Update workflow/dataloading/dataloading_render.py

workflow/dataloading/dataloading_render.py

@@ -1,210 +1,210 @@
-import os
-from typing import List, Optional
-
-import pandas as pd
-import streamlit as st
-import streamlit_antd_components as sac
-
-from workflow.dataloading.dataloading_core import process_complex_data, load_from_path, load_concat_file, PathFileWrapper
-
-
-def loading_data_file(agent):
-
-    st.info(
-        "💡 提示：\n"
-        "1. 支持一次上传多个数据文件\n"
-        "2. 自动使用大模型分析并处理数据\n"
-        "3. 支持多种格式的文件类型上传\n"
-    )
-
-    selected_index = sac.tabs([
-        sac.TabsItem(label='本地上传'),
-        sac.TabsItem(label='路径导入'),
-    ], color='#5980AE',)
-
-    if selected_index == "本地上传":
-        # 点击上传文件
-        uploaded_files = st.file_uploader(
-            "选择新文件",
-            accept_multiple_files=True,
-            help="拖拽或点击上传多个文件",
-        )
-
-        if uploaded_files:
-            current_memory_file_name = agent.load_file_name()
-            new_files = [f for f in uploaded_files if f.name not in current_memory_file_name]
-            if new_files:
-                try:
-                    with st.spinner("正在处理数据..."):
-                        df, dfs = process_complex_data(new_files, agent)
-                    if df is not None:
-                        agent.add_df(df)
-                        agent.save_dfs(dfs)
-                        for f in new_files:
-                            agent.save_file_name(f.name)
-                        st.rerun()
-                except Exception as err:
-                    st.error(f"导入失败：{err}")
-
-    elif selected_index == "路径导入":
-        # 路径上传文件
-        raw_paths = st.text_area(
-            "从路径导入数据 (每行一个文件路径)",
-            placeholder=    "C:\\data\\iris.names\nC:\\data\\iris.data",
-            height=100
-        )
-
-        if st.button("从路径加载文件", use_container_width=True):
-            if raw_paths:
-
-                path_list = [p.strip().strip("'\"") for p in raw_paths.strip().split('\n') if p.strip()]
-                
-                valid_paths = [p for p in path_list if os.path.exists(p)]
-                invalid_paths = [p for p in path_list if not os.path.exists(p)]
-
-                if invalid_paths:
-                    st.warning(f"路径不存在，已跳过：\n- " + "\n- ".join(invalid_paths))
-
-                if not valid_paths:
-                    st.error("未找到任何有效的本地文件路径。")
-                else:
-                    current_memory_file_name = agent.load_file_name()
-                    new_paths = [p for p in valid_paths if p not in current_memory_file_name]
-
-                    if not new_paths:
-                        st.info("所有指定的路径文件均已加载。")
-                    else:
-                        files_to_process = [PathFileWrapper(p) for p in new_paths]
-                        try:
-                            with st.spinner("正在处理数据..."):
-                                df, dfs = process_complex_data(files_to_process, agent)
-                            if df is not None:
-                                agent.add_df(df)
-                                agent.save_dfs(dfs)
-                                for p in new_paths:
-                                    agent.save_file_name(p)
-                                st.rerun()
-                        except Exception as err:
-                            st.error(f"本地文件读取失败：{err}")
-    
-    dfs = agent.load_dfs()
-    if dfs is not None and len(dfs) >= 2:
-        load_concat_file(dfs, agent)
-
-
-def loading_basic_info(agent):
-    
-    df = agent.load_df()
-    if df is not None:
-        r, c = df.shape
-        missing = int(df.isnull().sum().sum())
-        col1, col2, col3 = st.columns(3)
-        col1.metric("行数", r)
-        col2.metric("列数", c)
-        col3.metric("缺失值总数", missing)
-
-        dtype_info = pd.DataFrame({
-            "列名": df.columns,
-            "类型": df.dtypes.astype(str),
-            "非空": df.count().values,
-            "缺失%": (df.isnull().mean() * 100).round(2).values,
-        }).reset_index(drop=True)
-
-        selected_index = sac.tabs([
-            sac.TabsItem(label='数据类型概览'),
-            sac.TabsItem(label='数据预览'),
-        ],color='#5980AE',)
-
-        if selected_index == "数据类型概览":
-            st.dataframe(dtype_info, use_container_width=True)
-        elif selected_index == "数据预览":
-            if st.button("🎲 随机抽样"):
-                display_df = df.sample(10)
-                st.dataframe(display_df, use_container_width=True)
-            else:
-                st.dataframe(df.head(10), use_container_width=True)
-
-
-def loading_chat(agent, auto=False) -> None:
-
-    df = agent.load_df()
-    if df is None:
-        return
-
-    with st.chat_message("assistant"):
-        st.write(
-            "我是 Autostat 数据分析助手，很高兴为您服务！\n\n"
-            "请先上传您的数据文件，上传完成后，您可以在下方和我对话，也可以直接点击按钮解析数据含义。"
-        )
-        analyze_btn = st.button("🔍 解析含义")
-        result_placeholder = st.empty()
-        
-    # 渲染历史对话
-    chat_history = agent.load_memory()
-
-    for idx, entry in enumerate(chat_history):
-        bubble = st.chat_message(entry["role"])
-        content = entry["content"]
-        if isinstance(content, str):
-            bubble.write(content)
-
-    already_generated = any(
-        entry["role"] == "assistant" and "含义" in str(entry["content"])
-        for entry in chat_history
-    )
-
-    if analyze_btn or (auto and not already_generated):
-        st.chat_message("user").write("请帮我解析数据含义")
-        agent.add_memory({"role": "user", "content": "请帮我解析数据含义"})
-        with st.spinner("分析中..."):
-            desc = agent.do_data_description(df)
-
-        agent.finish_auto()
-        st.chat_message("assistant").write(desc)
-        agent.add_memory({"role": "assistant", "content": desc})
-        st.rerun()
-
-    # 用户自定义输入
-    user_input = st.chat_input("请输入需求，例如“帮我分析xx列”")
-    if user_input:
-        st.chat_message("user").write(user_input)
-        agent.add_memory({"role": "user", "content": user_input})
-        with st.spinner("处理中…"):
-            reply = agent.do_data_description(df, user_input)
-
-        st.chat_message("assistant").write(reply)
-        agent.add_memory({"role": "assistant", "content": reply})
-        st.rerun()
-
-
-if __name__ == "__main__":
-
-    agent = st.session_state.data_loading_agent
-    planner = st.session_state.planner_agent
-    auto = planner.loading_auto
-
-    if st.session_state.auto_mode == True:
-        if (agent.finish_auto_task == True and planner.switched_prep == False) or planner.loading_auto == False:
-            planner.finish_loading_auto()
-            st.switch_page("workflow/preprocessing/preprocessing_render.py")
-
-    c1,c2 = st.columns(2)
-    with c1:
-        st.title("数据导入")
-    with c2:
-        st.write("")  
-        st.write("")  
-        sac.buttons([
-            sac.ButtonsItem(label='Github', icon='github', href='https://github.com/ElvisWang1111/AAAAAnystat'),
-            sac.ButtonsItem(label='Doc', icon=sac.BsIcon(name='bi bi-file-earmark-post-fill', size=16), href='https://elviswang1111.github.io/anystatweb.github.io/index.html'),
-        ], align='end', color='dark', variant='filled', index=None)
-    st.markdown("---")
-
-    c = st.columns(2)
-    with c[0].expander('数据上传', True):
-        loading_data_file(agent)
-    with c[1].expander('数据建议', True):
-        loading_chat(agent, auto)
-    with c[0].expander('数据展示', True):
-        loading_basic_info(agent)
-
+import os
+from typing import List, Optional
+
+import pandas as pd
+import streamlit as st
+import streamlit_antd_components as sac
+
+from workflow.dataloading.dataloading_core import process_complex_data, load_from_path, load_concat_file, PathFileWrapper
+
+
+def loading_data_file(agent):
+
+    st.info(
+        "💡 提示：\n"
+        "1. 支持一次上传多个数据文件\n"
+        "2. 自动使用大模型分析并处理数据\n"
+        "3. 支持多种格式的文件类型上传\n"
+    )
+
+    selected_index = sac.tabs([
+        sac.TabsItem(label='本地上传'),
+        sac.TabsItem(label='路径导入'),
+    ], color='#5980AE',)
+
+    if selected_index == "本地上传":
+        # 点击上传文件
+        uploaded_files = st.file_uploader(
+            "选择新文件",
+            accept_multiple_files=True,
+            help="拖拽或点击上传多个文件",
+        )
+
+        if uploaded_files:
+            current_memory_file_name = agent.load_file_name()
+            new_files = [f for f in uploaded_files if f.name not in current_memory_file_name]
+            if new_files:
+                try:
+                    with st.spinner("正在处理数据..."):
+                        df, dfs = process_complex_data(new_files, agent)
+                    if df is not None:
+                        agent.add_df(df)
+                        agent.save_dfs(dfs)
+                        for f in new_files:
+                            agent.save_file_name(f.name)
+                        st.rerun()
+                except Exception as err:
+                    st.error(f"导入失败：{err}")
+
+    elif selected_index == "路径导入":
+        # 路径上传文件
+        raw_paths = st.text_area(
+            "从路径导入数据 (每行一个文件路径)",
+            placeholder=    "C:\\data\\iris.names\nC:\\data\\iris.data",
+            height=100
+        )
+
+        if st.button("从路径加载文件", use_container_width=True):
+            if raw_paths:
+
+                path_list = [p.strip().strip("'\"") for p in raw_paths.strip().split('\n') if p.strip()]
+                
+                valid_paths = [p for p in path_list if os.path.exists(p)]
+                invalid_paths = [p for p in path_list if not os.path.exists(p)]
+
+                if invalid_paths:
+                    st.warning(f"路径不存在，已跳过：\n- " + "\n- ".join(invalid_paths))
+
+                if not valid_paths:
+                    st.error("未找到任何有效的本地文件路径。")
+                else:
+                    current_memory_file_name = agent.load_file_name()
+                    new_paths = [p for p in valid_paths if p not in current_memory_file_name]
+
+                    if not new_paths:
+                        st.info("所有指定的路径文件均已加载。")
+                    else:
+                        files_to_process = [PathFileWrapper(p) for p in new_paths]
+                        try:
+                            with st.spinner("正在处理数据..."):
+                                df, dfs = process_complex_data(files_to_process, agent)
+                            if df is not None:
+                                agent.add_df(df)
+                                agent.save_dfs(dfs)
+                                for p in new_paths:
+                                    agent.save_file_name(p)
+                                st.rerun()
+                        except Exception as err:
+                            st.error(f"本地文件读取失败：{err}")
+    
+    dfs = agent.load_dfs()
+    if dfs is not None and len(dfs) >= 2:
+        load_concat_file(dfs, agent)
+
+
+def loading_basic_info(agent):
+    
+    df = agent.load_df()
+    if df is not None:
+        r, c = df.shape
+        missing = int(df.isnull().sum().sum())
+        col1, col2, col3 = st.columns(3)
+        col1.metric("行数", r)
+        col2.metric("列数", c)
+        col3.metric("缺失值总数", missing)
+
+        dtype_info = pd.DataFrame({
+            "列名": df.columns,
+            "类型": df.dtypes.astype(str),
+            "非空": df.count().values,
+            "缺失%": (df.isnull().mean() * 100).round(2).values,
+        }).reset_index(drop=True)
+
+        selected_index = sac.tabs([
+            sac.TabsItem(label='数据类型概览'),
+            sac.TabsItem(label='数据预览'),
+        ],color='#5980AE',)
+
+        if selected_index == "数据类型概览":
+            st.dataframe(dtype_info, use_container_width=True)
+        elif selected_index == "数据预览":
+            if st.button("🎲 随机抽样"):
+                display_df = df.sample(10)
+                st.dataframe(display_df, use_container_width=True)
+            else:
+                st.dataframe(df.head(10), use_container_width=True)
+
+
+def loading_chat(agent, auto=False) -> None:
+
+    df = agent.load_df()
+    if df is None:
+        return
+
+    with st.chat_message("assistant"):
+        st.write(
+            "我是 Autostat 数据分析助手，很高兴为您服务！\n\n"
+            "请先上传您的数据文件，上传完成后，您可以在下方和我对话，也可以直接点击按钮解析数据含义。"
+        )
+        analyze_btn = st.button("🔍 解析含义")
+        result_placeholder = st.empty()
+        
+    # 渲染历史对话
+    chat_history = agent.load_memory()
+
+    for idx, entry in enumerate(chat_history):
+        bubble = st.chat_message(entry["role"])
+        content = entry["content"]
+        if isinstance(content, str):
+            bubble.write(content)
+
+    already_generated = any(
+        entry["role"] == "assistant" and "含义" in str(entry["content"])
+        for entry in chat_history
+    )
+
+    if analyze_btn or (auto and not already_generated):
+        st.chat_message("user").write("请帮我解析数据含义")
+        agent.add_memory({"role": "user", "content": "请帮我解析数据含义"})
+        with st.spinner("分析中..."):
+            desc = agent.do_data_description(df)
+
+        agent.finish_auto()
+        st.chat_message("assistant").write(desc)
+        agent.add_memory({"role": "assistant", "content": desc})
+        st.rerun()
+
+    # 用户自定义输入
+    user_input = st.chat_input("请输入需求，例如“帮我分析xx列”")
+    if user_input:
+        st.chat_message("user").write(user_input)
+        agent.add_memory({"role": "user", "content": user_input})
+        with st.spinner("处理中…"):
+            reply = agent.do_data_description(df, user_input)
+
+        st.chat_message("assistant").write(reply)
+        agent.add_memory({"role": "assistant", "content": reply})
+        st.rerun()
+
+
+if __name__ == "__main__":
+
+    agent = st.session_state.data_loading_agent
+    planner = st.session_state.planner_agent
+    auto = planner.loading_auto
+
+    if st.session_state.auto_mode == True:
+        if (agent.finish_auto_task == True and planner.switched_prep == False) or planner.loading_auto == False:
+            planner.finish_loading_auto()
+            st.switch_page("workflow/preprocessing/preprocessing_render.py")
+
+    c1,c2 = st.columns(2)
+    with c1:
+        st.title("数据导入")
+    with c2:
+        st.write("")  
+        st.write("")  
+        sac.buttons([
+            sac.ButtonsItem(label='Github', icon='github', href='https://github.com/Automated-Statistician/AutoSTAT'),
+            sac.ButtonsItem(label='Doc', icon=sac.BsIcon(name='bi bi-file-earmark-post-fill', size=16), href='https://automated-statistician.github.io/autostatdoc.github.io/'),
+        ], align='end', color='dark', variant='filled', index=None)
+    st.markdown("---")
+
+    c = st.columns(2)
+    with c[0].expander('数据上传', True):
+        loading_data_file(agent)
+    with c[1].expander('数据建议', True):
+        loading_chat(agent, auto)
+    with c[0].expander('数据展示', True):
+        loading_basic_info(agent)
+