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import os
import subprocess
import gradio as gr
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForCausalLM
# 下载模型
base_dir = "/root/.cache/huggingface/hub"
if not os.path.isdir(base_dir):
os.makedirs(base_dir)
cmd_list = ["cd", "base_dir", "&&", "git clone", "https://huggingface.co/gpt2", "models"]
cmd_str = "".join(cmd)
print("cmd_str:", cmd_str)
ret, out = subprocess.getstatusoutput(cmd_str)
print("ret:", ret)
print("out:", out)
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path="/root/.cache/huggingface/hub/models")
model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path="/root/.cache/huggingface/hub/models")
generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
# generator = pipeline('text-generation', model='gpt2')
def generate(text):
result = generator(text, max_length=30, num_return_sequences=1)
return result[0]["generated_text"]
examples = [
["The Moon's orbit around Earth has"],
["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
]
demo = gr.Interface(
fn=generate,
inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
outputs=gr.outputs.Textbox(label="Generated Text"),
examples=examples
)
demo.launch(server_name="0.0.0.0", server_port=7860)
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