Update schemas.py

schemas.py

@@ -1,41 +1,71 @@
 from pydantic import BaseModel
-from typing import Dict, List, Optional, Union
+from typing import Dict, List, Optional, Union, Any
+
+# =============================================================================
+# REQUEST SCHEMA
+# =============================================================================
 
 class PredictRequest(BaseModel):
     """
-    Forest segmentation prediction request
-    
-    Supports Landsat 8 Collection 2 Level 2 data format.
-    Each band can be provided as:
-    - Base64-encoded float32 data (for remote API calls)
-    - Array/list format (for direct server calls)
-    
-    Required bands:
-      - Blue, Green, Red: Optical bands
-      - NIR, SWIR1, SWIR2: Infrared bands
-      - NDVI, NDWI, NBR: Spectral indices (or server will compute them)
-    
-    Optional special keys:
-      - _invert_mask: Set to true to invert forest/non-forest in response
-    
-    Value range expectations:
-      - Optical bands: [-0.2, 0.6]
-      - Indices: [-1, 1]
+    Forest segmentation prediction request.
+
+    This schema is intentionally flexible to support:
+    - Supabase Edge Functions
+    - Hugging Face remote inference
+    - Local inference scripts
+
+    Required:
+    - bands: Dict[str, Union[str, List[float]]]
+
+    Allowed extra fields (sent by Supabase):
+    - width, height
+    - bbox
+    - band_names
+    - preprocessing
+    - model_name, model_version
     """
+
     model_name: str = "forest_segmentation"
     model_version: str = "landsat8_v1"
-    bands: Dict[str, Union[str, List, int]]  # Band data as base64 or array
 
+    # Band data:
+    # - base64-encoded float32 (remote calls)
+    # - list/array of floats (local calls)
+    bands: Dict[str, Union[str, List[float]]]
+
+    class Config:
+        # 🔑 CRITICAL FIX
+        # Allows Supabase to send extra metadata without 422 errors
+        extra = "allow"
 
-from typing import List, Dict, Any, Optional
-from pydantic import BaseModel
+
+# =============================================================================
+# RESPONSE SCHEMA
+# =============================================================================
 
 class PredictResponse(BaseModel):
-    mask: List[int]                    # 1D flat list ✓
-    inverted_mask: List[int]           # 1D flat list ✓
+    """
+    Forest segmentation prediction response.
+
+    Mask values are CONTINUOUS (0–255) — NOT binary.
+    """
+
+    # Flattened mask (length = width * height)
+    mask: List[int]
+
+    # Inverted mask (optional utility)
+    inverted_mask: List[int]
+
+    # Statistics
     forest_percentage: float
     forest_confidence: float
     mean_prediction: float
+
+    # Class mapping
     classes: Dict[str, int]
+
+    # Model metadata
     model_info: Dict[str, Any]
+
+    # Optional debug block
     debug: Optional[Dict[str, Any]] = None