UPDATE

weather/water_need_prediction.py

@@ -0,0 +1,83 @@
+from __future__ import annotations
+
+from typing import Any
+
+from irrigation.evapotranspiration import calculate_forecast_water_needs, resolve_crop_profile
+
+from farm_data.models import SensorData
+from rag.services import get_water_need_prediction_insight
+
+from .services import get_forecast_for_location
+
+
+def build_water_need_prediction_payload(*, sensor: Any, forecasts: list[Any]) -> dict[str, Any]:
+    location = getattr(sensor, "center_location", None)
+    plants = list(sensor.plants.all()) if hasattr(sensor, "plants") else []
+    plant = plants[0] if plants else None
+    irrigation_method = getattr(sensor, "irrigation_method", None)
+
+    if not forecasts or location is None:
+        return {
+            "totalNext7Days": 0,
+            "unit": "mm",
+            "categories": [],
+            "series": [],
+            "dailyBreakdown": [],
+            "cropProfile": {},
+            "irrigationEfficiencyPercent": None,
+        }
+
+    crop_profile = resolve_crop_profile(plant)
+    efficiency = getattr(irrigation_method, "water_efficiency_percent", None) if irrigation_method else None
+    daily = calculate_forecast_water_needs(
+        forecasts=forecasts[:7],
+        latitude_deg=float(location.latitude),
+        crop_profile=crop_profile,
+        growth_stage=crop_profile.get("current_stage"),
+        irrigation_efficiency_percent=efficiency,
+    )
+    daily_requirements = [round(item["gross_irrigation_mm"], 2) for item in daily]
+
+    return {
+        "totalNext7Days": round(sum(daily_requirements), 2),
+        "unit": "mm",
+        "categories": [f"روز {index}" for index in range(1, len(daily_requirements) + 1)],
+        "series": [{"name": "نیاز آبی تعدیل‌شده", "data": daily_requirements}],
+        "dailyBreakdown": daily,
+        "cropProfile": crop_profile,
+        "irrigationEfficiencyPercent": efficiency,
+    }
+
+
+class WaterNeedPredictionService:
+    def get_water_need_prediction(self, *, farm_uuid: str) -> dict[str, Any]:
+        sensor = (
+            SensorData.objects.select_related("center_location", "irrigation_method")
+            .prefetch_related("plants")
+            .filter(farm_uuid=farm_uuid)
+            .first()
+        )
+        if sensor is None:
+            raise ValueError("Farm not found.")
+
+        forecasts = get_forecast_for_location(sensor.center_location, days=7)
+        payload = build_water_need_prediction_payload(sensor=sensor, forecasts=forecasts)
+        insight = get_water_need_prediction_insight(
+            farm_uuid=farm_uuid,
+            prediction_payload=payload,
+        )
+
+        return {
+            "farm_uuid": farm_uuid,
+            **payload,
+            "insight": {
+                "summary": insight.get("summary"),
+                "irrigation_outlook": insight.get("irrigation_outlook"),
+                "recommended_action": insight.get("recommended_action"),
+                "risk_note": insight.get("risk_note"),
+                "confidence": insight.get("confidence"),
+            },
+            "knowledge_base": insight.get("knowledge_base"),
+            "tone_file": insight.get("tone_file"),
+            "raw_response": insight.get("raw_response"),
+        }