File size: 1,590 Bytes
5953ef9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
import json
import gradio as gr
from rex.utils.initialization import set_seed_and_log_path
from src.task import SchemaGuidedInstructBertTask
set_seed_and_log_path(log_path="debug.log")
task = SchemaGuidedInstructBertTask.from_taskdir(
"mirror_outputs/Mirror_Pretrain_AllExcluded_2",
load_best_model=True,
initialize=False,
dump_configfile=False,
update_config={
"regenerate_cache": False,
},
)
def ask_mirror(instruction, schema, text):
input_data = {
"id": "app",
"instruction": instruction,
"schema": json.loads(schema),
"text": text,
"ans": {},
}
results = task.predict(input_data)
return results
with gr.Blocks() as demo:
gr.Markdown("# 🪞Mirror")
gr.Markdown(
"🪞Mirror can help you deal with a wide range of Natural Language Understanding and Information Extraction tasks."
)
gr.Markdown(
"[[paper]](https://arxiv.org/abs/2311.05419) | [[code]](https://github.com/Spico197/Mirror)"
)
instruction = gr.Textbox(label="Instruction")
schema = gr.Textbox(
label="schema",
placeholder='{"cls": ["class1", "class2"], "ent": ["type1", "type2"], "rel": ["relation1", "relation2"]} leave it as {} to support span extraction.',
)
text = gr.TextArea(label="Text")
output = gr.Textbox(label="Output")
submit_btn = gr.Button("Ask Mirror")
submit_btn.click(ask_mirror, inputs=[instruction, schema, text], outputs=output)
gr.Markdown("Made by Mirror Team w/ 💖")
if __name__ == "__main__":
demo.launch()
|