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import logging |
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import os |
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import traceback |
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import pandas as pd |
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import streamlit as st |
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from llm_guard.vault import Vault |
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from output import init_settings as init_output_settings |
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from output import scan as scan_output |
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from prompt import init_settings as init_prompt_settings |
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from prompt import scan as scan_prompt |
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PROMPT = "prompt" |
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OUTPUT = "output" |
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vault = Vault() |
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st.set_page_config( |
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page_title="LLM Guard Playground", |
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layout="wide", |
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initial_sidebar_state="expanded", |
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menu_items={ |
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"About": "https://llm-guard.com/", |
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}, |
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) |
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logger = logging.getLogger("llm-guard-playground") |
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logger.setLevel(logging.INFO) |
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st.sidebar.header( |
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""" |
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Scanning prompt and output using [LLM Guard](https://laiyer-ai.github.io/llm-guard/) |
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""" |
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) |
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scanner_type = st.sidebar.selectbox("Type", [PROMPT, OUTPUT], index=0) |
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st_fail_fast = st.sidebar.checkbox( |
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"Fail fast", value=False, help="Stop scanning after first failure" |
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) |
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enabled_scanners = None |
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settings = None |
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if scanner_type == PROMPT: |
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enabled_scanners, settings = init_prompt_settings() |
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elif scanner_type == OUTPUT: |
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enabled_scanners, settings = init_output_settings() |
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st.subheader("Guard Prompt" if scanner_type == PROMPT else "Guard Output") |
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with st.expander("About", expanded=False): |
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st.info( |
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"""LLM-Guard is a comprehensive tool designed to fortify the security of Large Language Models (LLMs). |
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\n\n[Code](https://github.com/laiyer-ai/llm-guard) | |
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[Documentation](https://llm-guard.com/)""" |
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) |
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analyzer_load_state = st.info("Starting LLM Guard...") |
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analyzer_load_state.empty() |
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prompt_examples_folder = "./examples/prompt" |
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output_examples_folder = "./examples/output" |
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prompt_examples = [f for f in os.listdir(prompt_examples_folder) if f.endswith(".txt")] |
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output_examples = [f for f in os.listdir(output_examples_folder) if f.endswith(".txt")] |
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if scanner_type == PROMPT: |
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st_prompt_example = st.selectbox("Select prompt example", prompt_examples, index=0) |
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with open(os.path.join(prompt_examples_folder, st_prompt_example), "r") as file: |
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prompt_example_text = file.read() |
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st_prompt_text = st.text_area( |
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label="Enter prompt", value=prompt_example_text, height=200, key="prompt_text_input" |
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) |
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elif scanner_type == OUTPUT: |
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col1, col2 = st.columns(2) |
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st_prompt_example = col1.selectbox("Select prompt example", prompt_examples, index=0) |
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with open(os.path.join(prompt_examples_folder, st_prompt_example), "r") as file: |
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prompt_example_text = file.read() |
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st_prompt_text = col1.text_area( |
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label="Enter prompt", value=prompt_example_text, height=300, key="prompt_text_input" |
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) |
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st_output_example = col2.selectbox("Select output example", output_examples, index=0) |
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with open(os.path.join(output_examples_folder, st_output_example), "r") as file: |
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output_example_text = file.read() |
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st_output_text = col2.text_area( |
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label="Enter output", value=output_example_text, height=300, key="output_text_input" |
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) |
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st_result_text = None |
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st_analysis = None |
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st_is_valid = None |
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try: |
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with st.form("text_form", clear_on_submit=False): |
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submitted = st.form_submit_button("Process") |
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if submitted: |
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results = {} |
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if scanner_type == PROMPT: |
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st_result_text, results = scan_prompt( |
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vault, enabled_scanners, settings, st_prompt_text, st_fail_fast |
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) |
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elif scanner_type == OUTPUT: |
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st_result_text, results = scan_output( |
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vault, enabled_scanners, settings, st_prompt_text, st_output_text, st_fail_fast |
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) |
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st_is_valid = all(item["is_valid"] for item in results) |
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st_analysis = results |
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except Exception as e: |
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logger.error(e) |
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traceback.print_exc() |
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st.error(e) |
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if st_is_valid is not None: |
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st.subheader(f"Results - {'valid' if st_is_valid else 'invalid'}") |
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col1, col2 = st.columns(2) |
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with col1: |
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st.text_area(label="Sanitized text", value=st_result_text, height=400) |
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with col2: |
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st.table(pd.DataFrame(st_analysis)) |
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