""" Prediction Router Handles single and batch predictions """ import io import csv from typing import List, Dict from datetime import datetime from fastapi import APIRouter, Depends, HTTPException, status, UploadFile, File, Form from fastapi.responses import StreamingResponse from sqlalchemy.orm import Session from app.database import get_db from app.models import User, PredictionHistory from app.schemas import ( SinglePredictionRequest, SinglePredictionResponse, BatchPredictionResponse, PredictionHistoryResponse, PDFReportRequest ) from app.services.auth_service import get_current_user from app.services.ml_service import get_ml_service, MLPredictionService from app.services.visualization_service import get_viz_service, VisualizationService from app.services.report_service import get_report_service, ReportService router = APIRouter() @router.post("/single", response_model=SinglePredictionResponse) async def predict_single( request: SinglePredictionRequest, current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ml_service: MLPredictionService = Depends(get_ml_service) ): """ Predict rating for a single comment - **product_name**: Name of the product - **comment**: Vietnamese product review text Returns predicted rating (1-5 stars) with confidence score """ # Make prediction prediction = ml_service.predict_single(request.comment) # Save to history history = PredictionHistory( user_id=current_user.id, product_name=request.product_name, comment=request.comment, predicted_rating=prediction['rating'], confidence_score=prediction['confidence'], prediction_type='single' ) db.add(history) db.commit() return { "predicted_rating": prediction['rating'], "confidence_score": prediction['confidence'], "comment": request.comment } @router.post("/batch", response_model=BatchPredictionResponse) async def predict_batch( product_name: str = Form(None), file: UploadFile = File(...), current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ml_service: MLPredictionService = Depends(get_ml_service), viz_service: VisualizationService = Depends(get_viz_service), report_service: ReportService = Depends(get_report_service) ): """ Predict ratings for batch of comments from CSV file - **product_name**: Name of the product - **file**: CSV file with 'Comment' column Returns predictions with visualization data (wordcloud, distribution chart) """ # Validate file type if not file.filename.endswith('.csv'): raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, detail="File must be a CSV" ) try: # Read CSV file contents = await file.read() csv_file = io.StringIO(contents.decode('utf-8')) reader = csv.DictReader(csv_file) # Check for Comment column if 'Comment' not in reader.fieldnames: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, detail="CSV must contain 'Comment' column" ) # Extract comments comments = [] for row in reader: if row.get('Comment', '').strip(): comments.append(row['Comment'].strip()) if not comments: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, detail="No valid comments found in CSV" ) # Make batch predictions predictions = ml_service.predict_batch(comments) final_product_name = product_name if product_name else "Unknown Product" # Save to history for pred in predictions: history = PredictionHistory( user_id=current_user.id, product_name=final_product_name, comment=pred['text'], predicted_rating=pred['rating'], confidence_score=pred['confidence'], prediction_type='batch' ) db.add(history) db.commit() # Calculate rating distribution ratings = [p['rating'] for p in predictions] distribution = viz_service.calculate_rating_distribution(ratings) # Generate word cloud wordcloud_filename = f"wordcloud_{current_user.username}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png" wordcloud_url = viz_service.generate_wordcloud(comments, wordcloud_filename) # Prepare results for CSV download results = [] for pred in predictions: results.append({ 'Comment': pred['text'], 'Predicted_Rating': pred['rating'], 'Confidence': pred['confidence'] }) # Generate PDF report pdf_filename = f"report_{current_user.username}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf" pdf_content = report_service.generate_pdf_report( predictions=predictions, distribution=distribution, wordcloud_path=wordcloud_url, username=current_user.username, filename=pdf_filename ) return { "total_predictions": len(predictions), "rating_distribution": distribution, "wordcloud_url": wordcloud_url, "results": results, "csv_download_url": f"/api/predict/download/{current_user.id}/{datetime.now().timestamp()}", "pdf_download_url": f"/api/predict/download-pdf/{current_user.id}/{datetime.now().timestamp()}" } except Exception as e: raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f"Error processing file: {str(e)}" ) @router.get("/history", response_model=List[PredictionHistoryResponse]) async def get_prediction_history( limit: int = 50, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): """ Get prediction history for current user - **limit**: Maximum number of records to return (default: 50) """ history = db.query(PredictionHistory).filter( PredictionHistory.user_id == current_user.id ).order_by(PredictionHistory.created_at.desc()).limit(limit).all() return history @router.post("/download-csv") async def download_predictions_csv( results: List[dict], current_user: User = Depends(get_current_user) ): """ Download prediction results as CSV """ # Create CSV in memory output = io.StringIO() if results: fieldnames = results[0].keys() writer = csv.DictWriter(output, fieldnames=fieldnames) writer.writeheader() writer.writerows(results) # Reset position output.seek(0) # Return as streaming response return StreamingResponse( iter([output.getvalue()]), media_type="text/csv", headers={ "Content-Disposition": f"attachment; filename=predictions_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv" } ) @router.post("/download-pdf") async def download_predictions_pdf( request: PDFReportRequest, current_user: User = Depends(get_current_user), report_service: ReportService = Depends(get_report_service) ): """ Download prediction results as PDF report """ try: pdf_content = report_service.generate_pdf_report( predictions=request.predictions, distribution=request.distribution, wordcloud_path=request.wordcloud_path, username=current_user.username ) return StreamingResponse( io.BytesIO(pdf_content), media_type="application/pdf", headers={ "Content-Disposition": f"attachment; filename=predictions_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf" } ) except Exception as e: raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f"Error generating PDF: {str(e)}" )