XF/GIA
1
0
Fork 0
Code Issues 2 Pull Requests Actions Packages Projects Releases Wiki Activity

Implement more information displays

Browse Source
This commit is contained in:
Mark Veidemanis
2026-02-15 19:27:16 +00:00
parent 4cf75b9923
commit 1ebd565f44
13 changed files with 1421 additions and 271 deletions
Download Patch File Download Diff File Expand all files Collapse all files

610
core/views/workspace.py
View File

@@ -629,6 +629,41 @@ def _compose_page_url_for_person(user, person):
return f"{reverse('compose_page')}?{query}"
def _message_rows_for_person(user, person, limit):
sessions = ChatSession.objects.filter(user=user, identifier__person=person)
identifiers = set(
PersonIdentifier.objects.filter(user=user, person=person).values_list(
"identifier", flat=True
)
)
messages = (
Message.objects.filter(user=user, session__in=sessions)
.select_related("session", "session__identifier")
.order_by("-ts")[:limit]
)
rows = []
for message in reversed(list(messages)):
rows.append(
{
"message": message,
"direction": _infer_direction(message, identifiers),
"ts_label": _format_unix_ms(message.ts),
}
)
return rows
def _recent_messages_for_person(user, person, limit):
sessions = ChatSession.objects.filter(user=user, identifier__person=person)
messages = (
Message.objects.filter(user=user, session__in=sessions)
.select_related("session", "session__identifier")
.order_by("-ts")[:limit]
)
return list(reversed(list(messages)))
def _is_truthy(value):
return str(value or "").strip().lower() in {"1", "true", "on", "yes"}
@@ -807,6 +842,274 @@ def _all_graph_payload(conversation):
return graphs
def _commitment_directionality_payload(conversation):
latest_snapshot = conversation.metric_snapshots.first()
inbound = conversation.commitment_inbound_score
outbound = conversation.commitment_outbound_score
confidence = conversation.commitment_confidence or 0.0
if inbound is None or outbound is None:
return {
"direction_key": "calibrating",
"direction_label": "Calibrating",
"magnitude": None,
"delta": None,
"confidence": confidence,
"conclusion": (
"Directionality cannot be inferred yet. Collect more exchanges to "
"stabilize directional signal."
),
"factors": [],
"graph_refs": [],
}
delta = round(float(outbound) - float(inbound), 2)
magnitude = round(abs(delta), 2)
if magnitude < 4:
direction_key = "balanced"
direction_label = "Balanced"
conclusion = (
"Commitment appears symmetric. Keep current cadence and focus on "
"maintaining clarity."
)
elif delta > 0:
direction_key = "outbound"
direction_label = "Outbound-Leaning"
conclusion = (
"You are carrying relatively more directional effort right now. "
"Consider reducing over-functioning and asking for explicit reciprocity."
)
else:
direction_key = "inbound"
direction_label = "Inbound-Leaning"
conclusion = (
"The other party is carrying relatively more directional effort right now. "
"Acknowledge this and match consistency to reduce asymmetry."
)
graph_refs = [
{"slug": "commitment_inbound", "title": "Commit In"},
{"slug": "commitment_outbound", "title": "Commit Out"},
{"slug": "inbound_response_score", "title": "Inbound Response Score"},
{"slug": "outbound_response_score", "title": "Outbound Response Score"},
{"slug": "balance_inbound_score", "title": "Inbound Balance Score"},
{"slug": "balance_outbound_score", "title": "Outbound Balance Score"},
{"slug": "commitment_confidence", "title": "Commit Confidence"},
]
factor_lookup = {
"inbound_response_score": (
latest_snapshot.inbound_response_score if latest_snapshot else None
),
"outbound_response_score": (
latest_snapshot.outbound_response_score if latest_snapshot else None
),
"balance_inbound_score": (
latest_snapshot.balance_inbound_score if latest_snapshot else None
),
"balance_outbound_score": (
latest_snapshot.balance_outbound_score if latest_snapshot else None
),
"commitment_confidence": confidence,
}
factors = [
{
"title": "Inbound Response",
"icon": "fa-solid fa-inbox",
"weight": "60% of Commit In",
"value": factor_lookup["inbound_response_score"],
"slug": "inbound_response_score",
},
{
"title": "Inbound Balance",
"icon": "fa-solid fa-scale-balanced",
"weight": "40% of Commit In",
"value": factor_lookup["balance_inbound_score"],
"slug": "balance_inbound_score",
},
{
"title": "Outbound Response",
"icon": "fa-solid fa-paper-plane",
"weight": "60% of Commit Out",
"value": factor_lookup["outbound_response_score"],
"slug": "outbound_response_score",
},
{
"title": "Outbound Balance",
"icon": "fa-solid fa-arrows-left-right",
"weight": "40% of Commit Out",
"value": factor_lookup["balance_outbound_score"],
"slug": "balance_outbound_score",
},
{
"title": "Confidence",
"icon": "fa-solid fa-shield-check",
"weight": "Applies To Direction",
"value": confidence,
"slug": "commitment_confidence",
},
]
return {
"direction_key": direction_key,
"direction_label": direction_label,
"magnitude": magnitude,
"delta": delta,
"confidence": confidence,
"conclusion": conclusion,
"commit_in": inbound,
"commit_out": outbound,
"factors": factors,
"graph_refs": graph_refs,
}
def _metric_pattern_context(conversation):
latest_snapshot = conversation.metric_snapshots.first()
directionality = _commitment_directionality_payload(conversation)
confidence = conversation.stability_confidence or 0.0
risk_signals = []
state_key = str(conversation.stability_state or "").lower()
if state_key == WorkspaceConversation.StabilityState.FRAGILE:
risk_signals.append(
{
"key": "stability_fragile",
"label": "Fragile Stability",
"severity": "high",
"explanation": (
"Stability is in fragile range. Bias corrections toward "
"de-escalation and explicit repair loops."
),
}
)
elif state_key == WorkspaceConversation.StabilityState.WATCH:
risk_signals.append(
{
"key": "stability_watch",
"label": "Watch Stability",
"severity": "medium",
"explanation": (
"Stability is watch-range. Reinforce concise requests and "
"misinterpretation checks before escalation."
),
}
)
if confidence < 0.25:
risk_signals.append(
{
"key": "low_confidence",
"label": "Low Confidence Window",
"severity": "low",
"explanation": (
"Confidence is low. Prefer reversible, low-risk corrections "
"that can be validated quickly."
),
}
)
magnitude = directionality.get("magnitude")
if magnitude is not None:
severity = "high" if magnitude >= 15 else "medium" if magnitude >= 8 else None
if severity:
risk_signals.append(
{
"key": "commitment_asymmetry",
"label": "Commitment Asymmetry",
"severity": severity,
"explanation": (
"Directional commitment is asymmetric. Add corrections "
"that restore reciprocity and explicit confirmation."
),
}
)
if latest_snapshot:
if (
latest_snapshot.volatility_score is not None
and latest_snapshot.volatility_score >= 70
):
risk_signals.append(
{
"key": "volatility_spike",
"label": "Volatility Spike",
"severity": "medium",
"explanation": (
"Volatility is elevated. Use short, bounded wording to "
"reduce sudden interaction swings."
),
}
)
if (
latest_snapshot.reciprocity_score is not None
and latest_snapshot.reciprocity_score <= 35
):
risk_signals.append(
{
"key": "reciprocity_drop",
"label": "Reciprocity Drop",
"severity": "medium",
"explanation": (
"Reciprocity is low. Add corrections that request and "
"acknowledge balanced turn-taking."
),
}
)
if (
latest_snapshot.response_score is not None
and latest_snapshot.response_score <= 35
):
risk_signals.append(
{
"key": "response_drag",
"label": "Response Drag",
"severity": "medium",
"explanation": (
"Response pace is slow. Prefer corrections that set timing "
"expectations and explicit follow-up windows."
),
}
)
state_label = (
conversation.get_stability_state_display()
if hasattr(conversation, "get_stability_state_display")
else str(conversation.stability_state or "")
)
return {
"stability": {
"state": state_label,
"score": conversation.stability_score,
"confidence": confidence,
"sample_messages": conversation.stability_sample_messages,
"sample_days": conversation.stability_sample_days,
"computed_at": conversation.stability_last_computed_at,
},
"commitment": {
"inbound": conversation.commitment_inbound_score,
"outbound": conversation.commitment_outbound_score,
"confidence": conversation.commitment_confidence,
"computed_at": conversation.commitment_last_computed_at,
"directionality": directionality,
},
"components": (
{
"reciprocity": latest_snapshot.reciprocity_score,
"continuity": latest_snapshot.continuity_score,
"response": latest_snapshot.response_score,
"volatility": latest_snapshot.volatility_score,
"inbound_response": latest_snapshot.inbound_response_score,
"outbound_response": latest_snapshot.outbound_response_score,
"balance_inbound": latest_snapshot.balance_inbound_score,
"balance_outbound": latest_snapshot.balance_outbound_score,
"source_event_ts": latest_snapshot.source_event_ts,
}
if latest_snapshot
else {}
),
"risk_signals": risk_signals[:8],
}
def _store_metric_snapshot(conversation, payload):
compare_keys = [
"source_event_ts",
@@ -1476,6 +1779,7 @@ def _build_mitigation_artifacts(
inspiration,
fundamentals,
output_profile,
metric_context=None,
):
fallback = _default_artifacts_from_patterns(source_text, person, output_profile)
@@ -1489,11 +1793,15 @@ def _build_mitigation_artifacts(
"role": "system",
"content": (
"You design practical relationship mitigation protocols. "
"Return strict JSON only with keys: title, objective, fundamental_items, rules, games. "
"Return strict JSON only with keys: title, objective, "
"fundamental_items, rules, games, corrections. "
"Each rule item must have title and content. "
"Each game item must have title and instructions. "
"Each correction item must have title and clarification. "
"If mode is auto, choose strongest artifacts. If mode is guided, strongly follow inspiration. "
"Output profile controls emphasis: framework (balanced), rules (rules-first), games (games-first)."
"Use provided metrics as risk context to tighten corrections. "
"Output profile controls emphasis: framework (balanced), "
"rules (rules-first), games (games-first)."
),
},
{
@@ -1504,6 +1812,8 @@ def _build_mitigation_artifacts(
f"Output profile: {output_profile}\n"
f"User inspiration: {inspiration or 'None'}\n"
f"Fundamental items (pre-agreed): {json.dumps(fundamentals)}\n\n"
"Metric context:\n"
f"{json.dumps(metric_context or {}, ensure_ascii=False, default=str)}\n\n"
f"Pattern analysis:\n{source_text}"
),
},
@@ -1551,6 +1861,28 @@ def _build_mitigation_artifacts(
if title_i and instructions_i:
games.append({"title": title_i, "instructions": instructions_i})
raw_corrections = parsed.get("corrections")
corrections = []
if isinstance(raw_corrections, list):
for item in raw_corrections:
if not isinstance(item, dict):
continue
title_i = _normalize_correction_title(
item.get("title") or "", fallback="Correction"
)
clarification_i = str(
item.get("clarification") or item.get("content") or ""
).strip()
source_phrase_i = str(item.get("source_phrase") or "").strip()
if title_i and clarification_i:
corrections.append(
{
"title": title_i[:255],
"clarification": clarification_i[:2000],
"source_phrase": source_phrase_i[:1000],
}
)
if not rules:
rules = fallback["rules"]
if not games:
@@ -1563,7 +1895,7 @@ def _build_mitigation_artifacts(
"fundamental_items": merged_fundamentals,
"rules": rules,
"games": games,
"corrections": [],
"corrections": _normalize_violation_items(corrections),
}
@@ -2342,6 +2674,74 @@ def _get_or_create_auto_settings(user, conversation):
return settings_obj
def _metric_guided_artifact_candidates(plan, metric_context):
signals = list((metric_context or {}).get("risk_signals") or [])
if not signals:
return []
artifacts = []
for rule in plan.rules.filter(enabled=True).order_by("created_at")[:10]:
artifacts.append(
{
"kind": "rule",
"title": str(rule.title or "").strip(),
"body": str(rule.content or "").strip(),
}
)
for game in plan.games.filter(enabled=True).order_by("created_at")[:10]:
artifacts.append(
{
"kind": "game",
"title": str(game.title or "").strip(),
"body": str(game.instructions or "").strip(),
}
)
if not artifacts:
for item in (plan.fundamental_items or [])[:10]:
text = str(item or "").strip()
if not text:
continue
artifacts.append(
{
"kind": "fundamental",
"title": text[:100],
"body": text,
}
)
if not artifacts:
return []
out = []
for idx, signal in enumerate(signals[:8]):
artifact = artifacts[idx % len(artifacts)]
kind_label = {
"rule": "Rule",
"game": "Game",
"fundamental": "Fundamental",
}.get(artifact["kind"], "Artifact")
title = _normalize_correction_title(
f"{signal.get('label') or 'Metric Signal'} Safeguard"
)
clarification = (
f"{str(signal.get('explanation') or '').strip()} "
f"Apply {kind_label.lower()} '{artifact['title']}' in the next exchange: "
f"{artifact['body']}"
).strip()
source_phrase = (
f"Metric signal: {signal.get('label') or 'Metric Signal'}; "
f"Artifact: {kind_label} '{artifact['title']}'"
)
out.append(
{
"title": title,
"source_phrase": source_phrase[:1000],
"clarification": clarification[:2000],
"severity": str(signal.get("severity") or "medium"),
}
)
return _normalize_violation_items(out)
def _detect_violation_candidates(plan, recent_rows):
candidates = []
for row in recent_rows:
@@ -2442,10 +2842,10 @@ def _existing_correction_signatures(plan, exclude_id=None):
return signatures
def _ai_detect_violations(user, plan, person, recent_rows):
def _ai_detect_violations(user, plan, person, recent_rows, metric_context=None):
ai_obj = AI.objects.filter(user=user).first()
if ai_obj is None:
return []
return {"violations": [], "artifact_corrections": []}
rules_payload = [
{"id": str(rule.id), "title": rule.title, "content": rule.content}
@@ -2477,6 +2877,7 @@ def _ai_detect_violations(user, plan, person, recent_rows):
"games": games_payload,
"corrections": corrections_payload,
},
"metrics": metric_context or {},
"recent_messages": recent_rows,
"output_schema": {
"violations": [
@@ -2486,6 +2887,14 @@ def _ai_detect_violations(user, plan, person, recent_rows):
"clarification": "correction-style guidance",
"severity": "low|medium|high",
}
],
"artifact_corrections": [
{
"title": "short string",
"source_phrase": "artifact reference + metric rationale",
"clarification": "proactive correction mapped to an artifact",
"severity": "low|medium|high",
}
]
},
}
@@ -2494,22 +2903,31 @@ def _ai_detect_violations(user, plan, person, recent_rows):
"role": "system",
"content": (
"You detect violations of mitigation patterns in a conversation. "
"Use recent_messages for direct violations. "
"Use plan artifacts plus metrics for proactive artifact_corrections. "
"Return strict JSON only. No markdown. No prose wrapper. "
"Use only schema keys requested."
),
},
{
"role": "user",
"content": json.dumps(source_payload, ensure_ascii=False),
"content": json.dumps(source_payload, ensure_ascii=False, default=str),
},
]
try:
raw = async_to_sync(ai_runner.run_prompt)(prompt, ai_obj)
except Exception:
return []
return {"violations": [], "artifact_corrections": []}
parsed = _extract_json_object(raw) or {}
return _normalize_violation_items(parsed.get("violations") or [])
return {
"violations": _normalize_violation_items(parsed.get("violations") or []),
"artifact_corrections": _normalize_violation_items(
parsed.get("artifact_corrections")
or parsed.get("artifact_based_corrections")
or []
),
}
def _maybe_send_auto_notification(user, auto_settings, title, body):
@@ -2563,6 +2981,8 @@ def _run_auto_analysis_for_plan(
}
limit = max(10, min(int(auto_settings.sample_message_window or 40), 200))
_refresh_conversation_stability(conversation, user, person)
metric_context = _metric_pattern_context(conversation)
sessions = ChatSession.objects.filter(user=user, identifier__person=person)
messages = (
Message.objects.filter(user=user, session__in=sessions)
@@ -2609,9 +3029,25 @@ def _run_auto_analysis_for_plan(
"notified": False,
}
ai_candidates = _ai_detect_violations(user, plan, person, recent_rows)
ai_detection = _ai_detect_violations(
user,
plan,
person,
recent_rows,
metric_context=metric_context,
)
ai_candidates = list(ai_detection.get("violations") or [])
artifact_candidates_ai = list(ai_detection.get("artifact_corrections") or [])
heuristic_candidates = _detect_violation_candidates(plan, recent_rows)
violations = _normalize_violation_items(ai_candidates + heuristic_candidates)
artifact_candidates_metric = _metric_guided_artifact_candidates(
plan, metric_context
)
violations = _normalize_violation_items(
ai_candidates
+ heuristic_candidates
+ artifact_candidates_ai
+ artifact_candidates_metric
)
created_corrections = 0
if auto_settings.auto_create_corrections and violations:
@@ -2847,40 +3283,6 @@ class AIWorkspaceContactsWidget(LoginRequiredMixin, View):
class AIWorkspacePersonWidget(LoginRequiredMixin, View):
allowed_types = {"widget"}
def _message_rows(self, user, person, limit):
sessions = ChatSession.objects.filter(user=user, identifier__person=person)
identifiers = set(
PersonIdentifier.objects.filter(user=user, person=person).values_list(
"identifier", flat=True
)
)
messages = (
Message.objects.filter(user=user, session__in=sessions)
.select_related("session", "session__identifier")
.order_by("-ts")[:limit]
)
rows = []
for message in reversed(list(messages)):
inferred_direction = _infer_direction(message, identifiers)
rows.append(
{
"message": message,
"direction": inferred_direction,
"ts_label": _format_unix_ms(message.ts),
}
)
return rows
def _recent_messages(self, user, person, limit):
sessions = ChatSession.objects.filter(user=user, identifier__person=person)
messages = (
Message.objects.filter(user=user, session__in=sessions)
.select_related("session", "session__identifier")
.order_by("-ts")[:limit]
)
return list(reversed(list(messages)))
def get(self, request, type, person_id):
if type not in self.allowed_types:
return HttpResponseBadRequest("Invalid type specified")
@@ -2894,14 +3296,13 @@ class AIWorkspacePersonWidget(LoginRequiredMixin, View):
limit = max(5, min(limit, 200))
context = {
"title": f"{person.name} Timeline",
"title": f"{person.name} AI",
"unique": f"ai-person-{person.id}",
"window_content": "partials/ai-workspace-person-widget.html",
"widget_options": 'gs-w="7" gs-h="16" gs-x="0" gs-y="0" gs-min-w="4"',
"widget_options": 'gs-w="8" gs-h="11" gs-x="4" gs-y="0" gs-min-w="4"',
"person": person,
"workspace_conversation": conversation,
"limit": limit,
"message_rows": self._message_rows(request.user, person, limit),
"ai_operations": [
("artifacts", "Plan"),
("summarise", "Summary"),
@@ -2915,6 +3316,32 @@ class AIWorkspacePersonWidget(LoginRequiredMixin, View):
return render(request, "mixins/wm/widget.html", context)
class AIWorkspacePersonTimelineWidget(LoginRequiredMixin, View):
allowed_types = {"widget"}
def get(self, request, type, person_id):
if type not in self.allowed_types:
return HttpResponseBadRequest("Invalid type specified")
person = get_object_or_404(Person, pk=person_id, user=request.user)
try:
limit = int(request.GET.get("limit", 20))
except (TypeError, ValueError):
limit = 20
limit = max(5, min(limit, 200))
context = {
"title": f"{person.name} Timeline",
"unique": f"ai-timeline-{person.id}",
"window_content": "partials/ai-workspace-person-timeline-widget.html",
"widget_options": 'gs-w="8" gs-h="10" gs-x="4" gs-y="11" gs-min-w="4"',
"person": person,
"limit": limit,
"message_rows": _message_rows_for_person(request.user, person, limit),
}
return render(request, "mixins/wm/widget.html", context)
class AIWorkspaceInsightDetail(LoginRequiredMixin, View):
allowed_types = {"page", "widget"}
@@ -2953,6 +3380,10 @@ class AIWorkspaceInsightDetail(LoginRequiredMixin, View):
"ai_workspace_insight_help",
kwargs={"type": "page", "person_id": person.id},
),
"information_url": reverse(
"ai_workspace_information",
kwargs={"type": "page", "person_id": person.id},
),
"workspace_url": f"{reverse('ai_workspace')}?person={person.id}",
}
return render(request, "pages/ai-workspace-insight-detail.html", context)
@@ -2976,11 +3407,62 @@ class AIWorkspaceInsightGraphs(LoginRequiredMixin, View):
"ai_workspace_insight_help",
kwargs={"type": "page", "person_id": person.id},
),
"information_url": reverse(
"ai_workspace_information",
kwargs={"type": "page", "person_id": person.id},
),
"workspace_url": f"{reverse('ai_workspace')}?person={person.id}",
}
return render(request, "pages/ai-workspace-insight-graphs.html", context)
class AIWorkspaceInformation(LoginRequiredMixin, View):
allowed_types = {"page", "widget"}
def get(self, request, type, person_id):
if type not in self.allowed_types:
return HttpResponseBadRequest("Invalid type specified")
person = get_object_or_404(Person, pk=person_id, user=request.user)
conversation = _conversation_for_person(request.user, person)
latest_snapshot = conversation.metric_snapshots.first()
directionality = _commitment_directionality_payload(conversation)
commitment_graph_cards = [
card for card in _all_graph_payload(conversation) if card["group"] == "commitment"
]
graph_refs = []
for ref in directionality.get("graph_refs", []):
slug = ref.get("slug")
if not slug:
continue
graph_refs.append(
{
**ref,
"slug": slug,
"value": _format_metric_value(conversation, slug, latest_snapshot),
}
)
directionality["graph_refs"] = graph_refs
context = {
"person": person,
"workspace_conversation": conversation,
"directionality": directionality,
"commitment_graph_cards": commitment_graph_cards,
"graphs_url": reverse(
"ai_workspace_insight_graphs",
kwargs={"type": "page", "person_id": person.id},
),
"help_url": reverse(
"ai_workspace_insight_help",
kwargs={"type": "page", "person_id": person.id},
),
"workspace_url": f"{reverse('ai_workspace')}?person={person.id}",
}
return render(request, "pages/ai-workspace-information.html", context)
class AIWorkspaceInsightHelp(LoginRequiredMixin, View):
allowed_types = {"page", "widget"}
@@ -3018,6 +3500,10 @@ class AIWorkspaceInsightHelp(LoginRequiredMixin, View):
"ai_workspace_insight_graphs",
kwargs={"type": "page", "person_id": person.id},
),
"information_url": reverse(
"ai_workspace_information",
kwargs={"type": "page", "person_id": person.id},
),
"workspace_url": f"{reverse('ai_workspace')}?person={person.id}",
}
return render(request, "pages/ai-workspace-insight-help.html", context)
@@ -3164,7 +3650,7 @@ class AIWorkspaceRunOperation(LoginRequiredMixin, View):
and auto_settings.enabled
and auto_settings.auto_create_mitigation
):
recent_messages = AIWorkspacePersonWidget()._recent_messages(
recent_messages = _recent_messages_for_person(
request.user,
person,
max(20, min(auto_settings.sample_message_window, 200)),
@@ -3609,6 +4095,8 @@ class AIWorkspaceCreateMitigation(LoginRequiredMixin, View):
else _conversation_for_person(request.user, person)
)
conversation.participants.add(person)
_refresh_conversation_stability(conversation, request.user, person)
metric_context = _metric_pattern_context(conversation)
source_text = ""
if source_result is not None:
@@ -3625,6 +4113,7 @@ class AIWorkspaceCreateMitigation(LoginRequiredMixin, View):
inspiration=user_context,
fundamentals=fundamentals,
output_profile=output_profile,
metric_context=metric_context,
)
# Deterministically seed from pasted context so long-form frameworks can
# create fundamentals/rules/games in one pass, even when AI output is sparse.
@@ -3657,6 +4146,31 @@ class AIWorkspaceCreateMitigation(LoginRequiredMixin, View):
instructions=str(game.get("instructions") or "").strip(),
)
existing_signatures = set()
for correction in artifacts.get("corrections", []):
title = _normalize_correction_title(
correction.get("title") or "", fallback="Correction"
)
clarification = str(correction.get("clarification") or "").strip()
source_phrase = str(correction.get("source_phrase") or "").strip()
if not clarification:
continue
signature = _correction_signature(title, clarification)
if signature in existing_signatures:
continue
PatternMitigationCorrection.objects.create(
user=request.user,
plan=plan,
title=title[:255],
clarification=clarification[:2000],
source_phrase=source_phrase[:1000],
perspective="second_person",
share_target="both",
language_style="adapted",
enabled=True,
)
existing_signatures.add(signature)
PatternMitigationMessage.objects.create(
user=request.user,
plan=plan,