feat: 初步实现

.gitignore

@@ -1 +1,154 @@
-data
+data
+deprecated
+_docs/
+_proc/
+
+*.bak
+.gitattributes
+.last_checked
+.gitconfig
+*.bak
+*.log
+*~
+~*
+_tmp*
+tmp*
+tags
+*.pkg
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+conda/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+.vscode
+*.swp
+
+# osx generated files
+.DS_Store
+.DS_Store?
+.Trashes
+ehthumbs.db
+Thumbs.db
+.idea
+
+# pytest
+.pytest_cache
+
+# tools/trust-doc-nbs
+docs_src/.last_checked
+
+# symlinks to fastai
+docs_src/fastai
+tools/fastai
+
+# link checker
+checklink/cookies.txt
+
+# .gitconfig is now autogenerated
+.gitconfig
+
+# Quarto installer
+.deb
+.pkg
+
+# Quarto
+.quarto

.streamlit/config.toml

@@ -0,0 +1,6 @@
+[theme]
+primaryColor="#F39C12"
+backgroundColor="#2E86C1"
+secondaryBackgroundColor="#7F8C8D"
+textColor="#FFFFFF"
+font="monospace"

src/data_loader.py

@@ -0,0 +1,188 @@
+# data_loader.py
+from pathlib import Path
+from typing import Dict, List, Any
+import pandas as pd
+import tomli
+import streamlit as st
+from functools import lru_cache # For non-Streamlit specific caching if needed
+
+# Assuming utils.py is in the same directory
+from utils import DATA_ROOT_PATH # Used for ensuring directory exists
+
+# --- Cache Clearing Functions ---
+# These are more specific cache clearing functions that can be called by model methods.
+
+def clear_study_cache():
+    """Clears all study discovery cache."""
+    discover_studies_cached.clear()
+    st.toast("所有 Study 发现缓存已清除。")
+
+def clear_trial_cache():
+    """Clears all trial-related data loading caches."""
+    # This is a bit broad. Ideally, clear caches for specific trials/studies.
+    load_input_variables_from_path.clear()
+    load_all_metrics_for_trial_path.clear()
+    discover_trials_from_path.clear()
+    st.toast("所有 Trial 数据加载缓存已清除。")
+
+def clear_specific_trial_metric_cache(trial_path: Path):
+    load_all_metrics_for_trial_path.clear() # This clears the whole cache for this func
+    # For more granular control with @st.cache_data, you'd typically rely on Streamlit's
+    # automatic cache invalidation based on input args, or rerun.
+    # If using lru_cache, you could do: load_all_metrics_for_trial_path.cache_clear()
+    # but for st.cache_data, clearing for specific args is not direct.
+    # The common pattern is to clear the entire function's cache.
+    st.toast(f"Trial '{trial_path.name}' 的指标缓存已清除 (函数级别)。")
+
+
+def clear_specific_trial_input_vars_cache(trial_path: Path):
+    load_input_variables_from_path.clear()
+    st.toast(f"Trial '{trial_path.name}' 的参数缓存已清除 (函数级别)。")
+
+def clear_specific_study_trial_discovery_cache(study_path: Path):
+    discover_trials_from_path.clear()
+    st.toast(f"Study '{study_path.name}' 的 Trial 发现缓存已清除 (函数级别)。")
+
+
+# --- Data Discovery and Loading Functions (Cached) ---
+
+def ensure_data_directory_exists(data_path: Path = DATA_ROOT_PATH):
+    """Ensures the root data directory exists."""
+    if not data_path.exists():
+        try:
+            data_path.mkdir(parents=True, exist_ok=True)
+            st.info(f"数据目录 {data_path} 已创建。")
+        except Exception as e:
+            st.error(f"创建数据目录 {data_path} 失败: {e}")
+            st.stop()
+    elif not data_path.is_dir():
+        st.error(f"路径 {data_path} 已存在但不是一个目录。")
+        st.stop()
+
+
+@st.cache_data(ttl=3600) # Cache for 1 hour, or adjust as needed
+def discover_studies_cached(_data_root: Path) -> Dict[str, Any]: # Return type hint as Any to avoid circular dep with data_models.Study
+    """
+    Scans the data_root for study directories and returns a dictionary
+    mapping study names to Study objects (or just their paths initially).
+    The actual Study object creation happens in the main app for now.
+    """
+    # To avoid issues with caching complex objects directly, or circular dependencies,
+    # this function can return simpler structures like Dict[str, Path]
+    # and the main app or model can instantiate Study objects.
+    # For this iteration, we'll import Study here for convenience, assuming careful structure.
+    from data_models import Study # Local import to help with potential circularity if models grow complex
+
+    if not _data_root.is_dir():
+        return {}
+    studies = {}
+    for d in _data_root.iterdir():
+        if d.is_dir():
+            studies[d.name] = Study(name=d.name, path=d)
+    return studies
+
+
+@st.cache_data(ttl=3600)
+def discover_trials_from_path(_study_path: Path) -> Dict[str, Path]:
+    """Scans a study_path for trial directories."""
+    if not _study_path.is_dir():
+        return {}
+    trials = {}
+    for d in _study_path.iterdir():
+        if d.is_dir():
+            trials[d.name] = d
+    return trials
+
+
+@st.cache_data(ttl=3600)
+def load_input_variables_from_path(_trial_path: Path) -> Dict[str, Any]:
+    """Loads input_variables.toml from a trial directory."""
+    input_vars_file = _trial_path / "input_variables.toml"
+    if input_vars_file.exists():
+        try:
+            with open(input_vars_file, "rb") as f:
+                return tomli.load(f)
+        except tomli.TOMLDecodeError:
+            # st.error(f"错误：无法解析 input_variables.toml 文件于 {_trial_path}") # Avoid st.error in cached funcs if possible
+            print(f"Error parsing input_variables.toml at {_trial_path}")
+            return {}
+    return {}
+
+
+def _load_single_metric_toml(_toml_file_path: Path) -> pd.DataFrame:
+    """Loads metrics from a single TOML file into a DataFrame."""
+    if not _toml_file_path.exists():
+        return pd.DataFrame()
+    try:
+        with open(_toml_file_path, "rb") as f:
+            data = tomli.load(f)
+        metrics_list = data.get("metrics", [])
+        if not metrics_list:
+            return pd.DataFrame()
+        return pd.DataFrame(metrics_list)
+    except tomli.TOMLDecodeError:
+        print(f"Error parsing TOML file: {_toml_file_path.name}")
+        return pd.DataFrame()
+    except Exception as e:
+        print(f"Error loading {_toml_file_path.name}: {e}")
+        return pd.DataFrame()
+
+
+@st.cache_data(ttl=300) # Cache metric data for 5 minutes
+def load_all_metrics_for_trial_path(_trial_path: Path) -> Dict[str, pd.DataFrame]:
+    """
+    Loads all metrics from all tracks in a trial.
+    Returns a dictionary where keys are metric names (e.g., 'loss', 'accuracy')
+    and values are DataFrames containing 'global_step', 'value', and 'track'.
+    """
+    scalar_dir = _trial_path / "logs" / "scalar"
+    if not scalar_dir.is_dir():
+        return {}
+
+    all_metrics_data_combined: Dict[str, pd.DataFrame] = {}
+
+    for toml_file in scalar_dir.glob("metrics_*.toml"):
+        track_name = toml_file.stem.replace("metrics_", "")
+        df_track = _load_single_metric_toml(toml_file)
+
+        if df_track.empty or "global_step" not in df_track.columns:
+            continue
+
+        id_vars = ["global_step"]
+        value_vars = [col for col in df_track.columns if col not in id_vars]
+        
+        if not value_vars:
+            continue
+        
+        # Process each metric column individually to build up the combined DataFrame
+        for metric_col_name in value_vars:
+            try:
+                # Create a DataFrame for the current metric and track
+                current_metric_df = df_track[["global_step", metric_col_name]].copy()
+                current_metric_df.rename(columns={metric_col_name: "value"}, inplace=True)
+                current_metric_df["track"] = track_name
+                current_metric_df['value'] = pd.to_numeric(current_metric_df['value'], errors='coerce')
+                current_metric_df.dropna(subset=['value'], inplace=True)
+
+                if current_metric_df.empty:
+                    continue
+
+                # Append to the combined DataFrame for this metric_col_name
+                if metric_col_name not in all_metrics_data_combined:
+                    all_metrics_data_combined[metric_col_name] = current_metric_df
+                else:
+                    all_metrics_data_combined[metric_col_name] = pd.concat(
+                        [all_metrics_data_combined[metric_col_name], current_metric_df],
+                        ignore_index=True
+                    )
+            except Exception as e:
+                print(f"Error processing metric '{metric_col_name}' from file '{toml_file.name}': {e}")
+                continue
+                
+    # Sort data by global_step for proper line plotting
+    for metric_name in all_metrics_data_combined:
+        all_metrics_data_combined[metric_name] = all_metrics_data_combined[metric_name].sort_values(
+            by=["track", "global_step"]
+        ).reset_index(drop=True)
+
+    return all_metrics_data_combined

src/data_models.py

@@ -0,0 +1,94 @@
+# data_models.py
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Dict, List, Optional, Any
+import pandas as pd
+import streamlit as st # For caching
+
+# Import from data_loader, assuming it's in the same directory
+# We'll define these functions in data_loader.py
+# To avoid circular imports, data_loader functions won't import data_models directly for type hints if possible,
+# or use string type hints.
+
+# Forward declaration for type hint if data_loader needs Study/Trial
+# class Study: pass
+# class Trial: pass
+
+from data_loader import (
+    load_input_variables_from_path,
+    load_all_metrics_for_trial_path,
+    discover_trials_from_path,
+    clear_trial_cache as clear_trial_loader_cache,
+    clear_study_cache as clear_study_loader_cache,
+    clear_specific_trial_metric_cache,
+    clear_specific_trial_input_vars_cache,
+    clear_specific_study_trial_discovery_cache
+)
+
+
+@dataclass
+class Trial:
+    name: str
+    path: Path
+    study_name: str # To know its parent study
+    input_variables: Dict[str, Any] = field(default_factory=dict, repr=False)
+    metrics_data: Dict[str, pd.DataFrame] = field(default_factory=dict, repr=False) # Key: metric_name, Value: DataFrame with global_step, value, track
+
+    def __post_init__(self):
+        # Automatically load data if needed, but prefer explicit calls from UI for clarity
+        pass
+
+    # Use st.cache_data on the loader functions, not directly here for complex objects.
+    # Instead, methods here will call cached loader functions.
+
+    def load_input_variables_cached(self):
+        """Loads or retrieves cached input variables."""
+        if not self.input_variables: # Load only if not already populated
+            self.input_variables = load_input_variables_from_path(self.path)
+        return self.input_variables
+
+    def load_metrics_cached(self):
+        """Loads or retrieves cached metrics data."""
+        if not self.metrics_data: # Load only if not already populated
+            self.metrics_data = load_all_metrics_for_trial_path(self.path)
+        return self.metrics_data
+
+    def get_metric_dataframe(self, metric_name: str) -> Optional[pd.DataFrame]:
+        """Returns the DataFrame for a specific metric, combining all tracks."""
+        return self.metrics_data.get(metric_name)
+
+    def clear_cache(self):
+        """Clears cached data for this specific trial."""
+        # Clear Streamlit's cache for functions related to this trial
+        clear_specific_trial_metric_cache(self.path)
+        clear_specific_trial_input_vars_cache(self.path)
+        # Reset instance variables
+        self.input_variables = {}
+        self.metrics_data = {}
+        st.success(f"Trial '{self.name}' 的缓存已清除。")
+
+
+@dataclass
+class Study:
+    name: str
+    path: Path
+    trials: Dict[str, Trial] = field(default_factory=dict, repr=False)
+
+    def discover_trials_cached(self):
+        """Discovers or retrieves cached trials for this study."""
+        if not self.trials: # Discover only if not already populated
+            trial_paths = discover_trials_from_path(self.path) # This loader function should be cached
+            for trial_name, trial_path in trial_paths.items():
+                self.trials[trial_name] = Trial(name=trial_name, path=trial_path, study_name=self.name)
+        return self.trials
+
+    def get_trial(self, trial_name: str) -> Optional[Trial]:
+        return self.trials.get(trial_name)
+
+    def clear_cache(self):
+        """Clears cached data for this study and its trials."""
+        clear_specific_study_trial_discovery_cache(self.path)
+        for trial in self.trials.values():
+            trial.clear_cache() # Clear cache for each trial within the study
+        self.trials = {} # Reset trials dictionary
+        st.success(f"Study '{self.name}' 及其 Trials 的缓存已清除。")

src/streamlit_app.py

@@ -1,97 +1,238 @@
 import streamlit as st
 from pathlib import Path
-import tomli
-import plotly.graph_objects as go
-from typing import List, Dict
-
-# === 配置路径 ===
-DATA_ROOT = Path(__file__).parent.parent / "data"
-
-# === 页面设置 ===
-st.set_page_config(page_title="Flowillower 🌸", layout="wide")
-st.markdown("<h1 style='text-align: center; color: #7b68ee;'>Flowillower 🌸</h1>", unsafe_allow_html=True)
-st.markdown("<p style='text-align: center;'>Elegant and Transparent Experiment Tracking</p>", unsafe_allow_html=True)
-st.divider()
-
-# === 加载所有 Study ===
-def get_all_studies(data_root: Path) -> List[Path]:
-    return [p for p in data_root.iterdir() if p.is_dir()]
-
-studies = get_all_studies(DATA_ROOT)
-study_names = []
-
-for s in studies:
-    meta_file = s / ".study_meta.toml"
-    if meta_file.exists():
-        try:
-            meta = tomli.loads(meta_file.read_text())
-            name = meta.get("display_name", s.name)
-        except:
-            name = s.name
+import altair as alt
+
+# 导入重构后的模块
+# 这些文件需要您在与此 app.py 同级的目录下或Python路径中创建
+try:
+    from utils import DATA_ROOT_PATH, AppMode
+    from data_models import Study, Trial
+    from data_loader import discover_studies_cached, ensure_data_directory_exists
+except ImportError as e:
+    st.error(
+        f"导入模块失败，请确保 utils.py, data_models.py, data_loader.py 文件存在于正确的位置: {e}"
+    )
+    st.stop()
+
+
+# --- 应用状态管理 ---
+if "selected_study_name" not in st.session_state:
+    st.session_state.selected_study_name = None
+if "selected_trial_name" not in st.session_state:
+    st.session_state.selected_trial_name = None
+if "studies_data" not in st.session_state: # To store loaded Study objects
+    st.session_state.studies_data = {}
+if "app_mode" not in st.session_state:
+    st.session_state.app_mode = AppMode.VIEWING # Default mode
+
+# --- Page Configuration ---
+st.set_page_config(layout="wide", page_title="柳暗花明 (flowillower)")
+
+
+# --- UI Rendering ---
+
+# --- Header ---
+header_cols = st.columns([2, 3, 1.5, 0.5, 0.5, 0.5])
+with header_cols[0]:
+    st.markdown("## 柳暗花明")
+    st.caption("flowillower")
+
+# 确保数据目录存在
+ensure_data_directory_exists(DATA_ROOT_PATH)
+
+# 加载 Studies
+all_study_objects = discover_studies_cached(DATA_ROOT_PATH) # Returns Dict[str, Study]
+study_names = list(all_study_objects.keys())
+
+if not study_names:
+    st.warning(f"在 {DATA_ROOT_PATH} 未找到任何 Study。请确保您的数据结构正确或使用 flowillower API 开始记录实验。")
+
+# Study 选择
+if study_names:
+    with header_cols[1]:
+        # 如果 session_state 中的 study_name 不在当前发现的 study_names 中，重置它
+        if st.session_state.selected_study_name not in study_names:
+            st.session_state.selected_study_name = study_names[0] if study_names else None
+
+        selected_study_name_from_ui = st.selectbox(
+            "选择 Study (Select Study)",
+            study_names,
+            index=study_names.index(st.session_state.selected_study_name) if st.session_state.selected_study_name in study_names else 0,
+            label_visibility="collapsed",
+            key="study_selector_ui" # Use a different key to avoid conflict if direct assignment is used
+        )
+        # Update session state if selection changes
+        if selected_study_name_from_ui != st.session_state.selected_study_name:
+            st.session_state.selected_study_name = selected_study_name_from_ui
+            st.session_state.selected_trial_name = None # Reset trial when study changes
+            st.rerun()
+
+
+    with header_cols[2]:
+        if st.session_state.selected_study_name:
+            st.write(f"当前 Study: **{st.session_state.selected_study_name}**")
+else:
+    with header_cols[1]:
+        st.info("没有可用的 Study。")
+
+
+# Placeholder for right-side icons
+with header_cols[3]:
+    st.button("➕", help="添加 (Add)", disabled=True)
+with header_cols[4]:
+    st.button("⚙️", help="设置 (Settings)", disabled=True)
+with header_cols[5]:
+    st.button("👤", help="用户 (User)", disabled=True)
+
+st.markdown("---")
+
+
+# --- Sidebar ---
+current_study: Study | None = None
+if st.session_state.selected_study_name and st.session_state.selected_study_name in all_study_objects:
+    current_study = all_study_objects[st.session_state.selected_study_name]
+    # Ensure trials are discovered for the current study
+    if not current_study.trials: # Discover trials if not already done
+        current_study.discover_trials_cached()
+
+
+trial_names = []
+if current_study:
+    trial_names = list(current_study.trials.keys())
+
+
+with st.sidebar:
+    st.markdown("### Study")
+    if current_study:
+        st.markdown(f"##### {current_study.name}")
+        if st.button("刷新 Study 数据 (Refresh Study Data)", use_container_width=True):
+            current_study.clear_cache() # Clear specific study cache
+            st.rerun()
+
+        # These buttons can be linked to specific views or functionalities later
+        if st.button("概览 (Overview)", use_container_width=True, disabled=True):
+            st.toast("功能待实现 (Feature to be implemented)")
+        if st.button("图表对比视图 (Chart Comparison View)", use_container_width=True, disabled=True):
+            st.toast("功能待实现 (Feature to be implemented)")
     else:
-        name = s.name
-    study_names.append((name, s))
-
-# === Study 选择器 ===
-study_name_list = [name for name, _ in study_names]
-selected_study_name = st.sidebar.selectbox("Select a Study", study_name_list)
-selected_study_path = dict(study_names)[selected_study_name]
-
-# === Study 概览区 ===
-st.subheader(f"📁 Study Overview: {selected_study_name}")
-st.markdown("Metadata, description, config summary can be shown here (to be implemented).")
-st.divider()
-
-# === 加载所有 Trial ===
-def get_all_trials(study_path: Path) -> List[Path]:
-    return [p for p in study_path.iterdir() if p.is_dir()]
-
-trials = get_all_trials(selected_study_path)
-trial_names = [t.name for t in trials]
-selected_trial_name = st.sidebar.selectbox("🧪 Select Trial", trial_names)
-selected_trial_path = selected_study_path / selected_trial_name
-
-# === 加载 track 的 metric 数据 ===
-def load_track_data(trial_path: Path) -> Dict[str, List[Dict]]:
-    track_dir = trial_path / "logs/scalar"
-    result = {}
-    if not track_dir.exists(): return result
-
-    for f in track_dir.glob("metrics_*.toml"):
-        track_name = f.stem.replace("metrics_", "")
-        with open(f, "rb") as fp:
-            try:
-                data = tomli.load(fp).get("metrics", [])
-                result[track_name] = data
-            except:
-                continue
-    return result
-
-# === 展示 Trial 详细信息 ===
-st.subheader(f"📊 Trial: {selected_trial_name}")
-track_data = load_track_data(selected_trial_path)
-
-if not track_data:
-    st.warning("No metrics data found for this trial.")
+        st.markdown("未选择 Study (No Study Selected)")
+
+    st.markdown("---")
+    st.markdown("### Trial")
+
+    if current_study and trial_names:
+        # 如果 session_state 中的 trial_name 不在当前发现的 trial_names 中，重置它
+        if st.session_state.selected_trial_name not in trial_names:
+            st.session_state.selected_trial_name = trial_names[0] if trial_names else None
+
+        selected_trial_name_from_ui = st.radio(
+            "选择 Trial (Select Trial)",
+            trial_names,
+            index=trial_names.index(st.session_state.selected_trial_name) if st.session_state.selected_trial_name in trial_names else 0,
+            label_visibility="collapsed",
+            key="trial_selector_ui"
+        )
+        if selected_trial_name_from_ui != st.session_state.selected_trial_name:
+            st.session_state.selected_trial_name = selected_trial_name_from_ui
+            st.rerun()
+
+        if st.session_state.selected_trial_name:
+            st.markdown(f"当前选择: **{st.session_state.selected_trial_name}**")
+
+    elif current_study:
+        st.info(f"Study '{current_study.name}' 中没有 Trial。")
+    else:
+        st.info("请先选择一个 Study。")
+
+    st.markdown("---")
+    if st.button("⚙️ App 设置 (App Settings)", use_container_width=True, disabled=True):
+        st.toast("功能待实现 (Feature to be implemented)")
+
+
+# --- Main Content Area ---
+current_trial: Trial | None = None
+if current_study and st.session_state.selected_trial_name and st.session_state.selected_trial_name in current_study.trials:
+    current_trial = current_study.trials[st.session_state.selected_trial_name]
+    # Load data for the current trial if not already loaded (methods are cached)
+    current_trial.load_input_variables_cached()
+    current_trial.load_metrics_cached()
+
+
+if current_study and current_trial:
+    main_title_cols = st.columns([3,1, 0.5])
+    with main_title_cols[0]:
+        st.markdown(f"## {current_trial.name}")
+        st.caption(f"属于 Study: {current_study.name}")
+    with main_title_cols[1]:
+        if st.button("刷新 Trial 数据 (Refresh Trial Data)", type="secondary"):
+            current_trial.clear_cache()
+            st.rerun()
+    with main_title_cols[2]:
+        st.button("...", help="更多选项 (More Options)", disabled=True)
+
+    tab_titles = ["图表 (Charts)", "参数 (Parameters)", "系统 (System)", "日志 (Logs)", "环境 (Environment)"]
+    tab_charts, tab_params, tab_system, tab_logs, tab_env = st.tabs(tab_titles)
+
+    with tab_charts:
+        st.header("指标图表 (Metrics Charts)")
+        st.markdown("---")
+
+        if not current_trial.metrics_data:
+            st.info("当前 Trial 没有可显示的指标数据。请检查 `logs/scalar` 文件夹和 TOML 文件。")
+        else:
+            num_metrics = len(current_trial.metrics_data)
+            cols_per_row = st.slider("每行图表数量 (Charts per row)", 1, 4, min(2, num_metrics) if num_metrics > 0 else 1, key=f"cols_slider_{current_trial.name}")
+            
+            metric_names = sorted(list(current_trial.metrics_data.keys()))
+            
+            for i in range(0, num_metrics, cols_per_row):
+                metric_chunk = metric_names[i : i + cols_per_row]
+                chart_cols = st.columns(cols_per_row)
+                for j, metric_name in enumerate(metric_chunk):
+                    with chart_cols[j]:
+                        df_metric = current_trial.get_metric_dataframe(metric_name)
+                        if df_metric is None or df_metric.empty:
+                            st.warning(f"指标 '{metric_name}' 数据不完整或缺失。")
+                            continue
+                        
+                        with st.container(border=True):
+                            st.subheader(metric_name)
+                            try:
+                                chart = alt.Chart(df_metric).mark_line(point=alt.MarkDef(size=20)).encode(
+                                    x=alt.X('global_step:Q', title='全局步骤 (Global Step)'),
+                                    y=alt.Y('value:Q', title=metric_name, scale=alt.Scale(zero=False)),
+                                    color='track:N',
+                                    tooltip=['global_step', 'value', 'track']
+                                ).interactive()
+                                st.altair_chart(chart, use_container_width=True)
+                            except Exception as e:
+                                st.error(f"为指标 '{metric_name}' 生成图表时出错: {e}")
+                                st.dataframe(df_metric)
+
+    with tab_params:
+        st.header("输入参数 (Input Parameters)")
+        if current_trial.input_variables:
+            st.json(current_trial.input_variables)
+        else:
+            st.info("未找到 `input_variables.toml` 或文件为空。")
+
+    # Placeholder tabs
+    for tab_content, name in [(tab_system, "系统监控 (System Monitoring)"), 
+                              (tab_logs, "日志 (Logs)"), 
+                              (tab_env, "环境 (Environment)")]:
+        with tab_content:
+            st.header(name)
+            st.info("此功能待您的 `flowillower` API 提供相关数据后实现。")
+
+elif not st.session_state.selected_study_name:
+    st.info("👈 请从顶部选择一个 Study 开始。(Please select a Study from the top to begin.)")
+elif not st.session_state.selected_trial_name:
+    st.info("👈 请从侧边栏选择一个 Trial。(Please select a Trial from the sidebar.)")
 else:
-    for track_name, records in track_data.items():
-        st.markdown(f"### Track: {track_name}")
-        if not records:
-            st.info("No data in this track.")
-            continue
-
-        # 自动获取所有标量键（除 global_step）
-        keys = [k for k in records[0].keys() if k != "global_step"]
-        for key in keys:
-            fig = go.Figure()
-            fig.add_trace(go.Scatter(
-                x=[r["global_step"] for r in records],
-                y=[r.get(key, None) for r in records],
-                mode='lines+markers',
-                name=key
-            ))
-            fig.update_layout(title=f"{track_name.upper()} - {key}", template="plotly_dark", height=350)
-            st.plotly_chart(fig, use_container_width=True)
-
-st.divider()
-st.markdown("*Flowillower 🌸 — Minimalist, Poetic and Open Source.*")
+    st.info("请选择 Study 和 Trial 以查看数据。")
+
+
+# --- Footer (Optional) ---
+st.markdown("---")
+st.caption("柳暗花明 (flowillower) - 数据可视化App (Data Visualization App)")
+

src/utils.py

@@ -0,0 +1,16 @@
+# utils.py
+from pathlib import Path
+from enum import Enum, auto
+
+# Base path for studies and trials.
+# Streamlit apps are typically run from their root directory.
+# If your app.py is in 'src/', and 'data/' is at the same level as 'src/',
+# then Path("./data") from app.py's perspective would be Path("../data").
+# For simplicity, assuming data is relative to where streamlit run is executed,
+# or you adjust this path accordingly.
+DATA_ROOT_PATH = Path("./data").resolve()
+
+
+class AppMode(Enum):
+    VIEWING = auto()
+    # Potentially other modes like COMPARISON, EDITING etc.