from __future__ import annotations

from backend.ai.client import llm_client, render_prompt
from backend.config import get_settings
from backend.ai.prompts import ANALYZE_PROMPT, CUSTOM_TASK_PROMPT
from backend.models.schemas import TaskCategory


def analyze_business_description(description: str) -> dict:
    settings = get_settings()
    if settings.analyze_with_ai and llm_client.is_ready():
        try:
            return llm_client.generate_json(render_prompt(ANALYZE_PROMPT, description=description))
        except Exception:
            pass

    lower = description.lower()
    fully_automatable = []
    ai_assisted = []
    manual = []
    if "order" in lower:
        fully_automatable.append(
            {
                "id": "task_order_processing",
                "name": "Order processing",
                "description": "Read order emails, check stock, log rows, and reply.",
                "time_estimate": "10-15 hours/week",
                "pain_level": "high",
            }
        )
    if "friday" in lower or "weekly" in lower:
        fully_automatable.append(
            {
                "id": "task_weekly_summary",
                "name": "Weekly order summary report",
                "description": "Compile the week's orders and send a summary.",
                "time_estimate": "1-2 hours/week",
                "pain_level": "medium",
            }
        )
    ai_assisted.append(
        {
            "id": "task_availability_inquiries",
            "name": "Availability inquiries",
            "description": "Draft replies using current stock and pricing.",
            "time_estimate": "2-3 hours/week",
            "pain_level": "medium",
        }
    )
    ai_assisted.append(
        {
            "id": "task_low_inventory_alerts",
            "name": "Low inventory alerts",
            "description": "Watch for thin inventory and propose next steps.",
            "time_estimate": "30 minutes/week",
            "pain_level": "medium",
        }
    )
    manual.append(
        {
            "id": "task_special_requests",
            "name": "Custom and relationship-sensitive requests",
            "description": "Gift boxes, delivery arrangements, and VIP relationship notes.",
            "reason_manual": "These need human judgment, pricing discretion, and a personal touch.",
        }
    )
    return {
        "business_type": "Email-driven small business",
        "summary": "FlowPilot found repetitive inbox work that maps well to Gmail, Sheets, and scheduled automations.",
        "tasks": {
            "fully_automatable": fully_automatable,
            "ai_assisted": ai_assisted,
            "manual": manual,
        },
    }


def analyze_custom_task(business_description: str, existing_workflows: list[dict], custom_task: str) -> dict:
    if llm_client.is_ready():
        return llm_client.generate_json(
            render_prompt(
                CUSTOM_TASK_PROMPT,
                business_description=business_description,
                existing_workflows=existing_workflows,
                primitives_list=[
                    "email_received",
                    "schedule",
                    "file_uploaded",
                    "manual_trigger",
                    "ai_extract",
                    "ai_classify",
                    "ai_compose",
                    "sheets_read",
                    "sheets_write",
                    "sheets_update",
                    "send_email",
                    "notify_owner",
                    "condition",
                    "loop",
                    "wait_for_input",
                ],
                custom_task=custom_task,
            )
        )

    lower = custom_task.lower()
    feasible = any(word in lower for word in ("flag", "priority", "sheet", "email"))
    category = TaskCategory.FULLY_AUTOMATABLE if feasible else TaskCategory.MANUAL
    return {
        "feasible": feasible,
        "category": category,
        "task_name": "Restaurant priority handling",
        "task_description": custom_task,
        "missing_capabilities": [] if feasible else ["A matching primitive for this request is missing."],
        "reason_if_manual": None if feasible else "This task needs a capability outside the current primitives.",
        "time_estimate": "1 hour/week",
        "pain_level": "medium",
    }