Metadata-Version: 2.1
Name: imagine-client-py
Version: 0.3.0
Summary: A python client for doc.ai's Imagine API
Home-page: https://github.com/doc-ai/imagine-client-py.git
Author: Steven Chand
Author-email: steven@doc.ai
License: UNKNOWN
Platform: UNKNOWN
Description-Content-Type: text/markdown

# Imagine Client for Python

This library allows you to easily connect to doc.ai's Imagine API.

## Supported Image Models

Model Id       | Type  | Description |
---------------|-------|-------------|
phenomenal-face| image | Given a facial image, this model infers the age, sex, height, and weight of a person |
happy-face     | image | Given a facial image, this model infers the mood of a person |
manna     | image | Given a food image, this model infers whether the food is healthy/unhealthy |
face-detector     | image | Given an image, this model detects whether a face is present |
food-detector     | image | Given an image, this model detects whether food is present | 


## API

```ImageBasedModel.create(imagine_client, model_id)```

> Given an ImagineClient (imagine_client) and string (model_id), creates an image model. See the *"Supported Image Models"* section for a list of supported model ids.

```ImageBasedModel.infer(image_file_stream, store=false, metadata=None)```

> Performs the model's inference on an image. A read stream should be passed as the parameter (image_file_stream).

```ImageBasedModel.insert_correction(corrections_dict, metadata=None)```

> Uploads a correction label to the imagine API, for a given inference.

```ImageBasedModel.insert_prediction(predictions_dict, metadata=None)```

> (FOR EDGE PREDICTIONS) Uploads a prediction label to the imagine API, for a given inference.

### Example

```python

from imagine_client_py import ImagineClient, ImageBasedModel

# Required parameters
API_KEY = <VALID_IMAGINE_API_KEY>
IMAGE_PATH = <IMAGE_FILE_PATH>
CLIENT_ID = <ANY_CLIENT_ID>

# Initialize imagine client
imagine_client = ImagineClient({
  'api_key': API_KEY,
  'client_id': CLIENT_ID
})

# Create the 'happy-face' model interface
model = ImageBasedModel.create(imagine_client, 'happy-face')

# Create a read stream to a image file
with open(IMAGE_PATH, 'rb') as image_face_file:
  # Perform the model's inference, on the image.
  result = model.infer(image_face_file)
```


##Tests

To run tests:
```
export IMAGINE_API_KEY=<API key> IMAGINE_CLIENT_ID=test
pytest
```

