implementing GenAI model integration in Flask app - unexpected output and performance hits
I'm optimizing some code but I tried several approaches but none seem to work... I'm currently integrating a Generative AI model using the Hugging Face Transformers library in my Flask application, but I'm working with some unexpected behaviors. Specifically, when I call the model for text generation, the output is often nonsensical and takes a important amount of time to return. Here is the basic setup I've implemented: ```python from flask import Flask, request, jsonify from transformers import pipeline app = Flask(__name__) model = pipeline('text-generation', model='gpt2') @app.route('/generate', methods=['POST']) def generate_text(): input_text = request.json.get('input', '') if not input_text: return jsonify({'behavior': 'No input provided'}), 400 result = model(input_text, max_length=50) return jsonify(result) if __name__ == '__main__': app.run(debug=True) ``` When I send a POST request to the `/generate` endpoint with a simple JSON payload like `{'input': 'Once upon a time'}`, I occasionally get back responses that are completely unrelated to the input, such as random phrases or even errors like "IndexError: list index out of range". I have also noticed that the performance degrades significantly as the input length increases, even when using the default settings of the model. I tried to debug by adding logging to see the input at each stage, and it looks like the input is being received correctly. However, the model predictions seem inconsistent. I also considered adjusting the `max_length` parameter, but even with values like 30 and 50, the outputs remain erratic. Is there a best practice for using the Hugging Face Transformers in a Flask application, particularly regarding input sanitation and model performance? Any insights or recommendations would be greatly appreciated! My development environment is Windows. Thanks in advance! This is for a microservice running on Linux. Is there a simpler solution I'm overlooking? For reference, this is a production service. Any help would be greatly appreciated!