import gradio as gr
from huggingface_hub import InferenceClient

# Initialize the Hugging Face Inference Client
client = InferenceClient()

# Function to interact with the Qwen2.5-32B model and analyze code for compliance
def analyze_compliance(code, compliance_standard):
    prompt = f"Analyze the following code for {compliance_standard} compliance and suggest modifications or refactoring to meet the guidelines:\n\n{code}"
    
    messages = [
        {"role": "user", "content": prompt}
    ]

    # Create a stream to receive generated content
    stream = client.chat.completions.create(
        model="Qwen/Qwen2.5-Coder-32B-Instruct",
        messages=messages,
        temperature=0.5,
        max_tokens=1024,
        top_p=0.7,
        stream=True
    )

    # Collect the output from the stream
    compliance_suggestions = ""
    for chunk in stream:
        compliance_suggestions += chunk.choices[0].delta.content

    return compliance_suggestions

# Create Gradio interface
with gr.Blocks() as app:
    gr.Markdown("## Legal and Compliance Code Advisor")
    gr.Markdown("Analyze your code for legal compliance standards (e.g., GDPR, HIPAA) and receive modification suggestions.")

    with gr.Row():
        code_input = gr.Textbox(lines=10, label="Code Snippet", placeholder="Enter your code here")
        compliance_standard = gr.Dropdown(
            choices=["GDPR", "HIPAA", "PCI-DSS", "SOC 2", "ISO 27001"],
            label="Compliance Standard",
            value="GDPR"
        )
        output_text = gr.Textbox(label="Compliance Suggestions", placeholder="Compliance suggestions will appear here...")

    analyze_button = gr.Button("Analyze Compliance")
    analyze_button.click(fn=analyze_compliance, inputs=[code_input, compliance_standard], outputs=output_text)

# Run the Gradio app
app.launch()