
stories_directory, nlu_data_directory = data.get_core_nlu_directories(training_files)
print(stories_directory, nlu_data_directory)
```

To test your model, call the `test` function, passing in the path to your saved model and directories containing the stories and nlu data to evaluate on.

```python
rasa.test(model_path, stories_directory, nlu_data_directory)
print("Done testing.")
```

The results of the core evaluation will be written to a file called `results`. NLU errors will be reported to `errors.json`. Together, they contain information about the accuracy of your model’s predictions and other metrics.

```python
if os.path.isfile("errors.json"):
    print("NLU Errors:")
    print(open("errors.json").read())
else:
    print("No NLU errors.")

if os.path.isdir("results"):
    print("
")
    print("Core Errors:")
    print(open("results/failed_stories.md").read())
```

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