update name

models/label.py

@@ -1 +1,98 @@
-c
+"""Pydantic schemas for key-point matching prediction endpoints"""
+
+from pydantic import BaseModel, Field, ConfigDict
+from typing import List, Optional, Dict
+
+
+class PredictionRequest(BaseModel):
+    """Request model for single key-point/argument prediction"""
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "argument": "Climate change is accelerating due to industrial emissions.",
+                "key_point": "Human industry contributes significantly to global warming."
+            }
+        }
+    )
+
+    argument: str = Field(
+        ..., min_length=5, max_length=1000,
+        description="The argument text to evaluate"
+    )
+    key_point: str = Field(
+        ..., min_length=5, max_length=500,
+        description="The key point used for comparison"
+    )
+
+
+class PredictionResponse(BaseModel):
+    """Response model for single prediction"""
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "prediction": 1,
+                "confidence": 0.874,
+                "label": "MATCH",
+                "probabilities": {
+                    "match": 0.874,
+                    "no_match": 0.126
+                }
+            }
+        }
+    )
+
+    prediction: int = Field(..., description="1 = match, 0 = no match")
+    confidence: float = Field(..., ge=0.0, le=1.0,
+                              description="Confidence score of the prediction")
+    label: str = Field(..., description="MATCH or NO_MATCH")
+    probabilities: Dict[str, float] = Field(
+        ..., description="Dictionary of class probabilities"
+    )
+
+
+class BatchPredictionRequest(BaseModel):
+    """Request model for batch predictions"""
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "pairs": [
+                    {
+                        "argument": "Schools should implement AI tools to support learning.",
+                        "key_point": "AI can improve student engagement."
+                    },
+                    {
+                        "argument": "Governments must reduce plastic usage.",
+                        "key_point": "Plastic waste harms the environment."
+                    }
+                ]
+            }
+        }
+    )
+
+    pairs: List[PredictionRequest] = Field(
+        ..., max_length=100,
+        description="List of argument-keypoint pairs (max 100)"
+    )
+
+
+class BatchPredictionResponse(BaseModel):
+    """Response model for batch key-point predictions"""
+    predictions: List[PredictionResponse]
+    total_processed: int = Field(..., description="Number of processed items")
+
+
+class HealthResponse(BaseModel):
+    """Health check model for the API"""
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "status": "ok",
+                "model_loaded": True,
+                "device": "cuda"
+            }
+        }
+    )
+    
+    status: str = Field(..., description="API health status")
+    model_loaded: bool = Field(..., description="Whether the model is loaded")
+    device: str = Field(..., description="Device used for inference (cpu/cuda)")