docstr: Using replicate makes the prediction according to your parameters
  
  Args:
      df (pd.DataFrame): The input DataFrame containing data to predict.
      args (Optional[Dict]): Additional arguments for prediction parameters.
  
  Returns:
      pd.DataFrame: The DataFrame containing the predicted results.
docstr_tok: ["Using","replicate","makes","the","prediction","according","to","your","parameters","Args",":","df","(","pd.DataFrame",")",":","The","input","DataFrame","containing","data","to","predict",".","args","(","Optional","[","Dict","]",")",":","Additional","arguments","for","prediction","parameters",".","Returns",":","pd.DataFrame",":","The","DataFrame","containing","the","predicted","results","."]
code: def predict(
      self, df: pd.DataFrame, args: Optional[Dict] = None
  ) -> pd.DataFrame:
      """Using replicate makes the prediction according to your parameters
  
      Args:
          df (pd.DataFrame): The input DataFrame containing data to predict.
          args (Optional[Dict]): Additional arguments for prediction parameters.
  
      Returns:
          pd.DataFrame: The DataFrame containing the predicted results.
      """
  
      # Extracting prediction parameters from input arguments
      pred_args = args["predict_params"] if args else {}
      args = self.model_storage.json_get("args")
      model_name, version, target_col = (
          args["model_name"],
          args["version"],
          args["target"],
      )
  
      # Check if '__mindsdb_row_id' column exists and drop it if present
      if "__mindsdb_row_id" in df.columns:
          df.drop(columns=["__mindsdb_row_id"], inplace=True)
  
      def get_data(conditions):
          # Run prediction using MindsDB's replicate library
          output = replicate.run(
              f"{args['model_name']}:{args['version']}",
              input={
                  **conditions.to_dict(),
                  **pred_args,
              },  # Unpacking parameters inputted
          )
          # Process output based on the model type
          if (
              isinstance(output, types.GeneratorType)
              and args.get("model_type") == "LLM"
          ):
              output = "".join(
                  list(output)
              )  # If model_type is LLM, make the stream a string
          elif isinstance(output, types.GeneratorType):
              output = list(output)[
                  -1
              ]  # Getting the final URL if output is a generator of frames URL
          elif isinstance(output, list) and len(output) > 0:
              output = output[
                  -1
              ]  # Returns generated image for controlNet models as it outputs filter and generated image
          return output
  
      # Check if any wrong parameters are given and raise an exception if necessary
      params_names = set(df.columns) | set(pred_args)
      available_params = self._get_schema(only_keys=True)
      wrong_params = []
      for i in params_names:
          if i not in available_params:
              wrong_params.append(i)
  
      if wrong_params:
          raise Exception(
              f"""'{wrong_params}' is/are not supported parameter for this model.
  Use DESCRIBE PREDICTOR mindsdb.<model_name>.features; to know about available parameters. OR 
  Visit https://replicate.com/{model_name}/versions/{version} to check parameters."""
          )
  
      # Set the Replicate API token for communication with the server
      replicate.default_client.api_token = self._get_replicate_api_key(
          args
      )
  
      # Run prediction on the DataFrame rows and format the results into a DataFrame
      data = df.apply(get_data, axis=1)
      data = pd.DataFrame(data)
      data.columns = [target_col]
  
      return data
  
file_path: mindsdb/mindsdb/integrations/handlers/replicate_handler/replicate_handler.py
imports: import replicate
  import pandas
  from mindsdb.integrations.libs.base import BaseMLEngine
  from typing import Dict, Optional
  import os
  import types
  from mindsdb.utilities.config import Config
label_desc: Use image node_id 3 for calling the ReplicateHandler obj's underlying member method code with example usage: obj.predict(df, args) and returns: data, output