Merge branch 'refs/heads/main' into staging

README.md

@@ -95,7 +95,6 @@
 
 - **implicit**: 협업 필터링 기반의 추천 알고리즘
 - **사용자 선호도 분석**: 사용자 행동 데이터를 기반으로 맞춤형 추천 제공
-- [추천 시스템](https://bubble-pick-143.notion.site/89314631a2404d76811c6ab6f550b43c?pvs=4)
 
 ---
 

app/model/prompt/graphic_article/2024-07-10.txt

@@ -19,3 +19,6 @@ Response format:
 	    "body": 매일의 판매액을 한눈에 볼 수 있게 했어요 📈\n매출 데이터는 날짜별로 그룹화했어요 🗓️\n날짜 순서대로 정렬했어요 📅\n그래프에서 각 날짜의 판매액을 확인할 수 있어요 🛒\n이제 일별 매출 트렌드를 알 수 있어요 😊
 	}
 }
+경고: groupby에서 preprocess_groupby 메서드를 사용할 때는 다음과 같이 받아와야 합니다.
+async def preprocess_groupby(self, df, by, agg_func):
+    return df.groupby(by).agg(agg_func).reset_index()

app/router/user_type_router.py

@@ -1,20 +1,18 @@
-from typing import List, Dict
+from typing import Dict, List
 
 from fastapi import APIRouter, Depends
 from groq import BaseModel
 from sqlalchemy.ext.asyncio import AsyncSession
 
-from app.database.repository import model_to_dict
 from app.database.session import get_db_session
-from app.model.user_type import UserType
-from app.recommend.recommend_service import RecommendService, user_id_to_classification_id, \
-    user_type_to_classification_id
+from app.recommend.recommend_service import user_type_to_classification_id
 from app.repository.crawled_article_crud import CrawledArticleRepository
 from app.repository.recommend_crud import RecommendRepository
 from app.service.user_type_service import (
     UserTypeQuestionnaire,
     UserTypeQuestionnaireRequestDTO,
-    UserTypeService, calculate_user_type,
+    UserTypeService,
+    calculate_user_type,
 )
 from app.utils.generic_response import GenericResponseDTO
 
@@ -27,73 +25,61 @@ class ArticleResponseDTO(BaseModel):
     content: str
     pubDate: str
     image: str
+
+
 class UserTypeResponseDTO(BaseModel):
     userType: Dict
     recommendNews: List[ArticleResponseDTO]
 
 
-
 @user_type_router.get(
-    "/user-type/questionnaire",
+    "/usertype/form",
     response_model=GenericResponseDTO[List[UserTypeQuestionnaire]],
 )
 async def get_questionnaire():
     data = await UserTypeService().get_questionnaire()
     return GenericResponseDTO[List[UserTypeQuestionnaire]](
-        data=data, message="Questionnaire retrieved successfully", result=True
+        data=data, message="유형검사 질문지 조회에 성공", result=True
     )
 
 
-@user_type_router.post("/user-type", response_model=GenericResponseDTO[UserTypeResponseDTO])
+@user_type_router.post(
+    "/usertype/calculate", response_model=GenericResponseDTO[UserTypeResponseDTO]
+)
 async def create_user_type_by_answers(
     request: UserTypeQuestionnaireRequestDTO,
     session: AsyncSession = Depends(get_db_session),
 ):
-    recommendNew_ids = []
-    if request.id == None:
-        userType = calculate_user_type(
-            request, UserTypeService().get_questionnaire_data()
-        )
-        recommendNew_ids = await RecommendRepository().get(
-            pk=await user_type_to_classification_id(userType),
-            session=session)
-        recommendNew_ids = recommendNew_ids.recommend_article_ids
-    else :
-        userType = await UserTypeService().create_user_type(request, session)
-        recommendNew_ids = await RecommendRepository().get(
-            pk=await user_id_to_classification_id(user_id=userType.user_id, session=session),
-            session=session)
-        recommendNew_ids = recommendNew_ids.recommend_article_ids
+    userType = calculate_user_type(request, UserTypeService().get_questionnaire_data())
+    recommendNew_ids = await RecommendRepository().get(
+        pk=await user_type_to_classification_id(userType), session=session
+    )
+    recommendNew_ids = recommendNew_ids.recommend_article_ids
     recommendNews = []
 
     for id in recommendNew_ids:
         temp_article = await CrawledArticleRepository().get(pk=id, session=session)
-        recommendNews.append(ArticleResponseDTO(
-            id=id,
-            title=temp_article.simple_title,
-            content=temp_article.simple_content,
-            pubDate=temp_article.published_at.strftime("%Y-%m-%d"),
-            image=temp_article.image_url
-        ))
-    if request.id == None:
-        return GenericResponseDTO[UserTypeResponseDTO](
-            data=UserTypeResponseDTO(
-                userType={'ISSUE_FINDER': userType[0],
-                          'LIFESTYLE_CONSUMER': userType[1],
-                          'ENTERTAINER': userType[2],
-                          'TECH_SPECIALIST': userType[3],
-                          'PROFESSIONALS': userType[4]},
-                recommendNews=recommendNews
-            ), message="user type created successfully", result=True
-        )
-    else:
-        return GenericResponseDTO[UserTypeResponseDTO](
-            data=UserTypeResponseDTO(
-                userType={'ISSUE_FINDER': userType.user_type_issue_finder,
-                          'LIFESTYLE_CONSUMER': userType.user_type_lifestyle_consumer,
-                          'ENTERTAINER': userType.user_type_entertainer,
-                          'TECH_SPECIALIST': userType.user_type_tech_specialist,
-                          'PROFESSIONALS': userType.user_type_professionals},
-                recommendNews=recommendNews
-            ), message="user type created successfully", result=True
+        recommendNews.append(
+            ArticleResponseDTO(
+                id=id,
+                title=temp_article.simple_title,
+                content=temp_article.simple_content,
+                pubDate=temp_article.published_at.strftime("%Y-%m-%d"),
+                image=temp_article.image_url,
+            )
         )
+
+    return GenericResponseDTO[UserTypeResponseDTO](
+        data=UserTypeResponseDTO(
+            userType={
+                "ISSUE_FINDER": userType[0],
+                "LIFESTYLE_CONSUMER": userType[1],
+                "ENTERTAINER": userType[2],
+                "TECH_SPECIALIST": userType[3],
+                "PROFESSIONALS": userType[4],
+            },
+            recommendNews=recommendNews,
+        ),
+        message="유형검사 결과가 성공",
+        result=True,
+    )

app/service/api_visualization_service.py

@@ -2,12 +2,12 @@
 # pylint: disable=R0911
 # pylint: disable=C0206
 # pylint: disable=C0327
+import json
 from enum import Enum
 from typing import List
 
 import pandas as pd
 import plotly.express as px
-import plotly.io as pio
 from fastapi import HTTPException
 from sqlalchemy.ext.asyncio import AsyncSession
 
@@ -316,31 +316,53 @@ async def create_article(
     if not await graph_service.get_api_data(data):
         raise HTTPException(status_code=400, detail="Couldn't get data")
 
+    # 여기가 ai보고 데이터 받아오라고 하는 곳.
     ai_result = await graph_service.graph_info(title=title)
-
-    fig = await graph_service.plot_graph(
-        ai_result["graph_type"],
-        ai_result["x_value"],
-        ai_result["y_value"],
-        ai_result["preprocessing_steps"],
-        **ai_result["kwargs"],
+    # 전처리 완료
+    await graph_service.apply_preprocessing(
+        graph_service.dataset, ai_result["preprocessing_steps"]
     )
 
-    # html 다 만든 것
-    html_str = pio.to_html(fig, full_html=True)
-
-    # 둘 다 html_str은 뱉는 걸로.
-    # return이 두 개입니다.
+    # 그래프 그리는 부분
+    # 근데 이 곳을 제끼고 return 을 달리하라는 거임.
+
+    # fig = await graph_service.plot_graph(
+    #     ai_result["graph_type"],
+    #     ai_result["x_value"],
+    #     ai_result["y_value"],
+    #     ai_result["preprocessing_steps"],
+    #     **ai_result["kwargs"],
+    # )
+    #
+    # # html 다 만든 것
+    # html_str = pio.to_html(fig, full_html=True)
+
+    x = graph_service.dataset[ai_result["x_value"]].tolist()
+    y = graph_service.dataset[ai_result["y_value"]].tolist()
+    graph_type = ai_result["graph_type"]
+    # 우선 무슨 값을 넣을 지 몰라서 hard coding 합니다.
+    mode = "lines+markers"
+    marker = "{color: 'red'}"
+
+    data = [{"x": x, "y": y, "type": graph_type, "mode": mode, "marker": marker}]
+
+    layout = {"title": title}
+
+    chart_config = {
+        "data": data,
+        "layout": layout,
+    }
+
+    json_data = json.dumps(chart_config, ensure_ascii=False)
     if user_input:
-        return html_str, ""
+        # 유저의 통제시 그냥 html만 리턴!
+        return json_data, ""
         # 기사를 만드는 경우는 저장을 한다!
-
     repository = ApiVisualizationService()
-    content = ai_result["article"]["body"]
     await repository.create_article(
         title=title,
-        graph_html=html_str,
-        content=content,
+        graph_html=json.dumps(chart_config),
+        content=ai_result["article"]["body"],
         session=session,
     )
-    return html_str, content
+    return json_data, ai_result["article"]["body"]