class_docstr: This integration seamlessly combines MindsDB and Leonardo AI to create a powerful
  AI-driven solution for creative content generation.
  
  Content Generation with Leonardo AI: Harness the power of advanced generative
  models for creative content production. From realistic images to artistic text, Leonardo
  AI opens up new possibilities for content creators.
class_docstr_tok: ["This","integration","seamlessly","combines","MindsDB","and","Leonardo","AI","to","create","a","powerful","AI-driven","solution","for","creative","content","generation",".","Content","Generation","with","Leonardo","AI",":","Harness","the","power","of","advanced","generative","models","for","creative","content","production",".","From","realistic","images","to","artistic","text",",","Leonardo","AI","opens","up","new","possibilities","for","content","creators","."]
code: def predict(
      self, df: pd.DataFrame, args: Optional[Dict] = None, **kwargs
  ) -> pd.DataFrame:
      pred_args = args["predict_params"] if args else {}
      args = self.model_storage.json_get("args")
  
      prompt_template = pred_args.get(
          "prompt_template",
          args.get(
              "prompt_template", "Generate a picture of {{{{text}}}}"
          ),
      )
  
      # prepare prompts
      prompts, empty_prompt_id = get_completed_prompts(
          prompt_template, df
      )
      df["__mdb_prompt"] = prompts
  
      # generate picture based on the given prompt
      result_df = pd.DataFrame(self.predict_answer(prompts))
      return result_df
  
file_path: mindsdb/mindsdb/integrations/handlers/leonardoai_handler/leonardo_ai_handler.py
imports: import os
  import requests
  import time
  from typing import Dict, Optional
  from mindsdb.integrations.libs.llm_utils import get_completed_prompts
  import pandas
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.utilities import log
  from mindsdb.interfaces.storage.model_fs import HandlerStorage
  from mindsdb.utilities.config import Config
label_desc: Use image node_id 3 for calling the LeonardoAIHandler obj's underlying member method code with example usage: obj.predict(df, args) and returns: result_df