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os.environ['debug'] = 'true'
import gradio as gr
from GPTagger import *
from langchain.prompts import PromptTemplate
default_prompt = """
Please understand the instructions above and do extraction in the text below.
TEXT:
\"\"\"
{text}
\"\"\"
"""
def ner(
model: str,
nr_calls: int,
tag_name: str,
tag_max_len: int,
text: str,
prompt: str,
key: str,
):
os.environ['OPENAI_API_KEY'] = key
ner_pipeline = NerPipeline(
tag_name=tag_name,
nr_calls=nr_calls,
model=model,
tag_max_len=tag_max_len
)
template = PromptTemplate.from_template(prompt)
extractions = ner_pipeline(text, template, "")
if not extractions:
output = []
else:
output = [
{"entity": tag_name.upper(), "start": item.start, "end": item.end}
for item in extractions
]
return {"text": text, "entities": output}
with gr.Blocks() as demo:
gr.Markdown(
"""
# GPTagger 🏷️
[GPTagger](https://github.com/hnliu-git/GPTagger) is a powerful text tagger that makes use of the GPT model. This tool allows you to extract tags from a given text by leveraging the capabilities of GPT.
Simply specify the tag you want to extract from the text using prompt, you will get them highlighted in the output.
"""
)
with gr.Row():
key = gr.Textbox(label='OpenAI API Key: （We don \'t record your key.)')
with gr.Row():
tag_name = gr.Textbox(label="Tag Name:", placeholder='Enter the tag you want to extract')
tag_max_len = gr.Slider(
minimum=10, maximum=1000, step=10, label="Max length of a tag", value=50
)
with gr.Row():
model = gr.Dropdown(
["gpt-3.5-turbo-0613", "gpt-4-0613"],
label="Model Name:",
value="gpt-3.5-turbo-0613",
)
nr_call = gr.Number(label="nr_of_calls", minimum=1, value=1, precision=0)
with gr.Row():
prompt = gr.TextArea(
placeholder="Enter your prompt here...",
label="Prompt: (Please include the default prompt at the end)",
value=default_prompt,
)
text = gr.TextArea(placeholder="Enter your text here...", label="Text")
btn = gr.Button("Submit")
output = gr.HighlightedText()
btn.click(
ner,
inputs=[
model,
nr_call,
tag_name,
tag_max_len,
text,
prompt,
key
],
outputs=output,
)
demo.launch()
|