import gradio as gr
import requests
import os

# Read backend URL from Hugging Face secret
BACKEND_URL = os.environ.get("KERNL_BACKEND_URL", "").rstrip('/')

if not BACKEND_URL:
    BACKEND_URL = None

def query_kernl(scenario, with_brain):
    """Call the Kernl /agent/handle endpoint and format the response."""
    if not BACKEND_URL:
        return "❌ Backend URL not configured. Please set the KERNL_BACKEND_URL secret."
    
    if not scenario or not scenario.strip():
        return "❗ Please enter a scenario."
    
    try:
        response = requests.post(
            f"{BACKEND_URL}/agent/handle",
            json={
                "company_id": "rivanly-inc",
                "scenario": scenario,
                "with_brain": with_brain
            },
            timeout=30
        )
        response.raise_for_status()
        data = response.json()
        
        output = []
        output.append(f"**Action:** `{data.get('action', 'N/A')}`")
        output.append(f"**Rule Applied:** `{data.get('rule_applied', 'N/A')}`")
        output.append(f"**Message:** {data.get('message_to_customer', data.get('answer', 'N/A'))}")
        if data.get('evidence'):
            output.append(f"**Evidence:** {data.get('evidence')}")
        output.append(f"**Skill Matched:** `{data.get('skill_matched', 'N/A')}`")
        output.append(f"**Confidence:** `{data.get('confidence', 'N/A')}`")
        
        return "\n\n".join(output)
    
    except requests.exceptions.ConnectionError:
        return "❌ Cannot connect to Kernl backend."
    except requests.exceptions.Timeout:
        return "❌ Request timed out."
    except Exception as e:
        return f"❌ Error: {str(e)}"

# Theme
theme = gr.themes.Soft(
    primary_hue="teal",
    secondary_hue="teal",
    neutral_hue="gray",
    font=gr.themes.GoogleFont("Inter")
)

with gr.Blocks(theme=theme, title="Kernl – Operational Memory for AI Agents") as demo:
    gr.Markdown("""
    # 🧠 Kernl
    ### Operational memory for AI agents
    
    Kernl compiles how your company actually decides things – from Slack, SOPs, and tickets – into an executable skills file.
    Any agent. Any task. Correct every time.
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            scenario_input = gr.Textbox(
                label="Enter your business scenario",
                placeholder="Example: Enterprise customer, 18 months tenure, wants $1,200 refund",
                lines=4
            )
            with_brain_toggle = gr.Checkbox(
                label="🧠 Use Company Brain (Kernl)",
                value=True,
                info="ON = Kernl uses compiled company knowledge. OFF = generic AI answer."
            )
            submit_btn = gr.Button("Ask Kernl", variant="primary", size="lg")
        
        with gr.Column(scale=2):
            output_box = gr.Markdown(label="Kernl's Response", value="*Your answer will appear here...*")
    
    gr.Markdown("""
    ---
    ### Try these example scenarios (copy & paste):
    
    - `Enterprise customer, 18 months tenure, wants $1,200 refund`
    - `Annual plan customer, day 10 of subscription, $300 refund requested`
    - `Customer reporting P0 bug on dashboard, enterprise plan`
    - `Customer showing 3 churn signals in last 30 days`
    - `Startup requesting 40% discount`
    
    ---
    **Built with AMD MI300X, vLLM, and LangGraph** | [GitHub](https://github.com/your-repo) | **Track 1: AI Agents & Agentic Workflows**
    """)
    
    submit_btn.click(
        fn=query_kernl,
        inputs=[scenario_input, with_brain_toggle],
        outputs=output_box
    )

demo.launch()