Create app.py

app.py

@@ -0,0 +1,48 @@
+from backend import Backend
+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())