File size: 4,167 Bytes
f3b1b8f eddd3f1 f3b1b8f eddd3f1 f3b1b8f eddd3f1 f3b1b8f eddd3f1 f3b1b8f eddd3f1 f3b1b8f eddd3f1 d45041d eddd3f1 f3b1b8f eddd3f1 f3b1b8f eddd3f1 d45041d eddd3f1 f3b1b8f eddd3f1 f3b1b8f eddd3f1 f3b1b8f eddd3f1 f3b1b8f eddd3f1 f3b1b8f eddd3f1 d45041d eddd3f1 f3b1b8f |
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 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
import json
import gradio as gr
from langchain_legal import run_pipeline
def load_json(path: str) -> dict:
with open(path, 'r') as f:
return json.load(f)
def get_ai_final_summary(text_input):
data = run_pipeline(text_input)
return data, data.get('ai_final_summary', 'No answer yet')
STEP_NAMES = [
'Initial assessment',
'Applicable EU regs',
'Applicable national regs',
'Complementary assessment',
]
def get_final_summary(data):
summ = data.get('ai_final_summary')
if summ is None:
return gr.Textbox('No answer yet', label='Final summary')
return gr.Textbox(summ, label='Final summary', show_copy_button=True)
def get_inital_assesment(data):
initial = data.get('ai_first_5', None)
if initial is None:
return gr.Textbox('No llm answer yet', show_label=False)
return gr.Textbox(initial, show_label=False, lines=25)
def get_eu_regs_eval(data):
eval = data.get('ai_eur_lex_eval', None)
if eval is None:
return gr.Textbox('No llm answer yet', show_label=False)
return gr.Textbox(eval, show_label=False, lines=25)
def get_eu_regs_sources(data):
lookup = data.get('eur_lex_lookup', None)
if lookup is None:
return gr.Json({}, show_label=False)
return gr.Json(lookup)
def get_national_regs_eval(data):
eval = data.get('ai_aus_eval', None)
if eval is None:
eval = data.get('ai_ger_eval')
if eval is None:
return gr.Textbox('No llm answer yet', show_label=False)
return gr.Textbox(eval, show_label=False, lines=25)
def get_national_regs_sources(data):
lookup = data.get('aus_lookup', None)
if lookup is None:
lookup = data.get('ger_lookup')
if lookup is None:
return gr.Json({}, show_label=False)
return gr.Json(lookup)
def get_ai_challange(data):
challange = data.get('ai_challange', None)
if challange is None:
return gr.Textbox('No llm answer yet', show_label=False)
return gr.Textbox(challange, show_label=False, lines=50)
def update_ui(data):
return (
get_inital_assesment(data),
get_eu_regs_eval(data),
get_eu_regs_sources(data),
get_national_regs_eval(data),
get_national_regs_sources(data),
get_ai_challange(data),
)
def main():
with gr.Blocks() as demo:
data = gr.State({})
gr.Markdown("# Lawgarithm's LLM legal research tool")
with gr.Column():
text_input = gr.Textbox(
label='Case description',
placeholder='Write your legal case here',
)
submit_btn = gr.Button('Submit')
text_output = gr.Textbox(label='Final Summary')
submit_btn.click(
get_ai_final_summary,
inputs=[text_input],
outputs=[data, text_output],
show_progress='full',
queue=True,
)
with gr.Tabs():
with gr.Tab(STEP_NAMES[0]):
inital_tab_text = gr.Textbox(
placeholder='No answer yet', show_label=False
)
with gr.Tab(STEP_NAMES[1]):
eu_regs_tab_text = gr.Textbox(
placeholder='No answer yet', show_label=False
)
eu_regs_tab_sources = gr.Json({})
with gr.Tab(STEP_NAMES[2]):
nat_regs_tab_text = gr.Textbox(
placeholder='No answer yet', show_label=False
)
nat_regs_tab_sources = gr.Json({})
with gr.Tab(STEP_NAMES[3]):
comp_assesment_tab_text = gr.Textbox(
placeholder='No answer yet', show_label=False
)
data.change(
update_ui,
inputs=data,
outputs=[
inital_tab_text,
eu_regs_tab_text,
eu_regs_tab_sources,
nat_regs_tab_text,
nat_regs_tab_sources,
comp_assesment_tab_text,
],
)
demo.launch()
if __name__ == '__main__':
main()
|