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"""Stance detection endpoints""" |
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from fastapi import APIRouter, HTTPException |
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from datetime import datetime |
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import logging |
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from models import ( |
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StanceRequest, |
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StanceResponse, |
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BatchStanceRequest, |
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BatchStanceResponse, |
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) |
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from services import stance_model_manager |
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router = APIRouter() |
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logger = logging.getLogger(__name__) |
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@router.post("/predict", response_model=StanceResponse, tags=["Stance Detection"]) |
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async def predict_stance(request: StanceRequest): |
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""" |
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Predict stance for a single topic-argument pair |
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- **topic**: The debate topic or statement (5-500 chars) |
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- **argument**: The argument to classify (5-1000 chars) |
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Returns predicted stance (PRO/CON) with confidence scores |
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""" |
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try: |
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result = stance_model_manager.predict(request.topic, request.argument) |
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response = StanceResponse( |
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topic=request.topic, |
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argument=request.argument, |
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predicted_stance=result["predicted_stance"], |
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confidence=result["confidence"], |
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probability_con=result["probability_con"], |
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probability_pro=result["probability_pro"], |
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timestamp=datetime.now().isoformat() |
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) |
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logger.info(f"Prediction: {result['predicted_stance']} ({result['confidence']:.4f})") |
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return response |
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except Exception as e: |
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logger.error(f"Prediction error: {str(e)}") |
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raise HTTPException(status_code=500, detail=f"Prediction failed: {str(e)}") |
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@router.post("/batch-predict", response_model=BatchStanceResponse, tags=["Stance Detection"]) |
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async def batch_predict_stance(request: BatchStanceRequest): |
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""" |
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Predict stance for multiple topic-argument pairs |
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- **items**: List of topic-argument pairs (max 50) |
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Returns predictions for all items |
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""" |
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try: |
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results = [] |
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for item in request.items: |
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result = stance_model_manager.predict(item.topic, item.argument) |
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response = StanceResponse( |
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topic=item.topic, |
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argument=item.argument, |
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predicted_stance=result["predicted_stance"], |
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confidence=result["confidence"], |
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probability_con=result["probability_con"], |
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probability_pro=result["probability_pro"], |
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timestamp=datetime.now().isoformat() |
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) |
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results.append(response) |
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logger.info(f"Batch prediction completed: {len(results)} items") |
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return BatchStanceResponse( |
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results=results, |
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total_processed=len(results) |
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) |
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except Exception as e: |
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logger.error(f"Batch prediction error: {str(e)}") |
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raise HTTPException(status_code=500, detail=f"Batch prediction failed: {str(e)}") |
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