[MRG] fix prevewing public dataset errors

mle/agents/advisor.py

@@ -55,7 +55,7 @@ def __init__(self, model, console=None):
  the model (or method), and the evaluation metrics, etc. You should always follow the user's requirements.
  2. You should briefly analyze the user's dataset, and give a summary of the dataset, the dataset input can be
  a public dataset name or a path to a local CSV file. You can use the function `preview_csv_data` to preview
- the CSV file.
+ the CSV file or not if the dataset is a public dataset.
  3. And then you should always use the function `search_arxiv` or `search_papers_with_code` to search the
  state-of-the-art machine learning tasks/models/algorithms that can be used to solve the user's requirements,
  and stay up-to-date with the latest.

mle/cli.py

@@ -40,7 +40,7 @@ def cli():
 
 @cli.command()
 @click.pass_context
-@click.argument('mode', default='general')
+@click.argument('mode', default='baseline')
 @click.option('--model', default=None, help='The model to use for the chat.')
 def start(ctx, mode, model):
  """
@@ -49,14 +49,14 @@ def start(ctx, mode, model):
  if not check_config(console):
  return
 
- if mode == 'general':
+ if mode == 'baseline':
  # Baseline mode
  return workflow.baseline(os.getcwd(), model)
  elif mode == 'report':
  # Report mode
  return ctx.invoke(report, model=model, visualize=False)
  else:
- raise ValueError("Invalid mode. Supported modes: 'general', 'report'.")
+ raise ValueError("Invalid mode. Supported modes: 'baseline', 'report'.")
 
 
 @cli.command()