"""Example snippets for the code review UI."""

from __future__ import annotations


EXAMPLES = {
    "Boundary Bug": {
        "domain_hint": "dsa",
        "context_window": "Analytics helper that groups sorted events into session windows.",
        "traceback_text": "AssertionError: expected [(1, 3), (8, 8)] but got [(1, 8)] on the boundary case.",
        "code": """def collapse_sessions(events, idle_timeout_minutes):\n    if not events:\n        return []\n\n    sessions = []\n    current_start = events[0]['minute']\n    current_end = current_start\n\n    for event in events[1:]:\n        minute = event['minute']\n        if minute - current_end > idle_timeout_minutes:\n            sessions.append((current_start, current_end))\n            current_start = minute\n        current_end = minute\n\n    return sessions\n""",
    },
    "Performance Hotspot": {
        "domain_hint": "dsa",
        "context_window": "Nightly export job running on a small CPU box with rising traffic volume.",
        "traceback_text": "BenchmarkWarning: function exceeded latency budget due to repeated full-list scans.",
        "code": """def rank_active_users(events):\n    users = []\n    for event in events:\n        if event['status'] == 'active':\n            found = False\n            for existing in users:\n                if existing == event['user_id']:\n                    found = True\n            if not found:\n                users.append(event['user_id'])\n\n    totals = []\n    for user in users:\n        count = 0\n        for event in events:\n            if event['status'] == 'active' and event['user_id'] == user:\n                count += 1\n        totals.append((user, count))\n\n    totals.sort(key=lambda item: (-item[1], item[0]))\n    return totals\n""",
    },
    "ML Inference": {
        "domain_hint": "ml_dl",
        "context_window": "Batch inference helper for a PyTorch image classifier.",
        "traceback_text": "",
        "code": """import torch\n\nclass Predictor:\n    def __init__(self, model):\n        self.model = model\n\n    def predict(self, batch):\n        outputs = self.model(batch)\n        return outputs.argmax(dim=1)\n""",
    },
    "FastAPI Endpoint": {
        "domain_hint": "web",
        "context_window": "Backend endpoint for creating review tasks from user-submitted payloads.",
        "traceback_text": "",
        "code": """from fastapi import FastAPI, Request\n\napp = FastAPI()\n\n@app.post('/tasks')\ndef create_task(request: Request):\n    payload = request.json()\n    return {'task': payload}\n""",
    },
}