# Python backend 

## Setup

```
pip install -r requirements.txt
chmod +x launch.sh
```

## Execution


`./launch.sh`

## Usage

The API listens to the port `6006` and the route `autocomplete`. It listens to `POST` requests.
Query it like this: `{POST}http://<url>:6006/autocomplete`

The necessary argument is `context` which is a string of characters (ideally a sentence) which will be converted in tokens and fed to GPT-2.

The optional arguments are detailed below:

`length` is an unsigned int which sets the maximum length (in tokens) of the generated sentence __default: 100__

`n_samples` is an int `0 < n_samples <= 3` which sets the maximum amount of samples generated. __default: 3__

`max_time` is an unsigned float which sets an heuristic for the maximum time spent generating sentences. It is a heuristic because it is not exact, it can slightly overflow. __default: infinite__

`model_size` takes `"small"` or `"medium"` as input and corresponds to the GPT model size __default: small__

`temperature` float - temperature of the model __default: 1__

`max_tokens` int - maximum amount of tokens that will be fed into the model. __default: 256__

`top_p` float - 0 < top_p < 1, nucleus sampling; only tokens with a cumulative probability of top_p will be selected for multinomial sampling __default: 0.9__

`top_k` int - Only top k tokens will be selected for multinomial sampling. __default: 256__

## Return format

The server returns a set of sentences according to the context. Their format is: 
```
{sentences: {value: string, time: number}[], time: number}
```

Example:

With POST parameters as:

```json
{
    "context": "That man is just another",
    "samples": 3
}
```

The response is as follows:

```json
{
    "sentences": [
        {"value": " handicapped working man.", "time": 0.15415167808532715}, 
        {"value": " guy, doing everything his manly nature requires.", "time": 0.2581148147583008},
        {"value": " guy, Mohr said.", "time": 0.17547011375427246}
    ],  
    "time": 0.264873743057251
}
```