How to consume single piece of text for classification in a model?
You have to repeat the same preprocessing steps for prediction as you did during training. Let's say you trained like this:

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=777)

vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(X_train)

mod = LinearSVC()
mod.fit(X_train, y_train)


Then you need to use that fit TfidfVectorizer to transform your test prediction as well (which you seem to have managed). Like this:

X_test = vectorizer.transform(X_test)
pr = mod.predict(X_test)


And the same goes if you just want to feed in a manual text:

X_manual = vectorizer.transform(['fresh fish'])
pr = mod.predict(X_manual)