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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,216 @@ | ||
| from collections import defaultdict | ||
| from dataclasses import dataclass | ||
| from datetime import datetime, timedelta | ||
| from typing import Any, Optional, List, Dict, Set, Awaitable, Callable | ||
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| from suppgram.entities import Event, MessageKind, EventKind | ||
| from suppgram.helpers import aenumerate | ||
| from suppgram.storage import Storage | ||
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| @dataclass(frozen=True) | ||
| class AgentAnalytics: | ||
| agent_id: Any | ||
| telegram_user_id: Optional[int] | ||
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| total_assigned: int | ||
| total_resolved: int | ||
| average_customer_rating: float | ||
| average_assignment_to_resolution_time_min: float | ||
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| @dataclass(frozen=True) | ||
| class ConversationAnalytics: | ||
| conversation_id: Any | ||
| customer_id: Any | ||
| last_assigned_agent_id: Optional[Any] | ||
| customer_rating: Optional[int] | ||
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| creation_time_utc: datetime | ||
| first_assignment_time_utc: Optional[datetime] | ||
| resolve_time_utc: Optional[datetime] | ||
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| last_message_kind: MessageKind | ||
| last_message_time_utc: datetime | ||
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| @dataclass(frozen=True) | ||
| class Report: | ||
| agents: List[AgentAnalytics] | ||
| conversations: List[ConversationAnalytics] | ||
| events: List[Event] | ||
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| average_start_to_first_response_time_min: float | ||
| average_start_to_resolution_time_min: float | ||
| average_customer_rating: float | ||
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| ProgressCallback = Callable[[float], Awaitable[None]] | ||
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| @dataclass | ||
| class _ConversationState: | ||
| conversation_id: Any | ||
| last_assigned_agent_id: Optional[Any] = None | ||
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| creation_time_utc: Optional[datetime] = None | ||
| first_assignment_time_utc: Optional[datetime] = None | ||
| first_response_time_utc: Optional[datetime] = None | ||
| last_assignment_time_utc: Optional[datetime] = None | ||
| resolve_time_utc: Optional[datetime] = None | ||
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| last_message_kind: Optional[MessageKind] = None | ||
| last_message_time_utc: Optional[datetime] = None | ||
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| class Reporter: | ||
| def __init__(self, storage: Storage): | ||
| self._storage = storage | ||
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| async def report(self, progress_callback: Optional[ProgressCallback]) -> Report: | ||
| if progress_callback is not None: | ||
| await progress_callback(0.0) | ||
| total_events = await self._storage.count_all_events() | ||
| total_conversations = await self._storage.count_all_conversations() | ||
| total_steps = max(1, total_events + total_conversations) | ||
| # Might also count agents, but there are probably going | ||
| # to be very few compared to conversations and events. | ||
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| conversation_states: Dict[Any, _ConversationState] = {} | ||
| agent_assignments: Dict[Any, Set[Any]] = defaultdict(set) | ||
| agent_resolutions: Dict[Any, Set[Any]] = defaultdict(set) | ||
| agent_ratings: Dict[Any, List[int]] = defaultdict(list) | ||
| agent_resolution_times: Dict[Any, List[timedelta]] = defaultdict(list) | ||
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| def _conv_state(conv_id: Any) -> _ConversationState: | ||
| conversation_states.setdefault(conv_id, _ConversationState(conversation_id=conv_id)) | ||
| return conversation_states[conv_id] | ||
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| events: List[Event] = [] | ||
| async for i, event in aenumerate(self._storage.find_all_events()): | ||
| events.append(event) | ||
| conv_state = _conv_state(event.conversation_id) | ||
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| if event.kind == EventKind.AGENT_ASSIGNED: | ||
| conv_state.last_assigned_agent_id = event.agent_id | ||
| if not conv_state.first_assignment_time_utc: | ||
| conv_state.first_assignment_time_utc = event.time_utc | ||
| conv_state.last_assignment_time_utc = event.time_utc | ||
| agent_assignments[event.agent_id].add(event.conversation_id) | ||
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| elif event.kind == EventKind.CONVERSATION_POSTPONED: | ||
| pass | ||
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| elif event.kind == EventKind.CONVERSATION_RATED: | ||
| # We'll only take into account final ratings stored in conversations. | ||
| # Rating will be attributed to the last agent assigned to a conversation. | ||
| pass | ||
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| elif event.kind == EventKind.CONVERSATION_RESOLVED: | ||
| conv_state.resolve_time_utc = event.time_utc | ||
| agent_resolutions[conv_state.last_assigned_agent_id].add(event.conversation_id) | ||
| if conv_state.last_assignment_time_utc: | ||
| agent_resolution_times[conv_state.last_assigned_agent_id].append( | ||
| event.time_utc - conv_state.last_assignment_time_utc | ||
| ) | ||
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| elif event.kind == EventKind.CONVERSATION_STARTED: | ||
| conv_state.creation_time_utc = event.time_utc | ||
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| elif event.kind == EventKind.CONVERSATION_TAG_ADDED: | ||
| pass | ||
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| elif event.kind == EventKind.CONVERSATION_TAG_REMOVED: | ||
| pass | ||
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| elif event.kind == EventKind.MESSAGE_SENT: | ||
| if ( | ||
| event.message_kind == MessageKind.FROM_AGENT | ||
| and not conv_state.first_response_time_utc | ||
| ): | ||
| conv_state.first_response_time_utc = event.time_utc | ||
| if event.message_kind in (MessageKind.FROM_AGENT, MessageKind.FROM_CUSTOMER): | ||
| conv_state.last_message_kind = event.message_kind | ||
| conv_state.last_message_time_utc = event.time_utc | ||
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| if progress_callback is not None: | ||
| await progress_callback((i + 1) / total_steps) | ||
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| conversations: List[ConversationAnalytics] = [] | ||
| async for i, conv in aenumerate(self._storage.find_all_conversations()): | ||
| conv_state = _conv_state(conv.id) | ||
| if ( | ||
| not conv_state.creation_time_utc | ||
| or not conv_state.last_message_kind | ||
| or not conv_state.last_message_time_utc | ||
| ): | ||
| # Not a single message within conversation — ignoring it. | ||
| continue | ||
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| if conv.customer_rating is not None and conv_state.last_assigned_agent_id is not None: | ||
| agent_ratings[conv_state.last_assigned_agent_id].append(conv.customer_rating) | ||
| conversations.append( | ||
| ConversationAnalytics( | ||
| conversation_id=conv.id, | ||
| customer_id=conv.customer.id, | ||
| last_assigned_agent_id=conv_state.last_assigned_agent_id, | ||
| customer_rating=conv.customer_rating, | ||
| creation_time_utc=conv_state.creation_time_utc, | ||
| first_assignment_time_utc=conv_state.first_assignment_time_utc, | ||
| resolve_time_utc=conv_state.resolve_time_utc, | ||
| last_message_kind=conv_state.last_message_kind, | ||
| last_message_time_utc=conv_state.last_message_time_utc, | ||
| ) | ||
| ) | ||
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| if progress_callback is not None: | ||
| await progress_callback((total_events + i + 1) / total_steps) | ||
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| agents = [ | ||
| AgentAnalytics( | ||
| agent_id=agent.id, | ||
| telegram_user_id=agent.telegram_user_id, | ||
| total_assigned=len(agent_assignments[agent.id]), | ||
| total_resolved=len(agent_resolutions[agent.id]), | ||
| average_customer_rating=sum(agent_ratings[agent.id]) / len(agent_ratings[agent.id]) | ||
| if agent_ratings[agent.id] | ||
| else float("nan"), | ||
| average_assignment_to_resolution_time_min=sum( | ||
| td.total_seconds() / 60.0 for td in agent_resolution_times[agent.id] | ||
| ) | ||
| / len(agent_resolution_times[agent.id]) | ||
| if agent_resolution_times[agent.id] | ||
| else float("nan"), | ||
| ) | ||
| async for agent in self._storage.find_all_agents() | ||
| ] | ||
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| first_response_times_min = [ | ||
| (conv_state.first_response_time_utc - conv_state.creation_time_utc).total_seconds() | ||
| / 60.0 | ||
| for conv_state in conversation_states.values() | ||
| if conv_state.creation_time_utc and conv_state.first_response_time_utc | ||
| ] | ||
| resolution_times_min = [ | ||
| (conv_state.resolve_time_utc - conv_state.creation_time_utc).total_seconds() / 60.0 | ||
| for conv_state in conversation_states.values() | ||
| if conv_state.creation_time_utc and conv_state.resolve_time_utc | ||
| ] | ||
| ratings = [ | ||
| conv.customer_rating for conv in conversations if conv.customer_rating is not None | ||
| ] | ||
|
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| return Report( | ||
| agents=agents, | ||
| conversations=conversations, | ||
| events=events, | ||
| average_start_to_first_response_time_min=sum(first_response_times_min) | ||
| / len(first_response_times_min) | ||
| if first_response_times_min | ||
| else float("nan"), | ||
| average_start_to_resolution_time_min=sum(resolution_times_min) | ||
| / len(resolution_times_min) | ||
| if resolution_times_min | ||
| else float("nan"), | ||
| average_customer_rating=sum(ratings) / len(ratings) if ratings else float("nan"), | ||
| ) |
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