[Python] How do I run a program that uses gradio on an HPC cluster

I want to run a program called protein generator locally. I downloaded the repo from huggingface and it uses gradio but I want to be able to submit jobs without having to navigate the UI. I also want a consistent version for the sake of reproducibility.

I have been using get_pssm, which is the function I would like to use takes two files on the UI (button labeled Generated PSSM).

I always run:

python app.py &
python
from gradio_client import Client
client = Client("http://127.0.0.1:7860/")


This is what I have tried:

1.

client.predict("../msa.fa", "../pssm.txt" ,api_name="/get_pssm")


this gives me: Traceback (most recent call last): File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio/queueing.py", line 536, in process_events response = await route_utils.call_process_api( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio/route_utils.py", line 322, in call_process_api output = await app.get_blocks().process_api( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio/blocks.py", line 1931, in process_api inputs = await self.preprocess_data( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio/blocks.py", line 1659, in preprocess_data inputs_cached = block.data_model(**inputs_cached) # type: ignore TypeError: ModelMetaclass object argument after ** must be a mapping, not str Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio_client/client.py", line 466, in predict return self.submit( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio_client/client.py", line 1499, in result return super().result(timeout=timeout) File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/concurrent/futures/_base.py", line 444, in result return self.__get_result() File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/concurrent/futures/_base.py", line 389, in __get_result raise self._exception File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/concurrent/futures/thread.py", line 57, in run result = self.fn(*self.args, **self.kwargs) File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio_client/client.py", line 1121, in _inner predictions = _predict(*data) File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio_client/client.py", line 1233, in _predict raise AppError( gradio_client.exceptions.AppError: The upstream Gradio app has raised an exception but has not enabled verbose error reporting. To enable, set show_error=True in launch().

2.

client.predict({"fasta_msa": "../msa.fa"}, {"input_pssm": "../pssm.txt"} ,api_name="/get_pssm")


and got Traceback (most recent call last): File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio/queueing.py", line 536, in process_events response = await route_utils.call_process_api( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio/route_utils.py", line 322, in call_process_api output = await app.get_blocks().process_api( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio/blocks.py", line 1931, in process_api inputs = await self.preprocess_data( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio/blocks.py", line 1659, in preprocess_data inputs_cached = block.data_model(**inputs_cached) # type: ignore File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/pydantic/main.py", line 214, in __init__ validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self) pydantic_core._pydantic_core.ValidationError: 1 validation error for FileData path Field required [type=missing, input_value={'fasta_msa': '../32_blaNDM_seqs/aligned_32.fa'}, input_type=dict] For further information visit https://errors.pydantic.dev/2.10/v/missing Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio_client/client.py", line 466, in predict return self.submit( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio_client/client.py", line 1499, in result return super().result(timeout=timeout) File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/concurrent/futures/_base.py", line 444, in result return self.__get_result() File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/concurrent/futures/_base.py", line 389, in __get_result raise self._exception File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/concurrent/futures/thread.py", line 57, in run result = self.fn(*self.args, **self.kwargs) File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio_client/client.py", line 1121, in _inner predictions = _predict(*data) File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio_client/client.py", line 1238, in _predict raise AppError( gradio_client.exceptions.AppError: The upstream Gradio app has raised an exception: 1 validation error for FileData path Field required [type=missing, input_value={'fasta_msa': '../32_blaNDM_seqs/aligned_32.fa'}, input_type=dict] For further information visit https://errors.pydantic.dev/2.10/v/missing

3.

with open("../msa.fa", "rb") as msa, open("../pssm.txt", "rb") as pssm:
client.predict(msa, pssm, api_name="/get_pssm")


and got Traceback (most recent call last): File "<stdin>", line 2, in <module> File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio_client/client.py", line 466, in predict return self.submit( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio_client/client.py", line 1499, in result return super().result(timeout=timeout) File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/concurrent/futures/_base.py", line 444, in result return self.__get_result() File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/concurrent/futures/_base.py", line 389, in __get_result raise self._exception File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/concurrent/futures/thread.py", line 57, in run result = self.fn(*self.args, **self.kwargs) File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio_client/client.py", line 1121, in _inner predictions = _predict(*data) File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio_client/client.py", line 1223, in _predict event_id = self.client.send_data(data, hash_data, self.protocol) File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/gradio_client/client.py", line 289, in send_data req = httpx.post( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/httpx/_api.py", line 304, in post return request( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/httpx/_api.py", line 109, in request return client.request( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/httpx/_client.py", line 812, in request request = self.build_request( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/httpx/_client.py", line 378, in build_request return Request( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/httpx/_models.py", line 408, in __init__ headers, stream = encode_request( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/httpx/_content.py", line 216, in encode_request return encode_json(json) File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/site-packages/httpx/_content.py", line 177, in encode_json body = json_dumps( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/json/__init__.py", line 234, in dumps return cls( File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/json/encoder.py", line 199, in encode chunks = self.iterencode(o, _one_shot=True) File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/json/encoder.py", line 257, in iterencode return _iterencode(o, 0) File "/users/aflemis1/.conda/envs/proteingenerator/lib/python3.8/json/encoder.py", line 179, in default raise TypeError(f'Object of type {o.__class__.__name__} ' TypeError: Object of type BufferedReader is not JSON serializable

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