aihero 0.1.3

AI Hero Python SDK

AI Hero Python SDK

Go from Zero to ML in minutes.

With AI Hero, 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

Creating an Automation with this Python API.

1. First, install this library.

pip install aihero

2. Establish a connection to AI Hero in your Python program.

from aihero import Client
aihero_client = Client()

3. To create an automation, you'll need a temporary authorization key to your workspace.

client.request_auth_secret(email="<YOUR_EMAIL_ADDRESS>")

# You'll be sent a auth_key in your email. Use it to get access to your workspace. 
# This auth_key is valid ONLY for two hours and meant as a temporary access. 
# Once you create the automation, you'll be able to create and use an API key that will last longer.

workspace = client.get_workspace(auth_secret="<AUTH_KEY_FROM_EMAIL>")

4. Next create an automation from the types available. See the next section for all types supported.

automation = workspace.create_automation(
        automation_type="detect_sentiment", 
        name="Detect sentiment in customer reviews", 
        description="Find customer sentiment in online reviews submitted by customers."
    )

# Get your automation id.
automation_id = automation["_id"]

The next time, you can use the API key you can find on the web console to get access to the automation directly:

# 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>')

5. Start using it:

# 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}}

Predicting using an Automation created on the AI Hero Platform.

1. We assume you have created an automation on AI Hero Platform and trained it.
2. Create an API Key on the platform (from the menu on the left when inside your automation).
3. Install this library.

pip install aihero

4. Start using the automation:

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}}

# Add more data to your trained model.
automation.add(text="It's great that I can add an example to the model.", guid="<SHARED_ID_FOR_TRACKING_DATA>")

Available Automations with the API

Detect Sentiment in Short Text

1. Creating an automation with the API if you havent already:

automation = workspace.create_automation(
        automation_type="detect_sentiment", 
        name="Detect sentiment in customer reviews", 
        description="Find customer sentiment in online reviews submitted by customers."
    )

2. Start using it! No training required.

# 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}}

Tag Short Text

1. Creating an automation with the API if you havent already:

automation = workspace.create_automation(
        automation_type="tag_short_text", 
        name="<NAME_YOUR_MODEL>", 
        description="<DESCRIBE_WHAT_YOUR_MODEL_WILL_DO>"
    )

2. Set the tags you want to tag your model with:

automation.set_tags(['<TAG/CATEGORY 1>', '<TAG/CATEGORY 2>', '<TAG/CATEGORY 3>'])

print(automation.get_tags())

3. Add the data to train the model with:

automation.add(text="It's great that I can add training data to the model.", guid="<SHARED_ID_FOR_TRACKING_DATA>")

4. Trigger the learning:

# Trigger relearn. You'll be notified when the automation is ready for review. 
relearning_job = automation.relearn()

# To check the status.
print(automation.get_status(relearning_job))

5. Start using it!

# Predict using your trained automation.
prediction = automation.predict(text="This works great!")

Tag Entire Images

1. Creating an automation with the API if you havent already:

automation = workspace.create_automation(
        automation_type="tag_entire_images", 
        name="<NAME_YOUR_MODEL>", 
        description="<DESCRIBE_WHAT_YOUR_MODEL_WILL_DO>"
    )

2. Set the tags you want to tag your model with:

automation.set_tags(['<TAG/CATEGORY 1>', '<TAG/CATEGORY 2>', '<TAG/CATEGORY 3>'])

print(automation.get_tags())

3. Add the data to train the model with:

automation.add(image_url="<IMAGE_URL>", guid="<SHARED_ID_FOR_TRACKING_DATA>")

4. Trigger the learning:

# Trigger relearn. You'll be notified when the automation is ready for review. 
relearning_job = automation.relearn()

# To check the status.
print(automation.get_status(relearning_job))

5. Start using it!

# Predict using your trained automation.
prediction = automation.predict(image_url="<IMAGE_URL>")

Improving your Automations

To improve the automation, teach it with feedback on how it has learned:

# Trigger relearn. You'll be notified when the automation is ready for review. 
relearning_job = automation.relearn()

# To check the status.
print(automation.get_status(relearning_job))

Once you've been notified. Use the UI to teach the automation by giving feedback on the predictions.

You can also add more data to train the model with:

automation.add(text="More data training data to the model.", guid="<SHARED_ID_FOR_TRACKING_DATA>")