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from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
from typing import Dict, Union, List
from models.text_classification import TextClassificationModel
router = APIRouter()
model = TextClassificationModel()
class TextInput(BaseModel):
text: str
class BatchTextInput(BaseModel):
texts: List[str]
class PredictionResponse(BaseModel):
label: str
confidence: float
class BatchPredictionResponse(BaseModel):
predictions: List[PredictionResponse]
@router.post("/predict", response_model=PredictionResponse)
async def predict(input_data: TextInput) -> Dict[str, Union[str, float]]:
"""Make a prediction for a single text."""
try:
result = await model.predict(input_data.text)
return result
except Exception as e:
raise HTTPException(
status_code=500,
detail=f"Prediction failed: {str(e)}"
)
@router.post("/predict_batch", response_model=BatchPredictionResponse)
async def predict_batch(input_data: BatchTextInput) -> Dict[str, List[Dict[str, Union[str, float]]]]:
"""Make predictions for multiple texts."""
try:
predictions = []
for text in input_data.texts:
result = await model.predict(text)
predictions.append(result)
return {"predictions": predictions}
except Exception as e:
raise HTTPException(
status_code=500,
detail=f"Batch prediction failed: {str(e)}"
)
@router.get("/info")
async def get_model_info():
"""Get information about the text classification model."""
return model.get_info() |