import gradio as gr
import sys
from typing import List, Dict, Any

# --- Consent/Event Audit Panel ---
def consent_audit_panel(consent_events: List[Dict[str, Any]]):
    """
    UI Panel to show all consent events, opt-outs, and compliance flags per contact and batch.
    Accepts a list of consent event dicts with structure as returned from backend JSON.
    """
    audit_md = "# Consent/Event Audit\nPer-contact and batch consent history."
    for event in consent_events:
        audit_md += f"\n- Contact: {event.get('contact_id','?')} | Event: {event.get('event_type','?')} | Opt-out: {event.get('opt_out',False)} | Compliance: {event.get('compliance_flag','?')})"
    return gr.Markdown(audit_md)

# --- Dynamic Branching Composer for Cadence Editing ---
def branching_composer(branches: List[Dict[str, Any]]):
    """
    Visual sequence builder for editing cadences.
    Structure assumed to be a list of dict branches with JSON mirroring backend.
    """
    builder_md = "# Dynamic Cadence Branching\n" + "\n## Sequence Tree:"
    for b in branches:
        builder_md += f"\n- Step {b.get('step','?')}: {b.get('description','?')} (Next: {b.get('next','?')})"
    return gr.Markdown(builder_md)

# --- Live Diagnostics Panel for Cadence Errors/Health ---
def diagnostics_panel(events: List[Dict[str, Any]]):
    """
    Incident/event log UI widget to show errors and health of cadence system.
    Format is a simple list of event dicts from backend state.
    """
    diag_md = "# Live Diagnostics: Cadence Health\n"
    for e in events:
        diag_md += f"\n- [{e.get('timestamp','?')}] {e.get('type','?')}: {e.get('message','?')}{' [ERROR]' if e.get('level','').lower()=='error' else ''}"
    return gr.Markdown(diag_md)

# --- Exportable Audit Trail Widget ---
def audit_trail_widget(audit_history: List[Dict[str, Any]]):
    """
    Compliance and debug history view for export.
    Accepts audit events from backend JSON/state.
    """
    audit_log = 'Contact,Event,Opt-out,Compliance\n'
    for item in audit_history:
        audit_log += f"{item.get('contact_id','?')},{item.get('event_type','?')},{item.get('opt_out',False)},{item.get('compliance_flag','?')}\n"
    return gr.Textbox(label="Audit Trail CSV", value=audit_log, lines=10)

# Example usage in Gradio Blocks
with gr.Blocks(title="StrategyStack UI") as demo:
    gr.Markdown("# Outreach Cadence & Compliance Dashboard")
consent_events_sample = [
        {
            "contact_id": "LEAD_001",
            "event_type": "consent_given",
            "timestamp": "2025-11-06T06:20:00Z",
            "opt_out": False,
            "compliance_flag": "ok",
            "region": "EU",
            "method": "checkbox_onboarding_form",
            "actor": "user",
            "batch_id": "BATCH_Q4_2025_HEALTHCARE"
        },
        {
            "contact_id": "LEAD_002",
            "event_type": "opt_out",
            "timestamp": "2025-11-06T06:21:00Z",
            "opt_out": True,
            "compliance_flag": "flagged",
            "region": "US",
            "method": "footer_unsubscribe",
            "actor": "user",
            "batch_id": "BATCH_Q4_2025_HEALTHCARE"
        },
        {
            "contact_id": "LEAD_003",
            "event_type": "consent_given",
            "timestamp": "2025-11-06T07:15:00Z",
            "opt_out": False,
            "compliance_flag": "ok",
            "region": "US",
            "method": "api_integration",
            "actor": "system",
            "batch_id": "BATCH_Q4_2025_SAAS"
        }
    ]
    
branches_sample = [
        {
            "step": 1,
            "description": "Initial Welcome Email - Personalized Industry Context",
            "channel": "email",
            "timing": "immediate",
            "next": 2,
            "condition": "not_opted_out"
        },
        {
            "step": 2,
            "description": "LinkedIn Connection Request with Custom Note",
            "channel": "linkedin",
            "timing": "2_days_after_step_1",
            "next": 3,
            "condition": "no_reply_from_step_1"
        },
        {
            "step": 3,
            "description": "Follow-up Call - Pain Point Discovery",
            "channel": "phone",
            "timing": "3_days_after_step_2",
            "next": 4,
            "condition": "engagement_score_below_0.7"
        },
        {
            "step": 4,
            "description": "Case Study Email - Relevant to Industry",
            "channel": "email",
            "timing": "1_day_after_step_3",
            "next": "END",
            "condition": "not_connected"
        }
    ]
    
diagnostics_sample = [
        {
            "timestamp": "2025-11-06T06:23:00Z",
            "type": "info",
            "message": "Cadence started for BATCH_Q4_2025_HEALTHCARE with 150 leads",
            "level": "info",
            "agent": "cadence_orchestrator"
        },
        {
            "timestamp": "2025-11-06T06:29:00Z",
            "type": "error",
            "message": "Compliance block: Message suppressed for LEAD_002 - opted out via footer_unsubscribe",
            "level": "error",
            "agent": "compliance_agent"
        },
        {
            "timestamp": "2025-11-06T06:33:00Z",
            "type": "warning",
            "message": "Rate limit reached for LinkedIn API; implementing exponential backoff for 15 minutes",
            "level": "warning",
            "agent": "outreach_agent"
        },
        {
            "timestamp": "2025-11-06T06:45:00Z",
            "type": "success",
            "message": "Enrichment completed for 45 leads with intent_score > 0.8",
            "level": "info",
            "agent": "enrichment_agent"
        },
        {
            "timestamp": "2025-11-06T07:00:00Z",
            "type": "warning",
            "message": "5 leads flagged for human validation due to incomplete firmographic data",
            "level": "warning",
            "agent": "validation_agent"
        }
    ]
    
audit_history_sample = consent_events_sample