Create apps

app.py

@@ -1,48 +1,8 @@
-from backend import Backend
+from ambiguous_search_backends import SBAmbiguousSearchBackend
+from classic_search_backends import ImplClassicSearchBackend
 import frontend
-import numpy as np
-from pandas import DataFrame
-from transformers import BertJapaneseTokenizer, BertModel
-import pickle
 
-with open("./himitsudogu_db.pkl", "rb") as file:
-    himitsudogu_db: dict = pickle.load(file)
-
-
-class HFBackend(Backend):
-    def __init__(self):
-        super().__init__()
-        self.feature_matrix = himitsudogu_db["feature_matrix_s"][
-            "sonoisa/sentence-bert-base-ja-mean-tokens-v2"
-        ]
-        # モデルsonoisa/sentence-bert-base-ja-mean-tokens-v2を使用する
-        self.tokenizer = BertJapaneseTokenizer.from_pretrained(
-            "sonoisa/sentence-bert-base-ja-mean-tokens-v2"
-        )
-        self.model = BertModel.from_pretrained(
-            "sonoisa/sentence-bert-base-ja-mean-tokens-v2"
-        )
-
-    def on_submit_button_press(self, query: str) -> DataFrame:
-        # 文章を形態素解析し、形態素ID列へ変換
-        tokenized = self.tokenizer(query, return_tensors="pt")
-        # 言語モデルへ形態素ID列を代入
-        output = self.model(**tokenized)
-        # 文章の特徴ベクトルを取得
-        pooler_output = output["pooler_output"]
-        query_feature_vector = pooler_output[0].detach().numpy()
-        # 各ひみつ道具の説明文の特徴ベクトルとの内積を取る
-        cs_s = self.feature_matrix @ query_feature_vector
-        # 内積が大きかったもの順にひみつ道具を表示するようにする
-        ranked_index_s = np.argsort(cs_s)[::-1]
-        output = DataFrame(columns=["類似度", "名前", "説明"])
-        for rank, i in enumerate(ranked_index_s[:20], 1):
-            output.loc[rank] = [
-                cs_s[i],
-                himitsudogu_db["name_s"][i],
-                himitsudogu_db["description_s"][i],
-            ]
-        return output
-
-
-frontend.launch_frontend(backend=HFBackend())
+frontend.launch_frontend(
+    ambiguous_search_backend=SBAmbiguousSearchBackend(),
+    classic_search_backend=ImplClassicSearchBackend(),
+)

nobert_app.py

@@ -0,0 +1,8 @@
+from ambiguous_search_backends import DummyAmbiguousSearchBackend
+from classic_search_backends import ImplClassicSearchBackend
+import frontend
+
+frontend.launch_frontend(
+    ambiguous_search_backend=DummyAmbiguousSearchBackend(),
+    classic_search_backend=ImplClassicSearchBackend(),
+)