初始渲染页面

db_utils.py

@@ -0,0 +1,33 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+from loguru import logger
+from sqlalchemy import Column, Integer, String, create_engine
+from sqlalchemy.orm import declarative_base, sessionmaker
+
+from bak.evolve_config2 import train_data
+from bak.init_data import BatchDataBase
+
+Base = declarative_base()
+# 创建 SQLite 数据库引擎
+engine = create_engine('sqlite:///data.db', echo=False)
+
+
+class BatchData(Base):
+    __tablename__ = 'batch_data'
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    year = Column(Integer)
+    census_batch = Column(String)  # 普查批次
+    id_code = Column(String)
+    precision = Column(String)  # 精度
+    is_train = Column(Integer)
+    is_validation = Column(Integer)
+    ann_file = Column(String)
+    img_prefix = Column(String)
+    filter_empty_gt = Column(Integer)
+    update_cache = Column(Integer)
+
+
+Base.metadata.create_all(engine)
+
+Session = sessionmaker(bind=engine, )
+session = Session()

flush.py

@@ -1,36 +1,11 @@
 #!/usr/bin/env python3
 # -*- coding:utf-8 -*-
 from loguru import logger
-from sqlalchemy import Column, Integer, String, create_engine
-from sqlalchemy.orm import declarative_base, sessionmaker
 
 from bak.evolve_config2 import train_data
 from bak.init_data import BatchDataBase
+from db_utils import BatchData, session
 
-Base = declarative_base()
-# 创建 SQLite 数据库引擎
-engine = create_engine('sqlite:///data.db', echo=False)
-
-
-class BatchData(Base):
-    __tablename__ = 'batch_data'
-    id = Column(Integer, primary_key=True, autoincrement=True)
-    year = Column(Integer)
-    census_batch = Column(String)  # 普查批次
-    id_code = Column(String)
-    precision = Column(String)  # 精度
-    is_train = Column(Integer)
-    is_validation = Column(Integer)
-    ann_file = Column(String)
-    img_prefix = Column(String)
-    filter_empty_gt = Column(Integer)
-    update_cache = Column(Integer)
-
-
-Base.metadata.create_all(engine)
-
-Session = sessionmaker(bind=engine, )
-session = Session()
 logger.debug(f"{len(train_data)=}")
 
 census_batches = []

main.py

@@ -1,22 +1,13 @@
 # streamlit_app.py
-
+import pandas as pd
 import streamlit as st
 
-# Create the SQL connection to pets_db as specified in your secrets file.
+from bak.init_data import BatchDataRead
-conn = st.connection('data_db', type='sql')
+from db_utils import BatchData, session
 
-# Insert some data with conn.session.
+df = pd.DataFrame(
-with conn.session as s:
+    data=(BatchDataRead.from_orm(db_obj).dict()
-    s.execute('CREATE TABLE IF NOT EXISTS pet_owners (person TEXT, pet TEXT);')
+          for db_obj in session.query(BatchData).all())
-    s.execute('DELETE FROM pet_owners;')
+)
-    pet_owners = {'jerry': 'fish', 'barbara': 'cat', 'alex': 'puppy'}
+#
-    for k in pet_owners:
+st.dataframe(df, use_container_width=True)
-        s.execute(
-            'INSERT INTO pet_owners (person, pet) VALUES (:owner, :pet);',
-            params=dict(owner=k, pet=pet_owners[k])
-        )
-    s.commit()
-
-# Query and display the data you inserted
-pet_owners = conn.query('select * from pet_owners')
-st.dataframe(pet_owners)

requirements.txt

@@ -1,4 +1,5 @@
 sqlalchemy~=1.4
 streamlit~=1.29
 pydantic~=1.0
-loguru~=0.7
+loguru~=0.7
+pandas~=2.2.1