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2026-07-13 13:17:40 +08:00

37 lines
1023 B
Python

import gradio as gr
from transformers import pipeline
import requests
# __doc_code_begin__
generator1 = pipeline("text-generation", model="gpt2")
generator2 = pipeline("text-generation", model="distilgpt2")
def model1(text):
generated_list = generator1(text, do_sample=True, min_length=20, max_length=100)
generated = generated_list[0]["generated_text"]
return generated
def model2(text):
generated_list = generator2(text, do_sample=True, min_length=20, max_length=100)
generated = generated_list[0]["generated_text"]
return generated
demo = gr.Interface(
lambda text: f"{model1(text)}\n------------\n{model2(text)}",
"textbox",
"textbox",
api_name="predict",
)
# __doc_code_end__
# Test example code
demo.launch(prevent_thread_lock=True)
response = requests.post(
"http://127.0.0.1:7860/gradio_api/run/predict/", json={"data": ["My name is Lewis"]}
)
assert response.status_code == 200
print("gradio-original.py: Response from example code is", response.json()["data"])