93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
from __future__ import annotations
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from typing import Any
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from farm_data.models import SensorData
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from .remote_sensing import fetch_or_get_ndvi_observation
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def _ndvi_explanation(observation, ai_bundle: dict | None = None) -> str:
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ai_bundle = ai_bundle or {}
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ai_payload = ai_bundle.get("ndviHealthCard", {}) if isinstance(ai_bundle, dict) else {}
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explanation = ai_payload.get("explanation")
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if isinstance(explanation, str) and explanation.strip():
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return explanation.strip()
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return (
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f"میانگین NDVI مزرعه {observation.mean_ndvi} ثبت شده و کلاس سلامت پوشش گیاهی "
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f"در وضعیت {observation.vegetation_health_class} قرار دارد."
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)
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def _build_ndvi_health_card(location: Any, ai_bundle: dict | None = None) -> dict[str, Any]:
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if location is None:
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return {
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"mean_ndvi": None,
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"ndvi_map": {},
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"vegetation_health_class": None,
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"observation_date": None,
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"satellite_source": None,
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"healthData": [],
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}
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observation = fetch_or_get_ndvi_observation(location)
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if observation is None:
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return {
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"mean_ndvi": None,
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"ndvi_map": {},
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"vegetation_health_class": "Unavailable",
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"observation_date": None,
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"satellite_source": None,
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"healthData": [
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{
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"title": "وضعیت NDVI",
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"value": "داده ماهوارهای موجود نیست",
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"color": "warning",
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"icon": "tabler-satellite-off",
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},
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],
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}
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mean_value = round(observation.mean_ndvi, 2)
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vegetation_class = observation.vegetation_health_class
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return {
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"ndviIndex": mean_value,
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"mean_ndvi": mean_value,
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"ndvi_map": observation.ndvi_map,
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"vegetation_health_class": vegetation_class,
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"observation_date": observation.observation_date.isoformat(),
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"satellite_source": observation.satellite_source,
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"healthData": [
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{
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"title": "سلامت پوشش گیاهی",
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"value": vegetation_class,
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"color": "success" if mean_value > 0.6 else "warning" if mean_value >= 0.4 else "error",
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"icon": "tabler-plant",
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},
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{
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"title": "تاریخ مشاهده",
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"value": observation.observation_date.isoformat(),
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"color": "info",
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"icon": "tabler-calendar",
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},
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{
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"title": "تفسیر",
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"value": _ndvi_explanation(observation, ai_bundle=ai_bundle),
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"color": "primary",
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"icon": "tabler-message-2",
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},
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],
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}
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class NdviHealthService:
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def get_ndvi_health(self, *, farm_uuid: str) -> dict[str, Any]:
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sensor = (
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SensorData.objects.select_related("center_location")
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.filter(farm_uuid=farm_uuid)
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.first()
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)
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if sensor is None:
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raise ValueError("Farm not found.")
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return _build_ndvi_health_card(sensor.center_location, ai_bundle=None)
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