Spaces:
Running
Running
File size: 3,573 Bytes
c16aa6d 9c64352 c16aa6d 9c64352 c16aa6d 9c64352 c16aa6d 9c64352 c16aa6d 9c64352 c16aa6d 9c64352 c16aa6d 9c64352 c16aa6d 9c64352 c16aa6d 9c64352 c16aa6d 9c64352 c16aa6d 9c64352 |
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 |
from dotenv import load_dotenv
load_dotenv(override=True)
import re
import os
import pandas as pd
import json
from typing import List, Dict, Any
import pandas as pd
import gradio as gr
from risk_atlas_nexus.blocks.inference import WMLInferenceEngine
from risk_atlas_nexus.blocks.inference.params import WMLInferenceEngineParams
from risk_atlas_nexus.library import RiskAtlasNexus
from functools import lru_cache
# Load the taxonomies
ran = RiskAtlasNexus() # type: ignore
@lru_cache
def risk_identifier(usecase: str,
model_name_or_path: str = "ibm/granite-20b-code-instruct",
taxonomy: str = "ibm-risk-atlas"): # -> List[Dict[str, Any]]: #pd.DataFrame:
inference_engine = WMLInferenceEngine(
model_name_or_path= model_name_or_path,
credentials={
"api_key": os.environ["WML_API_KEY"],
"api_url": os.environ["WML_API_URL"],
"project_id": os.environ["WML_PROJECT_ID"],
},
parameters=WMLInferenceEngineParams(
max_new_tokens=150, decoding_method="greedy", repetition_penalty=1
), # type: ignore
)
risks = ran.identify_risks_from_usecases( # type: ignore
usecases=[usecase],
inference_engine=inference_engine,
taxonomy=taxonomy,
)[0]
sample_labels = [r.name if r else r.id for r in risks]
out_sec = gr.Markdown("""<h2> Potential Risks </h2> """)
#return out_df
return out_sec, gr.State(risks), gr.Dataset(samples=[r.id for r in risks],
sample_labels=sample_labels,
samples_per_page=50, visible=True, label="Estimated by an LLM.")
@lru_cache
def mitigations(riskid: str, taxonomy: str) -> tuple[gr.Dataset, gr.DataFrame]:
"""
For a specific risk (riskid), returns
(a) related risks - as a dataset
(b) mitigations
"""
related_risk_ids = ran.get_related_risk_ids_by_risk_id(riskid)
action_ids = []
if taxonomy == "ibm-risk-atlas":
# look for actions associated with related risks
if related_risk_ids:
for i in related_risk_ids:
rai = ran.get_risk_actions_by_risk_id(i)
if rai:
action_ids += rai
else:
action_ids = []
else:
# Use only actions related to primary risks
action_ids = ran.get_risk_actions_by_risk_id(riskid)
# Sanitize outputs
if not related_risk_ids:
label = "No related risks found."
samples = None
sample_labels = None
else:
label = f"Risks from other taxonomies related to {riskid}"
samples = related_risk_ids
sample_labels = [i.name for i in ran.get_related_risks_by_risk_id(riskid)] #type: ignore
if not action_ids:
alabel = "No mitigations found."
asamples = None
asample_labels = None
mitdf = pd.DataFrame()
else:
alabel = f"Mitigation actions related to risk {riskid}."
asamples = action_ids
asample_labels = [ran.get_action_by_id(i).description for i in asamples] # type: ignore
asample_name = [ran.get_action_by_id(i).name for i in asamples] #type: ignore
mitdf = pd.DataFrame({"Mitigation": asample_name, "Description": asample_labels})
return (gr.Dataset(samples=samples, label=label, sample_labels=sample_labels, visible=True),
gr.DataFrame(mitdf, wrap=True, show_copy_button=True, show_search="search", label=alabel, visible=True))
|