I need some urgent help. I have created an serving endpoint through below code. The code logs in pyfunc model that internally calls DBRX endpoint. However when i query using below input i always get same error. Can someone please help me with it. Its really urgent.
code to create endpoint:
import mlflow
import pandas as pd
from mlflow.models.signature import infer_signature
import json
# define a custom model
class MyModel(mlflow.pyfunc.PythonModel😞
def predict(self, context, model_input, params=None😞
print('input predict', model_input)
print('type input predict', type(model_input))
print('output predict', self.my_custom_function(model_input, params))
print('type output predict', type(self.my_custom_function(model_input, params)))
return self.my_custom_function(model_input, params)
def my_custom_function(self, model_input, params=None😞
# Convert DataFrame to a list of dictionaries
model_input_list = model_input.to_dict(orient='records')
# Convert list of dictionaries to JSON string
model_input_json = json.dumps(model_input_list)
print(type(model_input_json))
print('model_input_json:',model_input_json)
client = get_deploy_client("databricks")
response = client.predict(
endpoint="openai",
inputs={
"messages": [
{"role": "user", "content": model_input_json},
],
},
)
print('output1',response)
print('type', type(response))
json_string = json.dumps(response, indent=4)
print('json_string',json_string)
print('type json_string',type(json_string))
return pd.DataFrame({'output': [json_string]})
# return pd.DataFrame({'predictions': ['Hi']})
# Convert some_input to a pandas DataFrame
some_input = pd.DataFrame(['how are you'])
# save the model
with mlflow.start_run():
model = MyModel()
input_example = some_input
signature = infer_signature(input_example, model.my_custom_function(input_example))
model_info = mlflow.pyfunc.log_model(
artifact_path="model",
python_model=model,
signature=signature,
input_example=input_example
)
# Register the model
catalog = "aclr_u"
schema = "chrgbck_s"
model_name = "new_pyfunc"
mlflow.set_registry_uri("databricks-uc")
mlflow.register_model(model_info.model_uri, f"{catalog}.{schema}.{model_name}")
print('/n model registered/n')
error: {"error_code": "BAD_REQUEST", "message": "Encountered an unexpected error while evaluating the model. Verify that the input is compatible with the model for inference. Error 'Expecting value: line 1 column 1 (char 0)'", "stack_trace": "Traceback (most recent call last):\n File \"/opt/conda/envs/mlflow-env/lib/python3.10/site-packages/requests/models.py\", line 974, in json\n return complexjson.loads(self.text, **kwargs)\n File \"/opt/conda/envs/mlflow-env/lib/python3.10/json/__init__.py\", line 346, in loads\n return _default_decoder.decode(s)\n File \"/opt/conda/envs/mlflow-env/lib/python3.10/json/decoder.py\", line 337, in decode\n obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n File \"/opt/conda/envs/mlflow-env/lib/python3.10/json/decoder.py\", line 355, in raw_decode\n raise JSONDecodeError(\"Expecting value\", s, err.value) from None\njson.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"/opt/conda/envs/mlflow-env/lib/python3.10/site-packages/mlflowserving/scoring_server/__init__.py\", line 627, in transformation\n (raw_predictions, databricks_output, fs_metrics) = _score_model(\n File \"/opt/conda/envs/mlflow-env/lib/python3.10/site-packages/mlflowserving/scoring_server/__init__.py\", line 368, in _score_model\n prediction, fs_metrics = score_model_maybe_with_fs_metrics(model, data, params)\n File \"/opt/conda/envs/mlflow-env/lib/python3.10/site-packages/mlflowserving/scoring_server/scoring_server_utils.py\", line 595, in score_model_maybe_with_fs_metrics\n return (score_pyfunc_model(model, data, params), None)\n File \"/opt/conda/envs/mlflow-env/lib/python3.10/site-packages/mlflowserving/scoring_server/scoring_server_utils.py\", line 600, in score_pyfunc_model\n return model.predict(data, params=params)\n File \"/opt/conda/envs/mlflow-env/lib/python3.10/site-packages/mlflow/pyfunc/__init__.py\", line 492, in predict\n return _predict()\n File \"/opt/conda/envs/mlflow-env/lib/python3.10/site-packages/mlflow/pyfunc/__init__.py\", line 478, in _predict\n return self._predict_fn(data, params=params)\n File \"/opt/conda/envs/mlflow-env/lib/python3.10/site-packages/mlflow/pyfunc/model.py\", line 469, in predict\n return self.python_model.predict(\n File \"/root/.ipykernel/8563/command-2484865133215772-2987730514\", line 16, in predict\n File \"/root/.ipykernel/8563/command-2484865133215772-2987730514\", line 54, in my_custom_function\n File \"/opt/conda/envs/mlflow-env/lib/python3.10/site-packages/requests/models.py\", line 978, in json\n raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)\nrequests.exceptions.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\n"}
input given to serving UI in databricks:
{
"dataframe_split": {
"data": [
[
"how are you"
]
]
}
}
