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How does Flask implement ChatGPT-like real-time data streaming?
How does Flask implement ChatGPT-like real-time data streaming?
Apr 01, 2025 pm 09:06 PM
Building real-time data flow using Flask framework: Simulating ChatGPT response
In Flask Web application development, it is often necessary to simulate the real-time data transmission effect of ChatGPT, that is, the data is generated and transmitted to the client immediately, rather than waiting for all data to be generated before sending it together. This article will introduce how to use Flask to implement this streaming and solve the latency problems present in traditional methods.
The problem with traditional methods is that when the response object receives the return value of the data generated function, it will block until the function is fully executed, and then all data will be returned to the client at one time. This causes the client to wait until the entire generation process is finished before seeing any output.
In order to achieve real-time transmission, we need to use Flask's stream_with_context decorator. This decorator ensures that it is sent to the client immediately every time the data is generated, avoiding delays.
The improved code is as follows:
from flask import Flask, Response, stream_with_context
from time import sleep
app = Flask(__name__)
@app.route('/stream')
def stream():
def generate():
for i in range(1, 21):
print(i)
yield f'This is item {i}\n'
sleep(0.5)
return Response(stream_with_context(generate()), mimetype='text/plain')
if __name__ == '__main__':
app.run(debug=True)
The key is to apply stream_with_context to the return value of the generate() function. This enables response object to send data immediately after each yield operation, thus achieving real-time transmission effect. Compared to traditional methods, this improved version simulates ChatGPT's real-time response more accurately. The content of the generate() function can be adjusted according to actual needs, such as reading data from the database or performing complex calculations, as long as a small amount of data is generated each time and returned using yield . This provides a more flexible and efficient streaming data transmission solution.
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