Implement category prediction and clean up fzf

src/predict.py

@@ -5,14 +5,36 @@ from typing import List
 
 def get_categories(transactions: List[Transaction]) -> List[str]:
     categories = set([t.account2 for t in transactions])
-    categories.discard(UNKNOWN_CATEGORY)
+    categories.add(UNKNOWN_CATEGORY)
     return list(categories)
 
 
+def get_sort_categories():
+    def sort_categories(row: str, categories: List[str]):
+        if learn is None:
+            return
+        _, _, probs = learn.predict(row)
+        cat_to_prob = dict(zip(learn.dls.vocab[1],probs.tolist()))
+        categories.sort(key=lambda c: cat_to_prob[c] if c in cat_to_prob else 0.0)
+
+    learn = None
+    try:
+        from fastai.text.all import load_learner
+        learn = load_learner("ldg.pkl")
+    except ModuleNotFoundError:
+        pass
+    except FileNotFoundError:
+        pass
+
+    return sort_categories
+
+
 def add_account2(transactions: List[Transaction]):
     categories = get_categories(transactions)
     unmapped_transactions = filter(lambda t: t.account2 == UNKNOWN_CATEGORY, transactions)
+    sort_categories = get_sort_categories()
     for t in unmapped_transactions:
+        sort_categories(t.row, categories)
         add_account2_interactive(t, categories)