text: import logging
  
  from fastapi import APIRouter
  from typing import List 
  from pydantic import BaseModel, Field, validator
  
  log = logging.getLogger(__name__)
  router = APIRouter()
  
  
  class recommendReq(BaseModel):
      """Use this data model to parse the request body JSON."""
  
      rating: float = Field(..., example=4.0)
      flavors: str = Field(..., example=['', ''])
      effects: str = Field(..., example=['', ''])
  
      def to_df(self):
          """Convert pydantic object to pandas dataframe with 1 row."""
          return pd.DataFrame([dict(self)])
  
      @validator('x1')
      def x1_must_be_positive(cls, value):
          """Validate that x1 is a positive number."""
          assert value > 0, f'x1 == {value}, must be > 0'
          return value
  
  
  @router.post('/predict')
  async def predict(item: Item):
      """
      Make random baseline predictions for classification problem 🔮
  
      ### Request Body
      - `x1`: positive float
      - `x2`: integer
      - `x3`: string
  
      ### Response
      - `prediction`: boolean, at random
      - `predict_proba`: float between 0.5 and 1.0, 
      representing the predicted class's probability
  
      Replace the placeholder docstring and fake predictions with your own model.
      """
  
      X_new = item.to_df()
      log.info(X_new)
      y_pred = random.choice([True, False])
      y_pred_proba = random.random() / 2 + 0.5
      return {
          'prediction': y_pred,
          'probability': y_pred_proba
      }
  