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| import os | |
| os.environ['TRANSFORMERS_CACHE'] = '/app/cache' | |
| # Import the necessary modules | |
| from flask import Flask, request, render_template | |
| from transformers import pipeline | |
| # Create a Flask app | |
| app = Flask(__name__) | |
| # Create a text classification pipeline using a pretrained model | |
| classifier = pipeline("text-classification", model="KoalaAI/Text-Moderation") | |
| def home(): | |
| # Return a simple HTML page | |
| return "<html><head><title>Text Classification</title></head><body><h1>Text Classification with Huggingface</h1></body></html>" | |
| # Import the xml module | |
| import xml.etree.ElementTree as ET | |
| # Define a route for the classification result | |
| def classify(): | |
| # Get the text from the web form | |
| text = request.form.get("text") | |
| # Perform the text classification | |
| result = classifier(text)[0] | |
| # Extract the label and the score | |
| label = result["label"] | |
| score = result["score"] | |
| # Create a root element for the XML response | |
| root = ET.Element("result") | |
| # Add sub-elements for the label and the score | |
| ET.SubElement(root, "label").text = label | |
| ET.SubElement(root, "score").text = str(score) | |
| # Convert the XML element to a byte string | |
| xml_string = ET.tostring(root) | |
| # Return the XML string as the response with the appropriate mimetype | |
| return app.response_class(xml_string, mimetype="application/xml") | |
| # Run the app in debug mode | |
| if __name__ == "__main__": | |
| app.run(host="0.0.0.0", port=7860, debug=False) |