881 lines
29 KiB
Python
881 lines
29 KiB
Python
from fastapi import FastAPI, Depends, HTTPException, status, UploadFile, File
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
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from sqlalchemy.orm import Session, joinedload
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from typing import List
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import os
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import shutil
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from datetime import datetime, timedelta
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from app.core.database import engine, get_db, Base
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from app.core.security import verify_password, get_password_hash, create_access_token, decode_access_token
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from app import models, schemas
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# Crear tablas
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Base.metadata.create_all(bind=engine)
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app = FastAPI(title="Checklist Inteligente API", version="1.0.0")
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# CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["http://localhost:5173", "http://localhost:3000"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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security = HTTPBearer()
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# Dependency para obtener usuario actual
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def get_current_user(
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credentials: HTTPAuthorizationCredentials = Depends(security),
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db: Session = Depends(get_db)
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):
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token = credentials.credentials
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payload = decode_access_token(token)
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print(f"Token payload: {payload}") # Debug
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if payload is None:
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raise HTTPException(
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status_code=status.HTTP_401_UNAUTHORIZED,
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detail="Token inválido o expirado"
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)
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user_id = int(payload.get("sub"))
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print(f"Looking for user ID: {user_id}") # Debug
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user = db.query(models.User).filter(models.User.id == user_id).first()
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if user is None:
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print(f"User not found with ID: {user_id}") # Debug
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raise HTTPException(status_code=404, detail="Usuario no encontrado")
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return user
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# ============= AUTH ENDPOINTS =============
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@app.post("/api/auth/register", response_model=schemas.User)
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def register(user: schemas.UserCreate, db: Session = Depends(get_db)):
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# Verificar si usuario existe
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db_user = db.query(models.User).filter(models.User.username == user.username).first()
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if db_user:
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raise HTTPException(status_code=400, detail="Usuario ya existe")
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# Crear usuario
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hashed_password = get_password_hash(user.password)
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db_user = models.User(
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username=user.username,
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email=user.email,
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full_name=user.full_name,
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role=user.role,
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password_hash=hashed_password
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)
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db.add(db_user)
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db.commit()
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db.refresh(db_user)
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return db_user
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@app.post("/api/auth/login", response_model=schemas.Token)
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def login(user_login: schemas.UserLogin, db: Session = Depends(get_db)):
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user = db.query(models.User).filter(models.User.username == user_login.username).first()
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if not user or not verify_password(user_login.password, user.password_hash):
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raise HTTPException(
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status_code=status.HTTP_401_UNAUTHORIZED,
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detail="Usuario o contraseña incorrectos"
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)
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access_token = create_access_token(data={"sub": str(user.id), "role": user.role})
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return {
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"access_token": access_token,
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"token_type": "bearer",
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"user": user
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}
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@app.get("/api/auth/me", response_model=schemas.User)
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def get_me(current_user: models.User = Depends(get_current_user)):
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return current_user
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# ============= CHECKLIST ENDPOINTS =============
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@app.get("/api/checklists", response_model=List[schemas.Checklist])
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def get_checklists(
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skip: int = 0,
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limit: int = 100,
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active_only: bool = False,
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db: Session = Depends(get_db),
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current_user: models.User = Depends(get_current_user)
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):
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query = db.query(models.Checklist)
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if active_only:
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query = query.filter(models.Checklist.is_active == True)
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return query.offset(skip).limit(limit).all()
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@app.get("/api/checklists/{checklist_id}", response_model=schemas.ChecklistWithQuestions)
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def get_checklist(checklist_id: int, db: Session = Depends(get_db)):
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checklist = db.query(models.Checklist).options(
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joinedload(models.Checklist.questions)
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).filter(models.Checklist.id == checklist_id).first()
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if not checklist:
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raise HTTPException(status_code=404, detail="Checklist no encontrado")
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return checklist
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@app.post("/api/checklists", response_model=schemas.Checklist)
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def create_checklist(
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checklist: schemas.ChecklistCreate,
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db: Session = Depends(get_db),
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current_user: models.User = Depends(get_current_user)
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):
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if current_user.role != "admin":
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raise HTTPException(status_code=403, detail="No autorizado")
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db_checklist = models.Checklist(**checklist.dict(), created_by=current_user.id)
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db.add(db_checklist)
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db.commit()
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db.refresh(db_checklist)
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return db_checklist
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@app.put("/api/checklists/{checklist_id}", response_model=schemas.Checklist)
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def update_checklist(
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checklist_id: int,
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checklist: schemas.ChecklistUpdate,
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db: Session = Depends(get_db),
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current_user: models.User = Depends(get_current_user)
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):
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if current_user.role != "admin":
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raise HTTPException(status_code=403, detail="No autorizado")
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db_checklist = db.query(models.Checklist).filter(models.Checklist.id == checklist_id).first()
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if not db_checklist:
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raise HTTPException(status_code=404, detail="Checklist no encontrado")
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for key, value in checklist.dict(exclude_unset=True).items():
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setattr(db_checklist, key, value)
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db.commit()
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db.refresh(db_checklist)
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return db_checklist
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# ============= QUESTION ENDPOINTS =============
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@app.post("/api/questions", response_model=schemas.Question)
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def create_question(
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question: schemas.QuestionCreate,
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db: Session = Depends(get_db),
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current_user: models.User = Depends(get_current_user)
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):
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if current_user.role != "admin":
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raise HTTPException(status_code=403, detail="No autorizado")
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db_question = models.Question(**question.dict())
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db.add(db_question)
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# Actualizar max_score del checklist
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checklist = db.query(models.Checklist).filter(
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models.Checklist.id == question.checklist_id
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).first()
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if checklist:
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checklist.max_score += question.points
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db.commit()
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db.refresh(db_question)
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return db_question
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@app.put("/api/questions/{question_id}", response_model=schemas.Question)
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def update_question(
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question_id: int,
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question: schemas.QuestionUpdate,
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db: Session = Depends(get_db),
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current_user: models.User = Depends(get_current_user)
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):
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if current_user.role != "admin":
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raise HTTPException(status_code=403, detail="No autorizado")
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db_question = db.query(models.Question).filter(models.Question.id == question_id).first()
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if not db_question:
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raise HTTPException(status_code=404, detail="Pregunta no encontrada")
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for key, value in question.dict(exclude_unset=True).items():
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setattr(db_question, key, value)
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db.commit()
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db.refresh(db_question)
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return db_question
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@app.delete("/api/questions/{question_id}")
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def delete_question(
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question_id: int,
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db: Session = Depends(get_db),
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current_user: models.User = Depends(get_current_user)
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):
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if current_user.role != "admin":
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raise HTTPException(status_code=403, detail="No autorizado")
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db_question = db.query(models.Question).filter(models.Question.id == question_id).first()
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if not db_question:
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raise HTTPException(status_code=404, detail="Pregunta no encontrada")
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db.delete(db_question)
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db.commit()
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return {"message": "Pregunta eliminada"}
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# ============= INSPECTION ENDPOINTS =============
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@app.get("/api/inspections", response_model=List[schemas.Inspection])
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def get_inspections(
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skip: int = 0,
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limit: int = 100,
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vehicle_plate: str = None,
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status: str = None,
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db: Session = Depends(get_db),
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current_user: models.User = Depends(get_current_user)
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):
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query = db.query(models.Inspection)
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# Mecánicos solo ven sus inspecciones
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if current_user.role == "mechanic":
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query = query.filter(models.Inspection.mechanic_id == current_user.id)
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if vehicle_plate:
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query = query.filter(models.Inspection.vehicle_plate.contains(vehicle_plate))
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if status:
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query = query.filter(models.Inspection.status == status)
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return query.order_by(models.Inspection.created_at.desc()).offset(skip).limit(limit).all()
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@app.get("/api/inspections/{inspection_id}", response_model=schemas.InspectionDetail)
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def get_inspection(
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inspection_id: int,
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db: Session = Depends(get_db),
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current_user: models.User = Depends(get_current_user)
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):
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inspection = db.query(models.Inspection).options(
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joinedload(models.Inspection.checklist).joinedload(models.Checklist.questions),
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joinedload(models.Inspection.mechanic),
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joinedload(models.Inspection.answers).joinedload(models.Answer.question),
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joinedload(models.Inspection.answers).joinedload(models.Answer.media_files)
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).filter(models.Inspection.id == inspection_id).first()
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if not inspection:
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raise HTTPException(status_code=404, detail="Inspección no encontrada")
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return inspection
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@app.post("/api/inspections", response_model=schemas.Inspection)
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def create_inspection(
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inspection: schemas.InspectionCreate,
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db: Session = Depends(get_db),
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current_user: models.User = Depends(get_current_user)
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):
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# Obtener max_score del checklist
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checklist = db.query(models.Checklist).filter(
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models.Checklist.id == inspection.checklist_id
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).first()
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if not checklist:
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raise HTTPException(status_code=404, detail="Checklist no encontrado")
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db_inspection = models.Inspection(
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**inspection.dict(),
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mechanic_id=current_user.id,
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max_score=checklist.max_score
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)
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db.add(db_inspection)
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db.commit()
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db.refresh(db_inspection)
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return db_inspection
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@app.put("/api/inspections/{inspection_id}", response_model=schemas.Inspection)
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def update_inspection(
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inspection_id: int,
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inspection: schemas.InspectionUpdate,
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db: Session = Depends(get_db),
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current_user: models.User = Depends(get_current_user)
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):
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db_inspection = db.query(models.Inspection).filter(
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models.Inspection.id == inspection_id
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).first()
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if not db_inspection:
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raise HTTPException(status_code=404, detail="Inspección no encontrada")
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for key, value in inspection.dict(exclude_unset=True).items():
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setattr(db_inspection, key, value)
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db.commit()
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db.refresh(db_inspection)
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return db_inspection
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@app.post("/api/inspections/{inspection_id}/complete", response_model=schemas.Inspection)
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def complete_inspection(
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inspection_id: int,
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db: Session = Depends(get_db),
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current_user: models.User = Depends(get_current_user)
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):
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inspection = db.query(models.Inspection).filter(
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models.Inspection.id == inspection_id
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).first()
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if not inspection:
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raise HTTPException(status_code=404, detail="Inspección no encontrada")
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# Calcular score
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answers = db.query(models.Answer).filter(models.Answer.inspection_id == inspection_id).all()
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total_score = sum(a.points_earned for a in answers)
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flagged_count = sum(1 for a in answers if a.is_flagged)
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inspection.score = total_score
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inspection.percentage = (total_score / inspection.max_score * 100) if inspection.max_score > 0 else 0
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inspection.flagged_items_count = flagged_count
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inspection.status = "completed"
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inspection.completed_at = datetime.utcnow()
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db.commit()
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db.refresh(inspection)
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return inspection
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# ============= ANSWER ENDPOINTS =============
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@app.post("/api/answers", response_model=schemas.Answer)
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def create_answer(
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answer: schemas.AnswerCreate,
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db: Session = Depends(get_db),
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current_user: models.User = Depends(get_current_user)
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):
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# Obtener la pregunta para saber los puntos
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question = db.query(models.Question).filter(models.Question.id == answer.question_id).first()
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if not question:
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raise HTTPException(status_code=404, detail="Pregunta no encontrada")
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# Calcular puntos según status
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points_earned = 0
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if answer.status == "ok":
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points_earned = question.points
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elif answer.status == "warning":
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points_earned = int(question.points * 0.5)
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db_answer = models.Answer(
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**answer.dict(),
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points_earned=points_earned
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)
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db.add(db_answer)
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db.commit()
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db.refresh(db_answer)
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return db_answer
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@app.put("/api/answers/{answer_id}", response_model=schemas.Answer)
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def update_answer(
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answer_id: int,
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answer: schemas.AnswerUpdate,
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db: Session = Depends(get_db),
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current_user: models.User = Depends(get_current_user)
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):
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db_answer = db.query(models.Answer).filter(models.Answer.id == answer_id).first()
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if not db_answer:
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raise HTTPException(status_code=404, detail="Respuesta no encontrada")
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# Recalcular puntos si cambió el status
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if answer.status and answer.status != db_answer.status:
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question = db.query(models.Question).filter(
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models.Question.id == db_answer.question_id
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).first()
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if answer.status == "ok":
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db_answer.points_earned = question.points
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elif answer.status == "warning":
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db_answer.points_earned = int(question.points * 0.5)
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else:
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db_answer.points_earned = 0
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for key, value in answer.dict(exclude_unset=True).items():
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setattr(db_answer, key, value)
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db.commit()
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db.refresh(db_answer)
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return db_answer
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# ============= MEDIA FILE ENDPOINTS =============
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@app.post("/api/answers/{answer_id}/upload", response_model=schemas.MediaFile)
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async def upload_photo(
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answer_id: int,
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file: UploadFile = File(...),
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db: Session = Depends(get_db),
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current_user: models.User = Depends(get_current_user)
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):
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# Verificar que la respuesta existe
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answer = db.query(models.Answer).filter(models.Answer.id == answer_id).first()
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if not answer:
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raise HTTPException(status_code=404, detail="Respuesta no encontrada")
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# Crear directorio si no existe
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upload_dir = f"uploads/inspection_{answer.inspection_id}"
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os.makedirs(upload_dir, exist_ok=True)
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# Guardar archivo
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file_extension = file.filename.split(".")[-1]
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file_name = f"answer_{answer_id}_{datetime.now().timestamp()}.{file_extension}"
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file_path = os.path.join(upload_dir, file_name)
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with open(file_path, "wb") as buffer:
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shutil.copyfileobj(file.file, buffer)
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# Crear registro en BD
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media_file = models.MediaFile(
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answer_id=answer_id,
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file_path=file_path,
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file_type="image"
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)
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db.add(media_file)
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db.commit()
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db.refresh(media_file)
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return media_file
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# ============= AI ANALYSIS =============
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@app.get("/api/ai/models", response_model=List[schemas.AIModelInfo])
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def get_available_ai_models(current_user: models.User = Depends(get_current_user)):
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"""Obtener lista de modelos de IA disponibles"""
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if current_user.role != "admin":
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raise HTTPException(status_code=403, detail="Solo administradores pueden ver modelos de IA")
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models_list = [
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# OpenAI Models
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{
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"id": "gpt-4o",
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"name": "GPT-4o (Recomendado)",
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"provider": "openai",
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"description": "Modelo multimodal más avanzado de OpenAI, rápido y preciso para análisis de imágenes"
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},
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{
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"id": "gpt-4o-mini",
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"name": "GPT-4o Mini",
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"provider": "openai",
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"description": "Versión compacta y económica de GPT-4o, ideal para análisis rápidos"
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},
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{
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"id": "gpt-4-turbo",
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"name": "GPT-4 Turbo",
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"provider": "openai",
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"description": "Modelo potente con capacidades de visión y contexto amplio"
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},
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{
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"id": "gpt-4-vision-preview",
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"name": "GPT-4 Vision (Preview)",
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"provider": "openai",
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"description": "Modelo específico para análisis de imágenes (versión previa)"
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},
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# Gemini Models - Actualizados a versiones 2.0, 2.5 y 3.0
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{
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"id": "gemini-3-pro-preview",
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"name": "Gemini 3 Pro Preview (Último)",
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"provider": "gemini",
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"description": "Modelo de próxima generación en preview, máxima capacidad de análisis"
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},
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{
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"id": "gemini-2.5-pro",
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|
"name": "Gemini 2.5 Pro (Recomendado)",
|
|
"provider": "gemini",
|
|
"description": "Último modelo estable con excelente análisis visual y razonamiento avanzado"
|
|
},
|
|
{
|
|
"id": "gemini-2.5-flash",
|
|
"name": "Gemini 2.5 Flash",
|
|
"provider": "gemini",
|
|
"description": "Versión rápida del 2.5, ideal para inspecciones en tiempo real"
|
|
},
|
|
{
|
|
"id": "gemini-2.0-flash",
|
|
"name": "Gemini 2.0 Flash",
|
|
"provider": "gemini",
|
|
"description": "Modelo rápido y eficiente de la generación 2.0"
|
|
},
|
|
{
|
|
"id": "gemini-1.5-pro-latest",
|
|
"name": "Gemini 1.5 Pro Latest",
|
|
"provider": "gemini",
|
|
"description": "Versión estable 1.5 con contexto de 2M tokens"
|
|
},
|
|
{
|
|
"id": "gemini-1.5-flash-latest",
|
|
"name": "Gemini 1.5 Flash Latest",
|
|
"provider": "gemini",
|
|
"description": "Modelo 1.5 rápido para análisis básicos"
|
|
}
|
|
]
|
|
|
|
return models_list
|
|
|
|
|
|
@app.get("/api/ai/configuration", response_model=schemas.AIConfiguration)
|
|
def get_ai_configuration(
|
|
db: Session = Depends(get_db),
|
|
current_user: models.User = Depends(get_current_user)
|
|
):
|
|
"""Obtener configuración de IA actual"""
|
|
if current_user.role != "admin":
|
|
raise HTTPException(status_code=403, detail="Solo administradores pueden ver configuración de IA")
|
|
|
|
config = db.query(models.AIConfiguration).filter(
|
|
models.AIConfiguration.is_active == True
|
|
).first()
|
|
|
|
if not config:
|
|
raise HTTPException(status_code=404, detail="No hay configuración de IA activa")
|
|
|
|
return config
|
|
|
|
|
|
@app.post("/api/ai/configuration", response_model=schemas.AIConfiguration)
|
|
def create_ai_configuration(
|
|
config: schemas.AIConfigurationCreate,
|
|
db: Session = Depends(get_db),
|
|
current_user: models.User = Depends(get_current_user)
|
|
):
|
|
"""Crear o actualizar configuración de IA"""
|
|
if current_user.role != "admin":
|
|
raise HTTPException(status_code=403, detail="Solo administradores pueden configurar IA")
|
|
|
|
# Desactivar configuraciones anteriores
|
|
db.query(models.AIConfiguration).update({"is_active": False})
|
|
|
|
# Crear nueva configuración
|
|
new_config = models.AIConfiguration(
|
|
provider=config.provider,
|
|
api_key=config.api_key,
|
|
model_name=config.model_name,
|
|
is_active=True
|
|
)
|
|
|
|
db.add(new_config)
|
|
db.commit()
|
|
db.refresh(new_config)
|
|
|
|
return new_config
|
|
|
|
|
|
@app.put("/api/ai/configuration/{config_id}", response_model=schemas.AIConfiguration)
|
|
def update_ai_configuration(
|
|
config_id: int,
|
|
config_update: schemas.AIConfigurationUpdate,
|
|
db: Session = Depends(get_db),
|
|
current_user: models.User = Depends(get_current_user)
|
|
):
|
|
"""Actualizar configuración de IA existente"""
|
|
if current_user.role != "admin":
|
|
raise HTTPException(status_code=403, detail="Solo administradores pueden actualizar configuración de IA")
|
|
|
|
config = db.query(models.AIConfiguration).filter(
|
|
models.AIConfiguration.id == config_id
|
|
).first()
|
|
|
|
if not config:
|
|
raise HTTPException(status_code=404, detail="Configuración no encontrada")
|
|
|
|
# Actualizar campos
|
|
for key, value in config_update.dict(exclude_unset=True).items():
|
|
setattr(config, key, value)
|
|
|
|
db.commit()
|
|
db.refresh(config)
|
|
|
|
return config
|
|
|
|
|
|
@app.delete("/api/ai/configuration/{config_id}")
|
|
def delete_ai_configuration(
|
|
config_id: int,
|
|
db: Session = Depends(get_db),
|
|
current_user: models.User = Depends(get_current_user)
|
|
):
|
|
"""Eliminar configuración de IA"""
|
|
if current_user.role != "admin":
|
|
raise HTTPException(status_code=403, detail="Solo administradores pueden eliminar configuración de IA")
|
|
|
|
config = db.query(models.AIConfiguration).filter(
|
|
models.AIConfiguration.id == config_id
|
|
).first()
|
|
|
|
if not config:
|
|
raise HTTPException(status_code=404, detail="Configuración no encontrada")
|
|
|
|
db.delete(config)
|
|
db.commit()
|
|
|
|
return {"message": "Configuración eliminada correctamente"}
|
|
|
|
|
|
@app.post("/api/analyze-image")
|
|
async def analyze_image(
|
|
file: UploadFile = File(...),
|
|
question_id: int = None,
|
|
db: Session = Depends(get_db),
|
|
current_user: models.User = Depends(get_current_user)
|
|
):
|
|
"""
|
|
Analiza una imagen usando IA para sugerir respuestas
|
|
Usa la configuración de IA activa (OpenAI o Gemini)
|
|
"""
|
|
# Obtener configuración de IA activa
|
|
ai_config = db.query(models.AIConfiguration).filter(
|
|
models.AIConfiguration.is_active == True
|
|
).first()
|
|
|
|
if not ai_config:
|
|
return {
|
|
"status": "disabled",
|
|
"message": "No hay configuración de IA activa. Configure en Settings."
|
|
}
|
|
|
|
# Guardar imagen temporalmente
|
|
import base64
|
|
|
|
contents = await file.read()
|
|
image_b64 = base64.b64encode(contents).decode('utf-8')
|
|
|
|
# Obtener contexto de la pregunta si se proporciona
|
|
question_obj = None
|
|
if question_id:
|
|
question_obj = db.query(models.Question).filter(models.Question.id == question_id).first()
|
|
|
|
try:
|
|
# Construir prompt dinámico basado en la pregunta específica
|
|
if question_obj:
|
|
# Prompt altamente específico para la pregunta
|
|
question_text = question_obj.text
|
|
question_type = question_obj.type
|
|
section = question_obj.section
|
|
|
|
system_prompt = f"""Eres un mecánico experto realizando una inspección vehicular.
|
|
|
|
PREGUNTA ESPECÍFICA A RESPONDER: "{question_text}"
|
|
Sección: {section}
|
|
|
|
Analiza la imagen ÚNICAMENTE para responder esta pregunta específica.
|
|
Sé directo y enfócate solo en lo que la pregunta solicita.
|
|
|
|
Responde en formato JSON:
|
|
{{
|
|
"status": "ok|minor|critical",
|
|
"observations": "Respuesta específica a: {question_text}",
|
|
"recommendation": "Acción si aplica",
|
|
"confidence": 0.0-1.0
|
|
}}
|
|
|
|
IMPORTANTE:
|
|
- Responde SOLO lo que la pregunta pide
|
|
- No des información genérica del vehículo
|
|
- Sé específico y técnico
|
|
- Si la pregunta es pass/fail, indica claramente si pasa o falla
|
|
- Si la pregunta es bueno/regular/malo, indica el estado específico del componente"""
|
|
|
|
user_message = f"Inspecciona la imagen y responde específicamente: {question_text}"
|
|
else:
|
|
# Fallback para análisis general
|
|
system_prompt = """Eres un experto mecánico automotriz. Analiza la imagen y proporciona:
|
|
1. Estado del componente (bueno/regular/malo)
|
|
2. Nivel de criticidad (ok/minor/critical)
|
|
3. Observaciones técnicas breves
|
|
4. Recomendación de acción
|
|
|
|
Responde en formato JSON:
|
|
{
|
|
"status": "ok|minor|critical",
|
|
"observations": "descripción técnica",
|
|
"recommendation": "acción sugerida",
|
|
"confidence": 0.0-1.0
|
|
}"""
|
|
user_message = "Analiza este componente del vehículo para la inspección general."
|
|
|
|
if ai_config.provider == "openai":
|
|
import openai
|
|
openai.api_key = ai_config.api_key
|
|
|
|
response = openai.ChatCompletion.create(
|
|
model=ai_config.model_name,
|
|
messages=[
|
|
{"role": "system", "content": system_prompt},
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": user_message
|
|
},
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}
|
|
}
|
|
]
|
|
}
|
|
],
|
|
max_tokens=500
|
|
)
|
|
|
|
ai_response = response.choices[0].message.content
|
|
|
|
elif ai_config.provider == "gemini":
|
|
import google.generativeai as genai
|
|
from PIL import Image
|
|
from io import BytesIO
|
|
|
|
genai.configure(api_key=ai_config.api_key)
|
|
model = genai.GenerativeModel(ai_config.model_name)
|
|
|
|
# Convertir base64 a imagen PIL
|
|
image = Image.open(BytesIO(contents))
|
|
|
|
prompt = f"{system_prompt}\n\n{user_message}"
|
|
response = model.generate_content([prompt, image])
|
|
ai_response = response.text
|
|
|
|
else:
|
|
return {
|
|
"success": False,
|
|
"error": f"Provider {ai_config.provider} no soportado"
|
|
}
|
|
|
|
# Intentar parsear como JSON, si falla, usar texto plano
|
|
try:
|
|
import json
|
|
import re
|
|
|
|
# Limpiar markdown code blocks si existen
|
|
cleaned_response = ai_response.strip()
|
|
|
|
# Remover ```json ... ``` si existe
|
|
if cleaned_response.startswith('```'):
|
|
# Extraer contenido entre ``` markers
|
|
match = re.search(r'```(?:json)?\s*\n?(.*?)\n?```', cleaned_response, re.DOTALL)
|
|
if match:
|
|
cleaned_response = match.group(1).strip()
|
|
|
|
analysis = json.loads(cleaned_response)
|
|
except:
|
|
# Si no es JSON válido, crear estructura básica
|
|
analysis = {
|
|
"status": "ok",
|
|
"observations": ai_response,
|
|
"recommendation": "Revisar manualmente",
|
|
"confidence": 0.7
|
|
}
|
|
|
|
return {
|
|
"success": True,
|
|
"analysis": analysis,
|
|
"raw_response": ai_response,
|
|
"model": ai_config.model_name,
|
|
"provider": ai_config.provider
|
|
}
|
|
|
|
except Exception as e:
|
|
print(f"Error en análisis AI: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return {
|
|
"success": False,
|
|
"error": str(e),
|
|
"message": "Error analyzing image with AI. Please check AI configuration in Settings."
|
|
}
|
|
|
|
try:
|
|
import openai
|
|
openai.api_key = settings.OPENAI_API_KEY
|
|
|
|
# Prompt especializado para inspección vehicular
|
|
system_prompt = """Eres un experto mecánico automotriz. Analiza la imagen y proporciona:
|
|
1. Estado del componente (bueno/regular/malo)
|
|
2. Nivel de criticidad (ok/minor/critical)
|
|
3. Observaciones técnicas breves
|
|
4. Recomendación de acción
|
|
|
|
Responde en formato JSON:
|
|
{
|
|
"status": "ok|minor|critical",
|
|
"observations": "descripción técnica",
|
|
"recommendation": "acción sugerida",
|
|
"confidence": 0.0-1.0
|
|
}"""
|
|
|
|
response = openai.ChatCompletion.create(
|
|
model="gpt-4-vision-preview" if "gpt-4" in str(settings.OPENAI_API_KEY) else "gpt-4o",
|
|
messages=[
|
|
{
|
|
"role": "system",
|
|
"content": system_prompt
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": f"Analiza este componente del vehículo.\n{question_context}"
|
|
},
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": f"data:image/jpeg;base64,{image_b64}"
|
|
}
|
|
}
|
|
]
|
|
}
|
|
],
|
|
max_tokens=500
|
|
)
|
|
|
|
ai_response = response.choices[0].message.content
|
|
|
|
# Intentar parsear como JSON, si falla, usar texto plano
|
|
try:
|
|
import json
|
|
analysis = json.loads(ai_response)
|
|
except:
|
|
# Si no es JSON válido, crear estructura básica
|
|
analysis = {
|
|
"status": "ok",
|
|
"observations": ai_response,
|
|
"recommendation": "Revisar manualmente",
|
|
"confidence": 0.7
|
|
}
|
|
|
|
return {
|
|
"success": True,
|
|
"analysis": analysis,
|
|
"raw_response": ai_response,
|
|
"model": "gpt-4-vision"
|
|
}
|
|
|
|
except Exception as e:
|
|
print(f"Error en análisis AI: {e}")
|
|
return {
|
|
"success": False,
|
|
"error": str(e),
|
|
"message": "Error analyzing image with AI"
|
|
}
|
|
|
|
|
|
# ============= HEALTH CHECK =============
|
|
@app.get("/")
|
|
def root():
|
|
return {"message": "Checklist Inteligente API", "version": "1.0.0", "status": "running"}
|
|
|
|
|
|
@app.get("/health")
|
|
def health_check():
|
|
return {"status": "healthy"}
|