| import logging | |
| from typing import Dict, List | |
| import streamlit as st | |
| from streamlit_tags import st_tags | |
| from llm_guard.input_scanners.anonymize import default_entity_types | |
| from llm_guard.output_scanners import ( | |
| BanSubstrings, | |
| BanTopics, | |
| Bias, | |
| Code, | |
| Deanonymize, | |
| MaliciousURLs, | |
| NoRefusal, | |
| Refutation, | |
| Regex, | |
| Relevance, | |
| Sensitive, | |
| ) | |
| from llm_guard.output_scanners.sentiment import Sentiment | |
| from llm_guard.output_scanners.toxicity import Toxicity | |
| from llm_guard.vault import Vault | |
| logger = logging.getLogger("llm-guard-playground") | |
| def init_settings() -> (List, Dict): | |
| all_scanners = [ | |
| "BanSubstrings", | |
| "BanTopics", | |
| "Bias", | |
| "Code", | |
| "Deanonymize", | |
| "MaliciousURLs", | |
| "NoRefusal", | |
| "Refutation", | |
| "Regex", | |
| "Relevance", | |
| "Sensitive", | |
| "Sentiment", | |
| "Toxicity", | |
| ] | |
| st_enabled_scanners = st.sidebar.multiselect( | |
| "Select scanners", | |
| options=all_scanners, | |
| default=all_scanners, | |
| help="The list can be found here: https://laiyer-ai.github.io/llm-guard/output_scanners/bias/", | |
| ) | |
| settings = {} | |
| if "BanSubstrings" in st_enabled_scanners: | |
| st_bs_expander = st.sidebar.expander( | |
| "Ban Substrings", | |
| expanded=False, | |
| ) | |
| with st_bs_expander: | |
| st_bs_substrings = st.text_area( | |
| "Enter substrings to ban (one per line)", | |
| value="test\nhello\nworld\n", | |
| height=200, | |
| ).split("\n") | |
| st_bs_match_type = st.selectbox("Match type", ["str", "word"]) | |
| st_bs_case_sensitive = st.checkbox("Case sensitive", value=False) | |
| settings["BanSubstrings"] = { | |
| "substrings": st_bs_substrings, | |
| "match_type": st_bs_match_type, | |
| "case_sensitive": st_bs_case_sensitive, | |
| } | |
| if "BanTopics" in st_enabled_scanners: | |
| st_bt_expander = st.sidebar.expander( | |
| "Ban Topics", | |
| expanded=False, | |
| ) | |
| with st_bt_expander: | |
| st_bt_topics = st_tags( | |
| label="List of topics", | |
| text="Type and press enter", | |
| value=["politics", "religion", "money", "crime"], | |
| suggestions=[], | |
| maxtags=30, | |
| key="bt_topics", | |
| ) | |
| st_bt_threshold = st.slider( | |
| label="Threshold", | |
| value=0.75, | |
| min_value=0.0, | |
| max_value=1.0, | |
| step=0.05, | |
| key="ban_topics_threshold", | |
| ) | |
| settings["BanTopics"] = {"topics": st_bt_topics, "threshold": st_bt_threshold} | |
| if "Bias" in st_enabled_scanners: | |
| st_bias_expander = st.sidebar.expander( | |
| "Bias", | |
| expanded=False, | |
| ) | |
| with st_bias_expander: | |
| st_bias_threshold = st.slider( | |
| label="Threshold", | |
| value=0.75, | |
| min_value=0.0, | |
| max_value=1.0, | |
| step=0.05, | |
| key="bias_threshold", | |
| ) | |
| settings["Bias"] = {"threshold": st_bias_threshold} | |
| if "Code" in st_enabled_scanners: | |
| st_cd_expander = st.sidebar.expander( | |
| "Code", | |
| expanded=False, | |
| ) | |
| with st_cd_expander: | |
| st_cd_languages = st.multiselect( | |
| "Programming languages", | |
| options=["python", "java", "javascript", "go", "php", "ruby"], | |
| default=["python"], | |
| ) | |
| st_cd_mode = st.selectbox("Mode", ["allowed", "denied"], index=0) | |
| settings["Code"] = {"languages": st_cd_languages, "mode": st_cd_mode} | |
| if "MaliciousURLs" in st_enabled_scanners: | |
| st_murls_expander = st.sidebar.expander( | |
| "Malicious URLs", | |
| expanded=False, | |
| ) | |
| with st_murls_expander: | |
| st_murls_threshold = st.slider( | |
| label="Threshold", | |
| value=0.75, | |
| min_value=0.0, | |
| max_value=1.0, | |
| step=0.05, | |
| key="murls_threshold", | |
| ) | |
| settings["MaliciousURLs"] = {"threshold": st_murls_threshold} | |
| if "NoRefusal" in st_enabled_scanners: | |
| st_no_ref_expander = st.sidebar.expander( | |
| "No refusal", | |
| expanded=False, | |
| ) | |
| with st_no_ref_expander: | |
| st_no_ref_threshold = st.slider( | |
| label="Threshold", | |
| value=0.5, | |
| min_value=0.0, | |
| max_value=1.0, | |
| step=0.05, | |
| key="no_ref_threshold", | |
| ) | |
| settings["NoRefusal"] = {"threshold": st_no_ref_threshold} | |
| if "Refutation" in st_enabled_scanners: | |
| st_refu_expander = st.sidebar.expander( | |
| "Refutation", | |
| expanded=False, | |
| ) | |
| with st_refu_expander: | |
| st_refu_threshold = st.slider( | |
| label="Threshold", | |
| value=0.5, | |
| min_value=0.0, | |
| max_value=1.0, | |
| step=0.05, | |
| key="refu_threshold", | |
| ) | |
| settings["Refutation"] = {"threshold": st_refu_threshold} | |
| if "Regex" in st_enabled_scanners: | |
| st_regex_expander = st.sidebar.expander( | |
| "Regex", | |
| expanded=False, | |
| ) | |
| with st_regex_expander: | |
| st_regex_patterns = st.text_area( | |
| "Enter patterns to ban (one per line)", | |
| value="Bearer [A-Za-z0-9-._~+/]+", | |
| height=200, | |
| ).split("\n") | |
| st_regex_type = st.selectbox( | |
| "Match type", | |
| ["good", "bad"], | |
| index=1, | |
| help="good: allow only good patterns, bad: ban bad patterns", | |
| ) | |
| settings["Regex"] = {"patterns": st_regex_patterns, "type": st_regex_type} | |
| if "Relevance" in st_enabled_scanners: | |
| st_rele_expander = st.sidebar.expander( | |
| "Relevance", | |
| expanded=False, | |
| ) | |
| with st_rele_expander: | |
| st_rele_threshold = st.slider( | |
| label="Threshold", | |
| value=0.5, | |
| min_value=-1.0, | |
| max_value=1.0, | |
| step=0.05, | |
| key="rele_threshold", | |
| help="The minimum cosine similarity (-1 to 1) between the prompt and output for the output to be considered relevant.", | |
| ) | |
| settings["Relevance"] = {"threshold": st_rele_threshold} | |
| if "Sensitive" in st_enabled_scanners: | |
| st_sens_expander = st.sidebar.expander( | |
| "Sensitive", | |
| expanded=False, | |
| ) | |
| with st_sens_expander: | |
| st_sens_entity_types = st_tags( | |
| label="Sensitive entities", | |
| text="Type and press enter", | |
| value=default_entity_types, | |
| suggestions=default_entity_types | |
| + ["DATE_TIME", "NRP", "LOCATION", "MEDICAL_LICENSE", "US_PASSPORT"], | |
| maxtags=30, | |
| key="sensitive_entity_types", | |
| ) | |
| st.caption( | |
| "Check all supported entities: https://microsoft.github.io/presidio/supported_entities/#list-of-supported-entities" | |
| ) | |
| settings["Sensitive"] = {"entity_types": st_sens_entity_types} | |
| if "Sentiment" in st_enabled_scanners: | |
| st_sent_expander = st.sidebar.expander( | |
| "Sentiment", | |
| expanded=False, | |
| ) | |
| with st_sent_expander: | |
| st_sent_threshold = st.slider( | |
| label="Threshold", | |
| value=-0.1, | |
| min_value=-1.0, | |
| max_value=1.0, | |
| step=0.1, | |
| key="sentiment_threshold", | |
| help="Negative values are negative sentiment, positive values are positive sentiment", | |
| ) | |
| settings["Sentiment"] = {"threshold": st_sent_threshold} | |
| if "Toxicity" in st_enabled_scanners: | |
| st_tox_expander = st.sidebar.expander( | |
| "Toxicity", | |
| expanded=False, | |
| ) | |
| with st_tox_expander: | |
| st_tox_threshold = st.slider( | |
| label="Threshold", | |
| value=0.0, | |
| min_value=-1.0, | |
| max_value=1.0, | |
| step=0.05, | |
| key="toxicity_threshold", | |
| help="A negative value (closer to 0 as the label output) indicates toxicity in the text, while a positive logit (closer to 1 as the label output) suggests non-toxicity.", | |
| ) | |
| settings["Toxicity"] = {"threshold": st_tox_threshold} | |
| return st_enabled_scanners, settings | |
| def get_scanner(scanner_name: str, vault: Vault, settings: Dict): | |
| logger.debug(f"Initializing {scanner_name} scanner") | |
| if scanner_name == "BanSubstrings": | |
| return BanSubstrings( | |
| substrings=settings["substrings"], | |
| match_type=settings["match_type"], | |
| case_sensitive=settings["case_sensitive"], | |
| ) | |
| if scanner_name == "BanTopics": | |
| return BanTopics(topics=settings["topics"], threshold=settings["threshold"]) | |
| if scanner_name == "Bias": | |
| return Bias(threshold=settings["threshold"]) | |
| if scanner_name == "Deanonymize": | |
| return Deanonymize(vault=vault) | |
| if scanner_name == "Code": | |
| mode = settings["mode"] | |
| allowed_languages = None | |
| denied_languages = None | |
| if mode == "allowed": | |
| allowed_languages = settings["languages"] | |
| elif mode == "denied": | |
| denied_languages = settings["languages"] | |
| return Code(allowed=allowed_languages, denied=denied_languages) | |
| if scanner_name == "MaliciousURLs": | |
| return MaliciousURLs(threshold=settings["threshold"]) | |
| if scanner_name == "NoRefusal": | |
| return NoRefusal(threshold=settings["threshold"]) | |
| if scanner_name == "Refutation": | |
| return Refutation(threshold=settings["threshold"]) | |
| if scanner_name == "Regex": | |
| match_type = settings["type"] | |
| good_patterns = None | |
| bad_patterns = None | |
| if match_type == "good": | |
| good_patterns = settings["patterns"] | |
| elif match_type == "bad": | |
| bad_patterns = settings["patterns"] | |
| return Regex(good_patterns=good_patterns, bad_patterns=bad_patterns) | |
| if scanner_name == "Relevance": | |
| return Relevance(threshold=settings["threshold"]) | |
| if scanner_name == "Sensitive": | |
| return Sensitive(entity_types=settings["entity_types"]) | |
| if scanner_name == "Sentiment": | |
| return Sentiment(threshold=settings["threshold"]) | |
| if scanner_name == "Toxicity": | |
| return Toxicity(threshold=settings["threshold"]) | |
| raise ValueError("Unknown scanner name") | |
| def scan( | |
| vault: Vault, enabled_scanners: List[str], settings: Dict, prompt: str, text: str | |
| ) -> (str, Dict[str, bool], Dict[str, float]): | |
| sanitized_output = text | |
| results_valid = {} | |
| results_score = {} | |
| with st.status("Scanning output...", expanded=True) as status: | |
| for scanner_name in enabled_scanners: | |
| st.write(f"{scanner_name} scanner...") | |
| scanner = get_scanner( | |
| scanner_name, vault, settings[scanner_name] if scanner_name in settings else {} | |
| ) | |
| sanitized_output, is_valid, risk_score = scanner.scan(prompt, sanitized_output) | |
| results_valid[scanner_name] = is_valid | |
| results_score[scanner_name] = risk_score | |
| status.update(label="Scanning complete", state="complete", expanded=False) | |
| return sanitized_output, results_valid, results_score | |