Metadata-Version: 2.1
Name: aihero
Version: 0.1.2
Summary: AI Hero Python SDK
Home-page: https://github.com/ai-hero/python-client-sdk
Author: AI Hero Team
Author-email: team@aihero.studio
License: MIT
Keywords: AI Hero,Spotcheck,MLOps,AI,Data Annotation,Labeling,Model Training,Model Serving,Model Deployment
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Description-Content-Type: text/markdown
License-File: LICENSE

# AI Hero Python SDK
*Go from Zero to ML in minutes.*

With [AI Hero](https://aihero.studio), you can protototype ML models to power your product features, automate routine tasks, and analyze customer feedback - all with a simple, no-code, self-serve platform. Choose from our growing list of automations to tag text, recommend products, tag images, detect customer sentiment, and other tasks!

## Usage
1. First, create an account on the [AI Hero Platform](https://api.aihero.studio)
2. Create an automation and train it. 
3. Create an API Key.
4. Install this library
5. Start using it:
```
from aihero import Client

# Create the client, which connects to AI Hero server.
client = Client()

# Get the automation object, using the automation id and your API key for that automation. 
automation = client.get_automation(automation_id='<YOUR_AUTOMATION_ID>',  api_key='<YOUR_API_KEY>')

# Predict using your trained automation.
# For example, for detecting sentiment in short text:
prediction = automation.predict(text="This works great!")

# predction object:
# {'explanation': 'The sentiment is positive', 'tags': {'negative': 0.00013113021850585938, 'positive': 0.9998688697814941}}

```

