Collab filtering Movie Predictions

I have searched everywhere and I cannot find a way to get information out of the learner in lesson4-collab.ipynb. How can you recommend movies to users? I have seen some posts that use get_idx but when ever we run this we get the following message. Even when using the MovieLens 100K data.
You’re trying to access an item that isn’t in the training data.
If it was in your original data, it may have been split such that it’s only in the validation set now.

To predict you can do learn.predict() and pass in a row.

Sorry for the late response. I was not getting notified of responses. When we try to use the learn.predict we get different errors depending on which way we add something
learn.predict(1) throws error
KeyError: ‘userId’
learn.predict([1]) throws error
KeyError: ‘userId’
learn.predict(userId[1]) throws error
NameError: name ‘userId’ is not defined

Thanks for your help

Zachary
This is what i get when i try to run the learn.predict(1)
Traceback (most recent call last):
File “C:\Users\ron.byrd\dev\MovieRecommender\lib\site-packages\pandas\core\indexes\base.py”, line 2646, in get_loc
return self._engine.get_loc(key)
File “pandas_libs\index.pyx”, line 111, in pandas._libs.index.IndexEngine.get_loc
File “pandas_libs\index.pyx”, line 135, in pandas._libs.index.IndexEngine.get_loc
File “pandas_libs\index_class_helper.pxi”, line 109, in pandas._libs.index.Int64Engine._check_type
KeyError: ‘userId’

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File “Movie_Recommender.py”, line 32, in
learn.predict(1)
File “C:\Users\ron.byrd\dev\MovieRecommender\lib\site-packages\fastai\basic_train.py”, line 372, in predict
batch = self.data.one_item(item)
File “C:\Users\ron.byrd\dev\MovieRecommender\lib\site-packages\fastai\basic_data.py”, line 181, in one_item
with ds.set_item(item):
File “c:\program files\python37\lib\contextlib.py”, line 112, in enter
return next(self.gen)
File “C:\Users\ron.byrd\dev\MovieRecommender\lib\site-packages\fastai\data_block.py”, line 615, in set_item
self.item = self.x.process_one(item)
File “C:\Users\ron.byrd\dev\MovieRecommender\lib\site-packages\fastai\data_block.py”, line 91, in process_one
for p in self.processor: item = p.process_one(item)
File “C:\Users\ron.byrd\dev\MovieRecommender\lib\site-packages\fastai\collab.py”, line 11, in process_one
res = super().process_one(item)
File “C:\Users\ron.byrd\dev\MovieRecommender\lib\site-packages\fastai\tabular\data.py”, line 44, in process_one
for proc in self.procs: proc(df, test=True)
File “C:\Users\ron.byrd\dev\MovieRecommender\lib\site-packages\fastai\tabular\transform.py”, line 124, in call
func(df)
File “C:\Users\ron.byrd\dev\MovieRecommender\lib\site-packages\fastai\tabular\transform.py”, line 145, in apply_test
df.loc[:,n] = pd.Categorical(df[n], categories=self.categories[n], ordered=True)
File “C:\Users\ron.byrd\dev\MovieRecommender\lib\site-packages\pandas\core\frame.py”, line 2800, in getitem
indexer = self.columns.get_loc(key)
File “C:\Users\ron.byrd\dev\MovieRecommender\lib\site-packages\pandas\core\indexes\range.py”, line 353, in get_loc
return super().get_loc(key, method=method, tolerance=tolerance)
File “C:\Users\ron.byrd\dev\MovieRecommender\lib\site-packages\pandas\core\indexes\base.py”, line 2648, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File “pandas_libs\index.pyx”, line 111, in pandas._libs.index.IndexEngine.get_loc
File “pandas_libs\index.pyx”, line 135, in pandas._libs.index.IndexEngine.get_loc
File “pandas_libs\index_class_helper.pxi”, line 109, in pandas._libs.index.Int64Engine._check_type
KeyError: ‘userId’