ODSC-39261: fix the link

Author: z7ye

docs/source/user_guide/model_registration/frameworks/automlmodel.rst

@@ -5,7 +5,7 @@ AutoMLModel
 
 .. note::
 
-The ``ads.model.framework.automl_model.AutoMLModel`` class is deprecated. See this :ref:link <_Oralce_AutoMlx>`` for more detailed information.
+The ``ads.model.framework.automl_model.AutoMLModel`` class is deprecated. See this :ref:`link <_Oralce_AutoMlx>` for more detailed information.
 
 To deploy an AutoMlx model, use `GenericModel <../../../ads.model.html#ads.model.generic_model.GenericModel>`__ class.
 
@@ -58,7 +58,7 @@ Example
 Open ``automl_model_artifact/score.py`` and edit the code to instantiate the model class. The edits are highlighted -
 
 .. code-block:: python3
-    :emphasize-lines: 21,30
+    :emphasize-lines: 21,22,23,24,25,26,27,28,29,30,101,102,103,104,105,106,107,108,109,110,171
 
     # score.py 1.0 generated by ADS 2.8.1 on 20230226_214703
     import json
@@ -91,7 +91,6 @@ Open ``automl_model_artifact/score.py`` and edit the code to instantiate the mod
         logger.addHandler(handler)
         automl.init(engine="local", engine_opts={"n_jobs": 1}, logger=logger)
 
-
     @lru_cache(maxsize=10)
     def load_model(model_file_name=model_name):
         """
@@ -162,16 +161,15 @@ Open ``automl_model_artifact/score.py`` and edit the code to instantiate the mod
         json_data = data.get('data', data) if isinstance(data, dict) else data
 
         if task and task == "forecasting":
-            if data_type:
+            try:
                 data_type = data_type.split("'")[1]
-                try:
-                    module, spec = ".".join(data_type.split(".")[:-1]), data_type.split(".")[-1]
-                    lib = importlib.import_module(name=module)
-                    func = getattr(lib, spec)
-                    return pd.DataFrame(index=func(json_data))
-                except:
-                    logging.warning("Cannot autodetect the type of the input data. By default, convert input data to pd.DatetimeIndex and feed the model an empty pandas DataFrame with index as input data. If assumption is not correct, modify the score.py and check with .verify() before saving model with .save().")
-                    return pd.DataFrame(index=pd.DatetimeIndex(json_data))
+                module, spec = ".".join(data_type.split(".")[:-1]), data_type.split(".")[-1]
+                lib = importlib.import_module(name=module)
+                func = getattr(lib, spec)
+                return pd.DataFrame(index=func(json_data))
+            except:
+                logging.warning("Cannot autodetect the type of the model input data. By default, convert input data to pd.DatetimeIndex and feed the model with an empty pandas DataFrame with index as input data. If assumption is not correct, modify the score.py and check with .verify() before saving model with .save().")
+                return pd.DataFrame(index=pd.DatetimeIndex(json_data))
         if "pandas.core.series.Series" in data_type:
             return pd.Series(json_data)
         if "pandas.core.frame.DataFrame" in data_type or isinstance(json_data, str):
@@ -240,7 +238,7 @@ Open ``automl_model_artifact/score.py`` and edit the code to instantiate the mod
         return {'prediction': yhat}
 
 
-Verify score.py changes by running inference locally
+Verify score.py changes by running inference locally.
  .. code-block:: python3
 
     automl_model.verify(X_test.iloc[:2], auto_serialize_data=True)