Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+
+title = "🍀고민 해결 도서 추천 챗봇🍀"
+description = "고민이 무엇인가요? 고민 해결을 도와줄 책을 추천해드립니다"
+examples = [["요즘 잠이 안 온다"]]
+
+
+model = SentenceTransformer('jhgan/ko-sroberta-multitask')
+
+df = pd.read_pickle('BookData_emb.pkl')
+df_emb = df[['서평임베딩']].copy()
+
+
+def recommend(message):
+  embedding = model.encode(message)
+  df_emb['거리'] = df_emb['서평임베딩'].map(lambda x: cosine_similarity([embedding], [x]).squeeze())
+  answer = df.loc[df_emb['거리'].idxmax()]
+  Book_title = answer['제목']
+  Book_author = answer['작가']
+  Book_publisher = answer['출판사']
+  Book_comment = answer['서평']
+  return Book_title
+
+gr.ChatInterface(
+        fn=recommend,
+        textbox=gr.Textbox(placeholder="말걸어주세요..", container=False, scale=7),
+        title="어떤 챗봇을 원하심미까?",
+        description="물어보면 답하는 챗봇임미다.",
+        theme="soft",
+        examples=[["안뇽"], ["요즘 덥다 ㅠㅠ"], ["점심메뉴 추천바람, 짜장 짬뽕 택 1"]],
+        retry_btn="다시보내기 ↩",
+        undo_btn="이전챗 삭제 ❌",
+        clear_btn="전챗 삭제 💫").launch()