Call to pipeline.predict() fails

System Info

• transformers version: 4.21.1
• Platform: macOS-12.5.1-arm64-arm-64bit
• Python version: 3.9.12
• Huggingface_hub version: 0.2.1
• PyTorch version (GPU?): 1.12.1 (False)
• Tensorflow version (GPU?): 2.9.2 (False)
• Flax version (CPU?/GPU?/TPU?): not installed (NA)
• Jax version: not installed
• JaxLib version: not installed
• Using GPU in script?: No
• Using distributed or parallel set-up in script?: No

Who can help?

@narsil

Information

• The official example scripts
• My own modified scripts

Tasks

• An officially supported task in the examples folder (such as GLUE/SQuAD, …)
• My own task or dataset (give details below)

Reproduction

Execute the following piece of code resulted in an exception that is pasted below.

from transformers import pipeline

pipe = pipeline("text-classification")
print(pipe.predict(["This restaurant is awesome"]))

Exception:

Traceback (most recent call last):
  File "pipeline_test.py", line 5, in <module>
    print(pipe.predict(["This restaurant is awesome"]))
  File "miniconda3/envs/mlflow-py3.9/lib/python3.9/site-packages/transformers/pipelines/base.py", line 840, in predict
    return self(X=X)
  File "miniconda3/envs/mlflow-py3.9/lib/python3.9/site-packages/transformers/pipelines/text_classification.py", line 138, in __call__
    result = super().__call__(*args, **kwargs)
TypeError: __call__() missing 1 required positional argument: 'inputs'

Expected behavior

Successful predictions as shown below

[{'label': 'POSITIVE', 'score': 0.9998743534088135}]

Proposed fix

I dig a bit deeper into the implementation based on the exception and found out that this change fixes the issue. If this indeed a fix, I am happy to create a PR.

[Narsil]

I will go ahead and create a PR. Prior to that I will run all the tests to make sure, the fix does not introduce any breaking changes. Let us see how it rolls 😃

Thanks ! Again as I said, this is very old code maybe it broke a long time ago already and your PR is good (and we probably need to add a test to make sure we don’t break again).

[s-udhaya]

Hi @Narsil ,

If that is the core of the issue (which sounds odd) can’t you just wrap the pipeline in another dummy object that just uses predict to use __call__ ?

This is exactly what happens in Pipeline’s predict method. I would rather prefer to fix the issue at the source than creating a workaround.

The problem is that because the purpose of this code as since been lost, we would be very hesitant to change it because of our commitment to never make breaking changes.

I understand your concern regarding breaking changes. however I believe, this change is not going to be a breaking change, and the predict function is not usable in its current state.

I will go ahead and create a PR. Prior to that I will run all the tests to make sure, the fix does not introduce any breaking changes. Let us see how it rolls 😃

Thanks for the response.