Set Up Your Environment
- Ensure you have Python installed on your system. This is necessary because Hugging Face libraries are primarily Python-based.
- Set up a virtual environment and activate it to create an isolated environment for your project.
python -m venv hf_env
source hf_env/bin/activate # On Windows use `hf_env\Scripts\activate`
Install Required Libraries
- Install the Hugging Face Transformers library and any other dependencies you might need for your machine learning models.
pip install transformers torch # TensorFlow if needed: pip install tensorflow
Create Your Hugging Face Model Script
- Write a Python script using Hugging Face's Transformers library to load and run predictions on your desired model.
- Ensure that this script processes input data and returns model outputs in a structured format suitable for Tableau.
from transformers import pipeline
# Load a pre-trained model for text generation
generator = pipeline('text-generation', model='gpt2')
def generate_text(prompt):
# Generate text based on a prompt
results = generator(prompt, max_length=50, num_return_sequences=1)
return results[0]["generated_text"]
# Example usage
generated_text = generate_text("Today, the weather is")
print(generated_text)
Set Up a Web API
- To integrate with Tableau, set up a RESTful API using Flask or FastAPI, serving your Python script's functionality over a web interface.
- Ensure your API endpoints can handle requests and return responses in JSON format.
pip install flask
from flask import Flask, request, jsonify
from transformers import pipeline
app = Flask(__name__)
generator = pipeline('text-generation', model='gpt2')
@app.route('/generate', methods=['POST'])
def generate():
data = request.json
prompt = data.get('prompt', '')
generated = generator(prompt, max_length=50, num_return_sequences=1)
return jsonify({'generated_text': generated[0]["generated_text"]})
if __name__ == '__main__':
app.run(debug=True)
Deploy and Test Your API
- Deploy your Flask API locally or on a server that Tableau can access. Ensure your server is running and accessible over a network.
- Test the API using tools like Postman or cURL to confirm it works as expected before integrating with Tableau.
Connect to Tableau
- In Tableau, use Web Data Connector functionality to integrate with your RESTful API.
- Create a JavaScript file that acts as the Web Data Connector, responsible for fetching data from your API.
(function() {
var myConnector = tableau.makeConnector();
myConnector.getSchema = function(schemaCallback) {
var cols = [{
id: "generated_text",
dataType: tableau.dataTypeEnum.string
}];
var tableSchema = {
id: "huggingFaceData",
alias: "Generated Text",
columns: cols
};
schemaCallback([tableSchema]);
};
myConnector.getData = function(table, doneCallback) {
$.ajax({
url: "http://your-flask-api-url:5000/generate",
type: "POST",
contentType: "application/json",
data: JSON.stringify({"prompt": "Your required prompt"}),
success: function(resp) {
table.appendRows([{ "generated_text": resp.generated_text }]);
doneCallback();
}
});
};
tableau.registerConnector(myConnector);
})();
Use Your Web Data Connector in Tableau
- Open Tableau and choose "Web Data Connector" when selecting your data source. Enter the URL of your JavaScript file.
- Fetch data and use it for analysis or visualization in Tableau, leveraging the capabilities provided by Hugging Face models.