46dea3304f
This commit integrates the backend API for fetching and updating report data. It also includes a normalization function to handle data consistency between the API and local storage. Co-authored-by: anthonymuncher <anthonymuncher@gmail.com>
101 lines
3.4 KiB
Python
101 lines
3.4 KiB
Python
from fastapi import APIRouter, UploadFile, File, Form, Depends, HTTPException
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from fastapi.responses import JSONResponse
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from sqlalchemy.orm import Session
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from app.database import get_db
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from app.services.ticket_service import TicketService, SeverityLevel
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from app.models.ticket_model import User
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from app.services.global_ai import get_ai_service
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import os, uuid, logging
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router = APIRouter()
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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UPLOAD_DIR = "app/static/uploads"
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os.makedirs(UPLOAD_DIR, exist_ok=True)
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@router.post("/report")
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async def report_issue(
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user_id: str = Form(...),
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latitude: float = Form(...),
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longitude: float = Form(...),
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description: str = Form(""),
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image: UploadFile = File(...),
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db: Session = Depends(get_db)
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):
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logger.debug("Received report request")
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ticket_service = TicketService(db)
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# Validate user
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user = db.query(User).filter(User.id == user_id).first()
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if not user:
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logger.error(f"User with id {user_id} not found")
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raise HTTPException(status_code=404, detail=f"User with id {user_id} not found")
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logger.debug(f"User found: {user.name} ({user.email})")
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# Save uploaded image
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file_ext = os.path.splitext(image.filename)[1]
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filename = f"{uuid.uuid4()}{file_ext}"
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file_path = os.path.join(UPLOAD_DIR, filename)
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try:
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content = await image.read()
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with open(file_path, "wb") as f:
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f.write(content)
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logger.debug(f"Saved image to {file_path} ({len(content)} bytes)")
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except Exception as e:
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logger.exception("Failed to save uploaded image")
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raise HTTPException(status_code=500, detail="Failed to save uploaded image")
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# Get initialized AI service
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ai_service = get_ai_service()
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logger.debug("AI service ready")
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# Run AI predictions
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try:
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category = ai_service.classify_category(file_path)
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logger.debug(f"Classification: {category}")
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if category.lower() == "pothole":
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severity_str, annotated_path = ai_service.detect_pothole_severity(file_path)
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logger.debug(f"Detection: severity={severity_str}, path={annotated_path}")
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severity = {
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"High": SeverityLevel.HIGH,
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"Medium": SeverityLevel.MEDIUM,
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"Low": SeverityLevel.LOW,
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"Unknown": SeverityLevel.NA
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}.get(severity_str, SeverityLevel.NA)
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else:
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severity = SeverityLevel.NA
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logger.debug("No detection needed")
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except Exception as e:
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logger.exception("AI prediction failed")
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category = "Unknown"
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severity = SeverityLevel.NA
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# Create ticket
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ticket = ticket_service.create_ticket(
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user_id=user.id,
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image_path=file_path,
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category=category,
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severity=severity,
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latitude=latitude,
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longitude=longitude,
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description=description
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)
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logger.info(f"Ticket created: {ticket.id} for user {user.id}")
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response = {
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"ticket_id": ticket.id,
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"user_id": user.id,
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"user_name": user.name,
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"user_email": user.email,
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"category": ticket.category,
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"severity": ticket.severity.value,
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"status": ticket.status.value,
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"description": ticket.description,
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"image_path": ticket.image_path
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}
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logger.debug(f"Response: {response}")
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return JSONResponse(status_code=201, content=response)
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