In today’s digital landscape, crafting a comprehensive Privacy Policy is crucial for businesses to ensure legal compliance and customer trust. However, the task can be daunting, requiring expertise in legal nuances and regulatory requirements. To address this challenge, a Privacy Policy Generator powered by Lyzr Automata offers a sophisticated solution.
Many businesses struggle to create tailored Privacy Policies that meet legal standards and effectively communicate their data handling practices to customers. This often leads to ambiguity and potential legal risks, as generic templates may not suffice in complex regulatory environments.
The Privacy Policy Generator leverages Lyzr Automata, a versatile AI agent and multi-agent framework designed for complex cognitive tasks. By integrating OpenAI’s powerful language model, it automates the creation of Privacy Policies tailored to specific business contexts. Users input company details such as name, type, operations, and website, prompting the AI to draft a detailed policy that meets legal requirements and reflects current best practices.
What is Lyzr Automata?
Lyzr Automata represents a cutting-edge advancement in AI-driven automation, specifically tailored for cognitive tasks requiring deep domain knowledge and nuanced decision-making. It functions as a multi-agent framework, capable of orchestrating interactions between various AI agents to tackle complex problems collaboratively.
At its core, Lyzr Automata harnesses the power of OpenAI’s language models, such as GPT-4 Turbo, to interpret and generate human-like text based on input instructions. This capability enables it to simulate expertise in diverse domains, including legal and policy crafting, by embodying specialized personas like a Privacy Policy Expert.
Benefits and Impact
The integration of Lyzr Automata in the Privacy Policy Generator offers several key benefits:
Accuracy and Compliance: Ensures policies are accurate and compliant with relevant regulations.
Efficiency: Reduces time and resources required for manual policy drafting.
Customization: Tailors policies to specific business contexts, enhancing relevance and clarity.
Trust and Transparency: Builds trust with customers by transparently outlining data handling practices.
Setting Up the Environment
Imports:
Imports necessary libraries: streamlit, libraries from lyzr_automata
pip install lyzr_automata streamlit
import streamlit as st
from lyzr_automata.ai_models.openai import OpenAIModel
from lyzr_automata import Agent,Task
from lyzr_automata.pipelines.linear_sync_pipeline import LinearSyncPipeline
from PIL import Image
Sidebar Configuration
api = st.sidebar.text_input("Enter our OPENAI API KEY Here", type="password")
if api:
openai_model = OpenAIModel(
api_key=api,
parameters={
"model": "gpt-4-turbo-preview",
"temperature": 0.2,
"max_tokens": 1500,
},
)
else:
st.sidebar.error("Please Enter Your OPENAI API KEY")
if api:: Checks if an API key is entered.
openai_model = OpenAIModel(): If a key is entered, creates an OpenAIModel object with the provided API key, model parameters (gpt-4-turbo-preview, temperature, max_tokens).
else: If no key is entered, displays an error message in the sidebar.
Privacy Policy Generation Function:
def privacy_policy_generator(name, company_type, operation, website):
policy_agent = Agent(
prompt_persona=f"You are an Expert in Privacy policy crafting.",
role="Privacy Policy Expert",
)
policy_task = Task(
name="Privacy Policy",
output_type=OutputType.TEXT,
input_type=InputType.TEXT,
model=openai_model,
agent=policy_agent,
log_output=True,
instructions=f"""
Draft a privacy policy for a {company_type} company called {name} that operates in the state of {operation}.
The company website is {website} .
""",
)
output = LinearSyncPipeline(
name="Privacy Policy Generation",
completion_message="Privacy Policy Generated!",
tasks=[
policy_task
],
).run()
return output[0]['task_output']
The privacy_policy_generator function takes name, company_type, operation, and website as inputs for generating the policy.
An Agent object (policy_agent) is created, defining the prompt persona and role for interacting with the model.
A Task object (policy_task) is created, specifying the task name, desired output format (text), input format (text), the OpenAIModel to use, the policy_agent, enabling logging, and providing detailed instructions for the model. These instructions include company details, website, and state of operation.
A LinearSyncPipeline object (output) is created, naming the pipeline, setting a completion message, and defining the list of tasks (in this case, only the policy_task). This pipeline executes the task and retrieves the output.
The function returns the generated policy text (task_output).
User Code Input:
company_name = st.text_input("Enter Company Name")
company_type = st.text_input("Enter Company Type")
state_of_operation = st.text_input("Enter Company's State of Operation")
website = st.text_input("Enter Company Website")
Text input fields are displayed using st.text_input to capture company name, type, state, and website.
Generate Button and Output Display:
if st.button("Generate"):
solution = privacy_policy_generator(company_name,company_type,state_of_operation,website)
st.markdown(solution)
Clicking the “Generate” button triggers the code within the button’s if statement.
The privacy_policy_generator function is called with the user-provided information.
The returned generated policy (solution) is displayed as markdown using st.markdown.
Running the App
Finally, run the app using the following command in your terminal:
streamlit run app.py
try it now: https://lyzr-privacy-policy.streamlit.app/
Code: https://github.com/harshit-lyzr/privacy_policy_generator/
For more information explore the website: Lyzr
Contibute to Our Project: https://github.com/LyzrCore/lyzr-automata
Top comments (0)