Upload schemas.py

app/schemas.py

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+from pydantic import BaseModel, ConfigDict
+from typing import Dict
+
+# Input Data Model
+class WeatherInput(BaseModel):
+    tmmn: float  # temp min (Kelvin)
+    tmmx: float  # temp max (Kelvin)
+    rmin: float  # humidity min (%)
+    rmax: float  # humidity max (%)
+    vs: float    # wind speed (m/s)
+    pr: float    # precipitation (mm)
+    erc: float   # energy release component
+    latitude: float
+    longitude: float
+
+    # Pydantic V2 Config
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "tmmn": 290.5,
+                "tmmx": 305.2,
+                "rmin": 12.5,
+                "rmax": 45.0,
+                "vs": 5.4,
+                "pr": 0.0,
+                "erc": 48.0,
+                "latitude": 34.05,
+                "longitude": -118.25
+            }
+        }
+    )
+
+# Output Data Model
+class PredictionOutput(BaseModel):
+    burning_index_prediction: float
+    risk_level_prediction: str
+    cluster_zone: int
+    pca_x: float
+    pca_y: float
+    seasonal_trend: Dict[int, float]  # Maps Month (1-12) to Avg Intensity
+    
+    # Pydantic V2 Config with example
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "burning_index_prediction": 65.8,
+                "risk_level_prediction": "Medium",
+                "cluster_zone": 2,
+                "pca_x": -1.25,
+                "pca_y": 0.83,
+                "seasonal_trend": {
+                    1: 42.3, 2: 44.1, 3: 47.8, 4: 52.4, 
+                    5: 58.9, 6: 67.2, 7: 75.6, 8: 78.3, 
+                    9: 71.8, 10: 62.4, 11: 51.2, 12: 45.7
+                }
+            }
+        }
+    )
+
+# Optional: Model for PCA Projection endpoint
+class PCAProjectionOutput(BaseModel):
+    pca_x: float
+    pca_y: float
+    explained_variance_ratio: list[float]
+    
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "pca_x": -1.25,
+                "pca_y": 0.83,
+                "explained_variance_ratio": [0.65, 0.25]
+            }
+        }
+    )
+
+# Optional: Model for Seasonal Trend endpoint
+class SeasonalTrendOutput(BaseModel):
+    description: str
+    trend: Dict[int, float]
+    units: str
+    
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "description": "Average Burning Index by Month",
+                "trend": {
+                    1: 42.3, 2: 44.1, 3: 47.8, 4: 52.4, 
+                    5: 58.9, 6: 67.2, 7: 75.6, 8: 78.3, 
+                    9: 71.8, 10: 62.4, 11: 51.2, 12: 45.7
+                },
+                "units": "Burning Index (BI)"
+            }
+        }
+    )
+
+# Optional: Health check response model
+class HealthResponse(BaseModel):
+    status: str
+    models_loaded: list[str] = []
+    missing: list[str] = []
+    detail: str = ""
+    version: str = ""
+    
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "status": "healthy",
+                "models_loaded": ["regression", "classification", "clustering", "encoder", "pca", "seasonality"],
+                "missing": [],
+                "detail": "",
+                "version": "1.1.0"
+            }
+        }
+    )
+
+# Optional: Model info response model
+class ModelInfo(BaseModel):
+    type: str
+    details: dict
+    
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "regression": {
+                    "type": "RandomForest",
+                    "n_estimators": 50,
+                    "n_features": 7
+                }
+            }
+        }
+    )