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thanhnv
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"""Schedule Task module — AI Create Task.
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Turns a natural-language description ("Mỗi thứ 2 lúc 9h, dùng Co4E đọc dữ liệu
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CAE, tạo báo cáo, rồi chuyển cho Cowork soạn email...") into a list of task
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dicts, via the active provider. The result is a PREVIEW — the UI shows it and
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only creates real tasks after the user confirms (spec §9.2).
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"""
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from __future__ import annotations
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import json
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import re
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from typing import Any, Dict, List, Optional
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from .tasks import (
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INPUT_MODES, PRIORITIES, REPEAT_TYPES, RUN_NEXT_MODES, TASK_TYPES, new_task,
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)
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_SYSTEM = """You convert a user's natural-language request into scheduled tasks
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for a desktop automation app. Reply with ONE JSON object only (no prose, no
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markdown fences) shaped exactly like:
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{"tasks": [{
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"title": str,
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"description": str,
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"task_type": "cowork"|"co4e_code"|"script"|"manual",
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"priority": "low"|"medium"|"high"|"critical",
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"schedule": {"enabled": bool, "run_at": "YYYY-MM-DD HH:MM" or null,
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"repeat_type": "none"|"daily"|"weekly"},
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"input": {"mode": "empty"|"manual"|"previous_task_output", "manual_text": str or null},
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"dependency": {"previous_task_id": "TASK_1" or null,
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"run_next_mode": "none"|"run_after_success"|"run_always"|"run_after_manual_confirm",
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"pass_output_to_next": bool}
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}]}
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Rules: use "co4e_code" for coding/data/file-processing work, "cowork" for
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documents/emails/reports/chat-style work. Reference earlier tasks in the same
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reply as "TASK_1", "TASK_2" (1-based order). If the user gives a schedule,
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fill run_at with the NEXT occurrence from today. Keep 1-4 tasks."""
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def _extract_json(text: str) -> Optional[dict]:
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"""The first parseable {...} block in the model's reply."""
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text = (text or "").strip()
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fenced = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL)
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if fenced:
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text = fenced.group(1)
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start = text.find("{")
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if start == -1:
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return None
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for end in range(len(text), start, -1):
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try:
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return json.loads(text[start:end])
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except json.JSONDecodeError:
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continue
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return None
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def _clamp(value, allowed, default):
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return value if value in allowed else default
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def normalize_planned_tasks(payload: dict) -> List[Dict[str, Any]]:
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"""Turn the model's JSON into real task dicts (all defaults filled) and
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resolve TASK_n references into actual ids + back-links, so the chain works
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both directions (prev's next_task_id AND next's previous_task_id)."""
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raw = (payload or {}).get("tasks") or []
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if not isinstance(raw, list) or not raw:
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return []
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tasks: List[Dict[str, Any]] = []
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for item in raw[:8]:
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if not isinstance(item, dict):
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continue
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t = new_task(str(item.get("title") or "Untitled task"))
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t["description"] = str(item.get("description") or "")
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t["task_type"] = _clamp(item.get("task_type"), TASK_TYPES, "cowork")
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t["priority"] = _clamp(item.get("priority"), PRIORITIES, "medium")
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t["is_ai_generated"] = True
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sched = item.get("schedule") or {}
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t["schedule"]["enabled"] = bool(sched.get("enabled"))
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t["schedule"]["run_at"] = sched.get("run_at") or None
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t["schedule"]["repeat_type"] = _clamp(sched.get("repeat_type"), REPEAT_TYPES, "none")
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if t["schedule"]["enabled"] and t["schedule"]["run_at"]:
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t["status"] = "scheduled"
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inp = item.get("input") or {}
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t["input"]["mode"] = _clamp(inp.get("mode"), INPUT_MODES, "empty")
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t["input"]["manual_text"] = inp.get("manual_text") or None
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dep = item.get("dependency") or {}
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t["dependency"]["run_next_mode"] = _clamp(dep.get("run_next_mode"),
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RUN_NEXT_MODES, "none")
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t["dependency"]["pass_output_to_next"] = bool(dep.get("pass_output_to_next"))
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t["_prev_ref"] = dep.get("previous_task_id") # TASK_n, resolved below
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tasks.append(t)
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# Resolve TASK_n → actual ids; wire both directions of the chain.
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for t in tasks:
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ref = t.pop("_prev_ref", None)
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if not ref:
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continue
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m = re.match(r"TASK_(\d+)$", str(ref).strip())
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idx = int(m.group(1)) - 1 if m else -1
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if 0 <= idx < len(tasks) and tasks[idx] is not t:
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prev = tasks[idx]
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t["dependency"]["previous_task_id"] = prev["task_id"]
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t["input"]["previous_task_id"] = prev["task_id"]
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prev["dependency"]["next_task_id"] = t["task_id"]
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if t["dependency"]["run_next_mode"] != "none":
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prev["dependency"]["run_next_mode"] = t["dependency"]["run_next_mode"]
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if t["dependency"]["pass_output_to_next"] or t["input"]["mode"] == "previous_task_output":
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prev["dependency"]["pass_output_to_next"] = True
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t["input"]["mode"] = "previous_task_output"
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return tasks
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def generate_task_description(provider, title: str, cancel=None) -> str:
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"""Best-effort ✨ helper: draft a task's description from its title.
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Returns '' on any error so the editor never breaks."""
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title = (title or "").strip()
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if not title:
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return ""
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messages = [
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{"role": "system", "content":
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"You write the DESCRIPTION of a scheduled automation task. Given its title, "
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"write 2-4 concise sentences describing exactly what the task should do "
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"(inputs, action, expected output). Reply with ONLY the description text, "
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"in the same language as the title."},
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{"role": "user", "content": title},
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]
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try:
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a = provider.chat(messages, tools=None, on_text=None, cancel=cancel)
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except Exception: # noqa: BLE001
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return ""
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return (a.get("content") or "").strip()
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def generate_task_input_text(provider, title: str, description: str, cancel=None) -> str:
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"""Best-effort ✨ helper: draft the task's Prompt/Input text from its
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title + description. Returns '' on any error so the editor never breaks."""
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title = (title or "").strip()
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if not title:
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return ""
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messages = [
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{"role": "system", "content":
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"You write the INPUT/PROMPT text for a scheduled automation task — extra "
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"context, data, or instructions the agent will need beyond the title and "
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"description. Given the task's title and description, write 2-4 concise "
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"sentences. Reply with ONLY the prompt text, in the same language as the title."},
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{"role": "user", "content": f"Title: {title}\nDescription: {description or '(none)'}"},
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]
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try:
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a = provider.chat(messages, tools=None, on_text=None, cancel=cancel)
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except Exception: # noqa: BLE001
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return ""
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return (a.get("content") or "").strip()
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def generate_prompt_from_description(provider, description: str, cancel=None) -> str:
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"""Best-effort ✨ helper: expand the task's DESCRIPTION into the ready-to-run
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Prompt/Input text the agent will act on. The title is intentionally NOT used
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— in Schedule Task the title is just the card's label, so the content comes
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only from the description. Returns '' on empty input or any error so the
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editor never breaks."""
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description = (description or "").strip()
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if not description:
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return ""
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messages = [
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{"role": "system", "content":
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"You turn a task DESCRIPTION into the ready-to-run PROMPT an AI agent will "
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"execute for a scheduled automation task. Rewrite the description as clear, "
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"actionable instructions (what to do, with which inputs, and the expected "
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"output). Do NOT invent a topic from a title — use ONLY the description. "
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"Reply with ONLY the prompt text, in the same language as the description."},
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{"role": "user", "content": description},
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]
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try:
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a = provider.chat(messages, tools=None, on_text=None, cancel=cancel)
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except Exception: # noqa: BLE001
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return ""
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return (a.get("content") or "").strip()
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def generate_agent_prompt(provider, name: str = "", role: str = "", hint: str = "", cancel=None) -> str:
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"""Best-effort ✨ helper: draft the INSTRUCTIONS/prompt for a Co4E agent from
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its name + role (+ optional hint) — used when the user hasn't attached a
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skill and wants the agent's behaviour written for them. Returns '' on error."""
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name = (name or "").strip()
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role = (role or "").strip()
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if not name and not role and not hint:
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return ""
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who = f"{name} ({role})" if role else name or role
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messages = [
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{"role": "system", "content":
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"You write the INSTRUCTIONS (system prompt) for a specialized AI agent that runs as one "
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"step in a workflow. Given the agent's name/role (and any hint), write clear, imperative "
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"guidance: what this agent is responsible for, how it should work, and what its output "
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"should be. A few concise sentences or short bullets. Reply with ONLY the instructions "
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"text — no title, no preamble."},
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{"role": "user", "content": f"Agent: {who}" + (f"\nHint: {hint}" if hint else "")},
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]
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try:
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a = provider.chat(messages, tools=None, on_text=None, cancel=cancel)
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except Exception: # noqa: BLE001 — generation must never break the dialog
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return ""
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return (a.get("content") or "").strip()
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def plan_tasks(provider, description: str, cancel=None) -> List[Dict[str, Any]]:
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"""description → normalized task dicts (NOT yet saved). Raises RuntimeError
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when the model's reply has no parseable task JSON."""
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from datetime import datetime
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messages = [
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{"role": "system", "content": _SYSTEM},
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{"role": "user", "content": f"Today is {datetime.now().strftime('%Y-%m-%d %H:%M %A')}.\n"
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f"Request: {description}"},
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]
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reply = provider.chat(messages, cancel=cancel)
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payload = _extract_json(reply.get("content", ""))
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tasks = normalize_planned_tasks(payload) if payload else []
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if not tasks:
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raise RuntimeError("AI reply did not contain a valid task list — try rephrasing.")
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return tasks
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