app.py

import os
os.system('pip install transformers')
os.system('pip install gradio')
os.system('pip install requests')

import requests
import gradio as gr
from huggingface_hub import InferenceClient
from transformers import pipeline

# Inference client for chat completion
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Different pipelines for different tasks
qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")

def respond(message, system_message, max_tokens, temperature, top_p):
    messages = [{"role": "system", "content": system_message}]
    messages.append({"role": "user", "content": message})

    response = ""
    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content
        if token is not None:
            response += token
    return response

# GDPR Compliance Expert
def evaluate_gdpr_compliance(audit_data):
    system_message = (
        "You are an expert GDPR compliance officer. Assess the audit data for compliance with GDPR regulations. "
        "Provide an analysis that identifies any compliance issues and suggestions for remediation. "
        "Ensure a thorough evaluation of data processing, storage, and protection practices in line with GDPR requirements."
    )
    compliance_analysis = respond(audit_data, system_message, max_tokens=1024, temperature=0.7, top_p=0.95)
    return compliance_analysis

# PCI Compliance Expert
def evaluate_pci_compliance(audit_data):
    system_message = (
        "You are an expert PCI compliance officer. Assess the audit data for compliance with PCI DSS regulations. "
        "Provide an analysis that identifies any compliance issues and suggestions for remediation. "
        "Ensure a thorough evaluation of payment card data security, storage, and processing practices in line with PCI requirements."
    )
    compliance_analysis = respond(audit_data, system_message, max_tokens=1024, temperature=0.7, top_p=0.95)
    return compliance_analysis

# Custom CSS for the specified theme
custom_css = """
body {
    background-color: #000000;
    color: #ffffff;
    font-family: Arial, sans-serif;
}
.gradio-container {
    max-width: 1000px;
    margin: 0 auto;
    padding: 20px;
    background-color: #000000;
    border: 1px solid #e0e0e0;
    border-radius: 8px;
    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
}
.gr-button {
    background-color: #000000 !important;
    border-color: #ff0000 !important;
    color: #ff0000 !important;
    margin: 5px;
}
.gr-button:hover {
    background-color: #ff0000 !important;
    border-color: #ff0000 !important;
    color: #000000 !important;
}
textarea.gr-textbox {
    border-radius: 4px !important;
    border: 2px solid #ff0000 !important;
    background-color: #ffffff !important;
    color: #000000 !important;
}
textarea.gr-textbox:focus {
    border-color: #ff0000 !important;
    outline: 0 !important;
    box-shadow: 0 0 0 0.2rem rgba(255, 0, 0, 0.5) !important;
}
#flagging-button {
    display: none;
}
footer {
    display: none;
}
.chatbox .chat-container .chat-message {
    background-color: #000000 !important;
    color: #ffffff !important;
}
.chatbox .chat-container .chat-message-input {
    background-color: #000000 !important;
    color: #ffffff !important;
}
.gr-markdown {
    background-color: #000000 !important;
    color: #ffffff !important;
}
.gr-markdown h1, .gr-markdown h2, .gr-markdown h3, .gr-markdown h4, .gr-markdown h5, .gr-markdown h6, .gr-markdown p, .gr-markdown ul, .gr-markdown ol, .gr-markdown li {
    color: #ffffff !important;
}
.score-box {
    width: 60px;
    height: 60px;
    display: flex;
    align-items: center
}
.label-hidden .gr-label {
    display: none;
}
"""

# Gradio Interface
with gr.Blocks(css=custom_css) as demo:
    with gr.Column():
        gr.Markdown("# GDPR and PCI Compliance Evaluation\n### Provide Audit Data for Compliance Check")
        audit_data = gr.Textbox(lines=5, placeholder="Enter audit data here...", label="Audit Data", elem_classes="label-hidden")
        
        gdpr_compliance = gr.Textbox(lines=10, placeholder="GDPR Compliance Analysis...", label="GDPR Compliance Analysis", elem_classes="label-hidden")
        pci_compliance = gr.Textbox(lines=10, placeholder="PCI Compliance Analysis...", label="PCI Compliance Analysis", elem_classes="label-hidden")
        
        def run_compliance_checks(audit_data):
            gdpr_analysis = evaluate_gdpr_compliance(audit_data)
            pci_analysis = evaluate_pci_compliance(audit_data)
            return gdpr_analysis, pci_analysis
        
        check_compliance_btn = gr.Button("Run Compliance Checks")
        check_compliance_btn.click(run_compliance_checks, inputs=[audit_data], outputs=[gdpr_compliance, pci_compliance])
        
        clear_btn = gr.Button("Clear")
        clear_btn.click(lambda: ("", "", ""), None, [audit_data, gdpr_compliance, pci_compliance])

demo.launch()