initial commit

.gitignore

@@ -0,0 +1 @@
+.DS_Store

LICENSE.md

@@ -0,0 +1,201 @@
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app.py

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+import os
+import gradio as gr
+import logging
+from executor import risk_identifier, mitigations
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class UI:
+
+    def __init__(self):
+        """Load any static assets """
+        self.load_css()
+
+    def header_block(self):
+        """Title/description"""
+ 
+        with open("static/text/header.md", "r") as f:
+            content = f.read()
+
+        gr.Markdown(content)
+        gr.Markdown("---")
+        gr.Markdown("<br>")
+
+    def risk_extraction(self):
+
+        with open("static/text/sec1.md") as f:
+            content = f.read()
+
+        with gr.Row():
+            gr.Markdown(content)
+
+        with gr.Row(equal_height=True):
+            with gr.Column(variant="compact", scale=1):
+                self.usecase = gr.TextArea(
+                    label="Intent",
+                    interactive=True,
+                    info="Describe the intent of the application, or choose from one of the examples below."
+                )
+                self.taxonomy = gr.Dropdown(
+                    choices=["ibm-risk-atlas", "mit-ai-risk-repository", "nist-ai-rmf", "ibm-granite-guardian"],
+                    multiselect=False,
+                    value="ibm-risk-atlas",
+                    label="Choose a risk taxonomy.",
+                    info="The risk taxonomy defines a wide range of risks, their classifications, and potential mitigations.",
+                    interactive=True,
+                )
+                self.model_name_or_path = gr.Dropdown(
+                    choices=['codellama/codellama-34b-instruct-hf', 'google/flan-t5-xl', 'google/flan-t5-xxl', 'google/flan-ul2', 'ibm/granite-13b-instruct-v2', 'ibm/granite-20b-code-instruct', 'ibm/granite-20b-multilingual', 'ibm/granite-3-2-8b-instruct-preview-rc', 'ibm/granite-3-2b-instruct', 'ibm/granite-3-8b-instruct', 'ibm/granite-34b-code-instruct', 'ibm/granite-3b-code-instruct', 'ibm/granite-8b-code-instruct', 'ibm/granite-guardian-3-2b', 'ibm/granite-guardian-3-8b', 'meta-llama/llama-2-13b-chat', 'meta-llama/llama-3-1-70b-instruct', 'meta-llama/llama-3-1-8b-instruct', 'meta-llama/llama-3-2-11b-vision-instruct', 'meta-llama/llama-3-2-1b-instruct', 'meta-llama/llama-3-2-3b-instruct', 'meta-llama/llama-3-2-90b-vision-instruct', 'meta-llama/llama-3-3-70b-instruct', 'meta-llama/llama-3-405b-instruct', 'meta-llama/llama-guard-3-11b-vision', 'mistralai/mistral-large', 'mistralai/mixtral-8x7b-instruct-v01'],
+                    value="ibm/granite-20b-code-instruct",
+                    multiselect=False,
+                    label="Choose language model to use",
+                    info="Language model used to assess risks (This is not the model being assessed).",
+                    interactive=True
+                )
+                examples = gr.Examples([["In a medical chatbot, generative AI can be employed to create a triage system that assesses patients' symptoms and provides immediate, contextually relevant advice based on their medical history and current condition. The chatbot can analyze the patient's input, identify potential medical issues, and offer tailored recommendations or insights to the patient or healthcare provider. This can help streamline the triage process, ensuring that patients receive the appropriate level of care and attention, and ultimately improving patient outcomes.", "ibm-risk-atlas"],
+                                        ["Building a customer support agent that automatically triages common problems with services.", "ibm-risk-atlas"]],
+                                       [self.usecase, self.taxonomy],
+                                        label='Example use cases', example_labels=["Medical chatbot", "Customer service agent"] 
+                                       )
+                self.risk_execute = gr.Button("Submit")
+        
+            with gr.Column(variant="panel", scale=2):
+            
+                gr.Markdown(
+                    """<h2 style='color: black;'> Potential Risks </h2> """
+                )
+                self.assessed_risks = gr.Dataset(label=None)
+            # gr.DataFrame(
+            #     headers=["name", "description", "concern"],
+            #     datatype=["str", "str", "str"], 
+            #     label="Risks",
+            #     visible=True, 
+            #     wrap=True,
+            #     show_copy_button=True,
+            #     show_search="filter",
+            #     type='pandas')
+
+                gr.Markdown(
+                """<h2 style='color: black;'> Related Risks </h2> 
+                Select a potential risk above to check for related risks.
+                """
+                )
+                self.relatedrisks = gr.Dataset(label=None, visible=False)
+
+                gr.Markdown(
+                """<h2 style='color: black;'> Mitigations </h2> 
+                Select a potential risk to determine possible mitigations. """
+                )
+                self.mitigations = gr.Dataset(label=None, visible=False)
+            
+        gr.Markdown("---")
+        gr.Markdown("<br>")
+
+   
+    def load_css(self):
+        with open("static/style.css", "r") as file: 
+            self.css = file.read()
+
+    def layout(self):
+        """Assemble the overall layout""" 
+
+        with gr.Blocks(theme=gr.themes.Default()) as demo:
+
+            self.header_block()
+            self.risks = gr.State()
+
+            # Risk assessment based on user intents
+            self.risk_extraction()
+
+            # Register event listeners
+            self.risk_execute.click(
+                fn=risk_identifier,
+                inputs=[
+                    self.usecase,
+                    self.model_name_or_path,
+                    self.taxonomy
+                ],
+                outputs=[self.risks, self.assessed_risks],
+                api_name="risk_identifier"
+            )
+            self.assessed_risks.select(
+                fn=mitigations,
+                inputs=[self.assessed_risks, self.taxonomy], 
+                # NOTETOSELF: Intent based risk is stored in self.risk (if needed)
+                outputs=[self.relatedrisks, self.mitigations]
+            )
+        return demo
+
+
+    def run(self): 
+        self.ui = self.layout()
+        self.ui.queue().launch(allowed_paths=["static/"])
+
+
+if __name__ == "__main__":
+    ui = UI()
+    ui.run()

executor.py

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+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+import re
+import os
+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()
+
+
+@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=100, decoding_method="greedy", repetition_penalty=1
+    #     ),  # type: ignore
+    # )
+
+    # risks = ran.identify_risks_from_usecase(
+    #     usecase=usecase,
+    #     inference_engine=inference_engine,
+    #     taxonomy=taxonomy,
+    # )
+    risks = ' ["Harmful code generation", "Hallucination", "Harmful output", "Toxic output", "Spreading toxicity", "Spreading disinformation", "Nonconsensual use", "Non-disclosure", "Data contamination", "Data acquisition restrictions", "Data usage rights restrictions", "Confidential data in prompt", "Confidential information in data", "Personal information in prompt", "Personal information in data", "IP information in prompt",'
+
+
+    if isinstance(risks, str):
+        # Translate LLM output to Risk catalog
+        out = []
+        try:
+            risks = json.loads(risks)
+        except json.JSONDecodeError:
+            
+            # Fallback to regex - will skip any partial categories
+            risks = re.findall(r'"(.*?)"', risks)
+
+        for risk in risks:
+            k = list(filter(lambda r: r.name == risk, ran._ontology.risks)) # type: ignore
+            out+= ([i.model_dump() for i in k])
+
+    elif isinstance(risks, List):
+        # FIXME: assumes that the output is structured - not sure if that's correct.
+        out = risks
+    
+    #out_df = pd.DataFrame(out)
+        
+    #return out_df
+    return gr.State(out), gr.Dataset(samples=[i['id'] for i in out], 
+                                     sample_labels=[i['name'] for i in out], samples_per_page=50, visible=True, label="Estimated by an LLM.")
+    
+
+@lru_cache
+def mitigations(riskid: str, taxonomy: str) -> tuple[gr.Dataset, gr.Dataset]:
+    
+    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 = "Related risks"
+        samples = related_risk_ids
+        sample_labels = [i.name for i in ran.get_related_risks_by_risk_id(riskid)]
+
+    if not action_ids:
+        alabel = "No mitigations found."
+        asamples = None
+        asample_labels = None
+    else:
+        alabel = ""
+        asamples = action_ids
+        asample_labels = [ran.get_action_by_id(i).description for i in action_ids]
+
+
+    return (gr.Dataset(samples=samples, label=label, sample_labels=sample_labels, visible=True),
+            gr.Dataset(samples=asamples, label=alabel, sample_labels=asample_labels, visible=True))

requirements.txt

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+gradio
+linkml
+linkml_runtime
+risk-atlas-nexus @ git+https://github.com/IBM/risk-atlas-nexus

static/style.css

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+html {
+	font-family: Inter;
+	font-size: 16px;
+	font-weight: 400;
+	line-height: 1.5;
+	-webkit-text-size-adjust: 100%;
+	background: #fff;
+	color: #323232;
+	-webkit-font-smoothing: antialiased;
+	-moz-osx-font-smoothing: grayscale;
+	text-rendering: optimizeLegibility;
+}
+
+:root {
+	--space: 1;
+	--vspace: calc(var(--space) * 1rem);
+	--vspace-0: calc(3 * var(--space) * 1rem);
+	--vspace-1: calc(2 * var(--space) * 1rem);
+	--vspace-2: calc(1.5 * var(--space) * 1rem);
+	--vspace-3: calc(0.5 * var(--space) * 1rem);
+}
+
+.app {
+	max-width: 748px !important;
+}
+
+.prose p {
+	margin: var(--vspace) 0;
+	line-height: var(--vspace * 2);
+	font-size: 1rem;
+}
+
+code {
+	font-family: "Inconsolata", sans-serif;
+	font-size: 16px;
+}
+
+h1,
+h1 code {
+	font-weight: 400;
+	line-height: calc(2.5 / var(--space) * var(--vspace));
+}
+
+h1 code {
+	background: none;
+	border: none;
+	letter-spacing: 0.05em;
+	padding-bottom: 5px;
+	position: relative;
+	padding: 0;
+}
+
+h2 {
+	margin: var(--vspace-1) 0 var(--vspace-2) 0;
+	line-height: 1em;
+}
+
+h3,
+h3 code {
+	margin: var(--vspace-1) 0 var(--vspace-2) 0;
+	line-height: 1em;
+}
+
+h4,
+h5,
+h6 {
+	margin: var(--vspace-3) 0 var(--vspace-3) 0;
+	line-height: var(--vspace);
+}
+
+.bigtitle,
+h1,
+h1 code {
+	font-size: calc(8px * 4.5);
+	word-break: break-word;
+}
+
+.title,
+h2,
+h2 code {
+	font-size: calc(8px * 3.375);
+	font-weight: lighter;
+	word-break: break-word;
+	border: none;
+	background: none;
+}
+
+.subheading1,
+h3,
+h3 code {
+	font-size: calc(8px * 1.8);
+	font-weight: 600;
+	border: none;
+	background: none;
+	letter-spacing: 0.1em;
+	text-transform: uppercase;
+}
+
+h2 code {
+	padding: 0;
+	position: relative;
+	letter-spacing: 0.05em;
+}
+
+blockquote {
+	font-size: calc(8px * 1.1667);
+	font-style: italic;
+	line-height: calc(1.1667 * var(--vspace));
+	margin: var(--vspace-2) var(--vspace-2);
+}
+
+.subheading2,
+h4 {
+	font-size: calc(8px * 1.4292);
+	text-transform: uppercase;
+	font-weight: 600;
+}
+
+.subheading3,
+h5 {
+	font-size: calc(8px * 1.2917);
+	line-height: calc(1.2917 * var(--vspace));
+
+	font-weight: lighter;
+	text-transform: uppercase;
+	letter-spacing: 0.15em;
+}
+
+h6 {
+	font-size: calc(8px * 1.1667);
+	font-size: 1.1667em;
+	font-weight: normal;
+	font-style: italic;
+	font-family: "le-monde-livre-classic-byol", serif !important;
+	letter-spacing: 0px !important;
+}
+
+#start .md > *:first-child {
+	margin-top: 0;
+}
+
+h2 + h3 {
+	margin-top: 0;
+}
+
+.md hr {
+	border: none;
+	border-top: 1px solid var(--block-border-color);
+	margin: var(--vspace-2) 0 var(--vspace-2) 0;
+}
+.prose ul {
+	margin: var(--vspace-2) 0 var(--vspace-1) 0;
+}
+
+.gap {
+	gap: 0;
+}

static/text/header.md

@@ -0,0 +1,9 @@
+
+
+<img src="/gradio_api/file=static/images/Risk_Atlas_Nexus_vector.png" width=300 style="display:block;margin:0 auto;">
+<h1 style='text-align: center; color: black;'> Risk Atlas Nexus </h1>
+
+
+Risk Atlas Nexus aims to provide tooling to help bring together disparate resources related to governance of foundation models. We support a community driven approach to curating and cataloguing resources such as datasets, benchmarks and mitigations. Our goal is to turn abstract risk definitions into actionable workflows that streamline AI governance processes. By connecting fragmented resources, Risk Atlas Nexus seeks to fill a critical gap in AI governance, enabling stakeholders to build more robust, transparent, and accountable systems. Risk Atlas Nexus builds on the [IBM AI Risk Atlas](https://www.ibm.com/docs/en/watsonx/saas?topic=ai-risk-atlas) making this educational resource a nexus of governance assets and tooling. An AI System's Knowledge Graph is used to provide a unified structure that links and contextualize the very heterogeneous domain data.
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+Our intention is to create a starting point for an open AI Systems ontology whose focus is on risk and that the community can extend and enhance. This ontology serves as the foundation that unifies innovation and tooling in the AI risk space. By lowering the barrier to entry for developers, it fosters a governance-first approach to AI solutions, while also inviting the broader community to contribute their own tools and methodologies to expand its impact. [Source code](https://github.com/IBM/risk-atlas-nexus/).<br>

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+<h1 style='text-align: left; color: black;'> Risk assessment based on intent </h1>
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+Assess potential risks for a use case description based on risk taxonomies.