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| import os | |
| ''' | |
| os.system("pip uninstall httpx -y") | |
| os.system("pip uninstall pydantic -y") | |
| os.system("pip uninstall gradio -y") | |
| os.system("pip install -U gradio") | |
| ''' | |
| os.system("pip install transformers==4.30.2") | |
| ''' | |
| import subprocess | |
| out = subprocess.check_output("pip --help") | |
| print(out.decode()) | |
| out = subprocess.check_output("pip --version") | |
| print(out.decode()) | |
| ''' | |
| os.system("pip install huggingface_hub") | |
| from huggingface_hub import space_info | |
| import sys | |
| import re | |
| from flair.models import SequenceTagger | |
| from flair.data import Sentence | |
| flair_ner_model_path = "flair_model" | |
| assert os.path.exists(flair_ner_model_path) | |
| loaded_model: SequenceTagger = SequenceTagger.load(os.path.join(flair_ner_model_path ,"best-model.pt")) | |
| def one_item_process(r, loaded_model): | |
| #assert type(r) == type(pd.Series()) | |
| zh = r["question"] | |
| zh = zh.replace(" ", "").strip() | |
| sentence = Sentence(" ".join(list(zh))) | |
| loaded_model.predict(sentence) | |
| sentence_str = str(sentence) | |
| ask_spans = re.findall(r'\["(.+?)"/ASK\]', sentence_str) | |
| sentence = re.findall(r'Sentence: "(.+?)"', sentence_str) | |
| if ask_spans: | |
| ask_spans = ask_spans[0] | |
| else: | |
| ask_spans = "" | |
| if sentence: | |
| sentence = sentence[0] | |
| else: | |
| sentence = "" | |
| ask_spans, sentence = map(lambda x: x.replace(" ", "").strip(), [ask_spans, sentence]) | |
| return ask_spans, sentence | |
| import gradio as gr | |
| example_sample = [ | |
| "宁波在哪个省份?", | |
| "美国的通货是什么?", | |
| ] | |
| def demo_func(question): | |
| assert type(question) == type("") | |
| ask_spans, sentence = one_item_process( | |
| {"question": question}, | |
| loaded_model | |
| ) | |
| return { | |
| "Question words": ask_spans | |
| } | |
| markdown_exp_size = "##" | |
| lora_repo = "svjack/chatglm3-few-shot" | |
| lora_repo_link = "svjack/chatglm3-few-shot/?input_list_index=2" | |
| emoji_info = space_info(lora_repo).__dict__["cardData"]["emoji"] | |
| space_cnt = 1 | |
| task_name = "[---Chinese Question Words extractor---]" | |
| description = f"{markdown_exp_size} {task_name} few shot prompt in ChatGLM3 Few Shot space repo (click submit to activate) : [{lora_repo_link}](https://huggingface.co/spaces/{lora_repo_link}) {emoji_info}" | |
| demo = gr.Interface( | |
| fn=demo_func, | |
| inputs="text", | |
| outputs="json", | |
| title=f"Chinese Question Words extractor 🐱 demonstration", | |
| #description = description, | |
| examples=example_sample if example_sample else None, | |
| cache_examples = False | |
| ) | |
| with demo: | |
| gr.HTML( | |
| ''' | |
| <div style="justify-content: center; display: flex;"> | |
| <iframe | |
| src="https://svjack-chatglm3-few-shot-demo.hf.space/?input_list_index=2" | |
| frameborder="0" | |
| width="1400" | |
| height="768" | |
| ></iframe> | |
| </div> | |
| ''' | |
| ) | |
| demo.launch(server_name=None, server_port=None) | |