289 lines
7.0 KiB
Python
Executable File
289 lines
7.0 KiB
Python
Executable File
from pydantic import BaseModel, Field
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from typing import List, Optional, Literal, Union, Dict, Any
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from utils.bale_buttons import BUTTON_TEXT_TO_CALLBACK_LIST
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import json
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from typing import Optional, Callable, List, Any
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from pydantic import BaseModel
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class BaleStartMessageForm(BaseModel):
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id: int
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is_bot: bool = False
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first_name: str
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last_name: Optional[str] = None
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username: Optional[str] = None
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class BaleStartMessageChat(BaseModel):
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id: int
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type: str
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username: Optional[str] = None
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first_name: Optional[str] = None
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class BaleStartMessage(BaseModel):
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message_id: int
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from_user: BaleStartMessageForm = Field(..., alias="from")
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date: int
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chat: BaleStartMessageChat
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text: str
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entities: List[dict] = []
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class Config:
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populate_by_name = True
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class BaleCallbackFrom(BaseModel):
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id: int
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is_bot: bool
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first_name: str
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username: Optional[str] = None
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class BaleCallbackMessage(BaseModel):
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message_id: int
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chat: BaleStartMessageChat
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text: Optional[str]
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class BaleCallbackQuery(BaseModel):
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id: str
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from_user: BaleCallbackFrom = Field(..., alias="from")
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message: BaleCallbackMessage
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data: str
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class Config:
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populate_by_name = True
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class BaleUpdate(BaseModel):
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update_id: int
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message: Optional[BaleStartMessage] = None
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callback_query: Optional[BaleCallbackQuery] = None
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class Config:
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exclude_none = True
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class QaChatSingle(BaseModel):
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id: str
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chat_id: int
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user_query: str
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model_key: str
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model_effort: str
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retrived_passage: str
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retrived_ref_ids: str
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retrived_duration: Optional[int] = 0
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prompt_type: str = "question"
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llm_duration: int
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full_duration: Optional[int] = 0
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time_create: Optional[int] = 0
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used_ref_ids: Optional[str] = ""
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status_text: Optional[str] = ""
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status: Optional[int] = 0
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prompt_answer: str
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other_info: dict | None
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class QaChatBlock(BaseModel):
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id: str
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title: str
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user_id: str
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is_premium: bool
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chat: QaChatSingle
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total_token: int
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is_end: bool
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class QaChat(BaseModel):
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time_stamp: int = 0
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id: str
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chat_id: int
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title: Optional[str] = ""
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user_id: str
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user_query: str
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query_type: str = (
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"question" # llm -> greeting, other, legal_question | rag -> question
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)
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full_duration: Optional[float] = 0
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other_info: Optional[dict] = ""
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ss_ref_ids: Optional[List[str]] = ""
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ss_model_key: Optional[str] = ""
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ss_duration: Optional[float] = 0
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ss_answer: Optional[str] = ""
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llm_ref_ids: Optional[List[str]] = []
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llm_model_key: Optional[str] = ""
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llm_duration: Optional[float] = 0
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llm_answer: Optional[str] = ""
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status_text: Optional[str] = ""
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status: Optional[int] = 0
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class ConflictDetection(BaseModel):
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explanation_of_conflict: str
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has_confict: bool = False
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class ConflictTypeDetection(BaseModel):
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conflict_type: Literal["Doctrinal_conflict", "Practical_conflict"]
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explanation_of_type: str
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class RelationIdentification(BaseModel):
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reasoning: str
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relation_type: Literal[
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"مطلق مقدم، مقید موخر",
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"مقید مقدم، مطلق موخر",
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"تکرار حکم",
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"تعارض مستقر",
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"بدون تعارض",
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]
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class Evaluation(BaseModel):
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is_subject_unity_assessment_correct: bool
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is_conflict_detection_correct: bool
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is_conflict_type_detection_correct: bool
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is_relation_type_detection_correct: bool
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valid_relation_type: str
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comments: str
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class SingleRuleRelation(BaseModel):
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rule_id: str
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rule_content: str
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rule_type: str
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section_id: str
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section_content: str
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qanon_etebar: Optional[str] = None
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qanon_id: Optional[str] = None
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qanon_title: Optional[str] = None
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state_etebar: Optional[str] = None
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date: Optional[str] = None
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def __str__(self):
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dict_ = {
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# "rule_id": self.rule_id,
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"متن حکم": self.rule_content,
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"نوع حکم": self.rule_type,
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"متن ماده قانونی": self.section_content,
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}
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# Return the dictionary as a formatted JSON string
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return json.dumps(dict_, ensure_ascii=False, indent=2)
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class SubjectUnity(BaseModel):
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has_subject_unity: Literal["yes", "no", "yes_under_assumptions"]
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required_assumptions: Optional[str] = None
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reasoning: Optional[str]
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class RuleRelation(BaseModel):
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# ----- input rule
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in_rule: SingleRuleRelation
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# ----- semantic-search close rule
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db_rule: SingleRuleRelation
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# ----- subject-unity data
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subject_unity: SubjectUnity | None = None
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# ----- conflict-detection data
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conflict_detection: ConflictDetection | None = None
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# ----- conflict-type-detection data
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conflict_type_detection: ConflictTypeDetection | None = None
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# ----- relation-identification data
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relation_identification: RelationIdentification | None = None
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class StateDetail(BaseModel):
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state: str
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message: str
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button_text: str
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end_buttons: List = []
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inline_buttons: Optional[List[List[dict]]] = None
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handler : str = None
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allow_empty_input: bool = True
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class BaleUser(BaseModel):
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uc_id: str
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chat_id: int
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user_id: str
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update: BaleUpdate
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username: str
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is_bot: bool = False
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first_name: str = ""
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last_name: str = ""
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rule_relation: RuleRelation | None = None
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subject_unities:Dict = {}
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# ---- defaults
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effort: Literal["medium", "low"] = "low"
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limit: int = 10
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permission: str = "normal"
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# ---- runtime
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is_processing_lock : bool = False
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is_call_back_query : bool = False
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state_detail : StateDetail = None
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last_message_id : int = 0
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input_query: str = "" # ورودی کاربر
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call_back_query: str = "" # ورودی کاربر
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_query_type: str = "" # ورودی کاربر
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sub_state: str = "" # برای روندی ها
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all_qq: bool = False
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# ---- memory
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last_query: Dict[str, str] = {} # آخرین سوال کاربر
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last_result: Dict[str, Any] = {} # آخرین نتایج
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last_runtime: Dict[str, Any] = (
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{}
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) # مثلا {"GP": {"effort": "medium", "limit": 20}} برای بقیه چیزها
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stack: Dict[str, List] = {} # پشته برای داده های مرحله ای
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class KeyboardItem(BaseModel):
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text: str
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class InlineKeyboardItem(BaseModel):
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text: str
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callback_data: str
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class ReplyMarkup(BaseModel):
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keyboard: List[List[Union[KeyboardItem, Dict]]] = Field(
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default_factory=lambda: BUTTON_TEXT_TO_CALLBACK_LIST
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)
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resize_keyboard: bool = True
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one_time_keyboard: bool = False
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inline_keyboard: List[List[Union[InlineKeyboardItem, Dict]]] | None = (
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None # دکمه ها ی داخال صفحه
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)
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class BalePayload(BaseModel):
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reply_markup: ReplyMarkup = ReplyMarkup()
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chat_id: int
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message_id: int = None
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text: str
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class Step(BaseModel):
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name: str
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level : int
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data_input : str
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data_output : str
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bot_output : List
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