added streamlit app

app.py

@@ -0,0 +1,148 @@
+import os
+import json
+from pathlib import Path
+import pandas as pd
+from typing import Dict, List
+import streamlit as st
+from st_aggrid import AgGrid, GridOptionsBuilder
+
+def create_config_dataframe(flattened_configs: List[Dict], ids: List[str]) -> pd.DataFrame:
+    df = pd.DataFrame(flattened_configs)
+    df.columns = [str(col).strip() for col in df.columns]
+    df.insert(0, 'id', ids)
+    return df
+
+def flatten_dict(d, parent_key='', sep='.'):
+    items = []
+    for k, v in d.items():
+        new_key = f"{parent_key}{sep}{k}" if parent_key else k
+        if isinstance(v, dict):
+            items.extend(flatten_dict(v, new_key, sep=sep).items())
+        else:
+            items.append((new_key, v))
+    return dict(items)
+
+@st.cache_data
+def load_config_data():
+    log_dir = Path(__file__).parent / '../.bulb/logs'
+    configs = []
+    dir_ids = []
+
+    for dir_name in log_dir.glob('*'):
+        if not dir_name.is_dir():
+            continue
+
+        config_path = dir_name / 'config.json'
+        if not config_path.exists():
+            continue
+
+        with open(config_path, 'r') as f:
+            config = json.load(f)
+            flattened_config = flatten_dict(config)
+            configs.append(flattened_config)
+            dir_ids.append(dir_name.name)
+    return create_config_dataframe(configs, dir_ids)
+
+@st.cache_data
+def load_eval_data():
+    log_dir = Path(__file__).parent / '../.bulb/logs'
+    scores = []
+    dir_ids = []
+
+    for dir_name in log_dir.glob('*'):
+        if not dir_name.is_dir():
+            continue
+
+        eval_path = dir_name / 'eval_log.json'
+        if not eval_path.exists():
+            continue
+
+        with open(eval_path, 'r') as f:
+            eval_data = json.load(f)
+            score_dict = {'test/mean_score': eval_data.get('test/mean_score')}
+            scores.append(score_dict)
+            dir_ids.append(dir_name.name)
+
+    return create_config_dataframe(scores, dir_ids)
+
+@st.cache_data
+def load_meta_data():
+    log_dir = Path(__file__).parent / '../.bulb/logs'
+    metas = []
+    dir_ids = []
+
+    for dir_name in log_dir.glob('*'):
+        if not dir_name.is_dir():
+            continue
+
+        meta_path = dir_name / 'meta.json'
+        if not meta_path.exists():
+            continue
+
+        with open(meta_path, 'r') as f:
+            meta = json.load(f)
+            metas.append(meta)
+            dir_ids.append(dir_name.name)
+    return create_config_dataframe(metas, dir_ids)
+
+def configure_grid(df):
+    gb = GridOptionsBuilder.from_dataframe(df)
+    gb.configure_pagination(paginationAutoPageSize=True)
+    gb.configure_side_bar()
+    gb.configure_default_column(groupable=True, value=True, enableRowGroup=True, aggFunc='sum', editable=False)
+    return gb.build()
+
+# Load data
+config_df = load_config_data()
+score_df = load_eval_data()
+meta_df = load_meta_data()
+
+experiments_df = pd.merge(
+    config_df, 
+    score_df,
+    on='id',
+    how='inner'
+)
+
+# Preprocess data
+columns_to_keep = ['id', 'Filter.name', 'checkpoint', 'model', 'task', 'test/mean_score', 
+                   'tags', 'start_time', 'Filter.threshold', 'Filter.seed', 'dataset']
+filtered_df = experiments_df[columns_to_keep].copy()
+filtered_df['Filter.threshold'] = filtered_df['Filter.threshold'].fillna('None')
+filtered_df['Filter.seed'] = filtered_df['Filter.seed'].fillna('None')
+filtered_df['start_time'] = pd.to_datetime(filtered_df['start_time'], format='%Y%m%d_%H%M%S')
+
+# Grouped view
+grouped_df = filtered_df.groupby(['model', 'Filter.name', 'tags', 'task', 'Filter.threshold']).agg({
+    'test/mean_score': ['mean', lambda x: list(x)],
+    'checkpoint': ('count', list),
+    'start_time': ('max', lambda x: sorted(x, reverse=True)),
+    'Filter.seed': ('count', list),
+}).reset_index()
+
+# Streamlit app
+st.set_page_config(layout="wide")
+st.title("Experiment Results Dashboard")
+
+tab1, tab2, tab3 = st.tabs(["Meta Data", "Experiment Results", "Grouped Analysis"])
+
+with tab1:
+    st.header("Experiment Metadata")
+    AgGrid(meta_df.sort_values(['start_time'], ascending=False), 
+           gridOptions=configure_grid(meta_df), 
+           height=400, 
+           fit_columns_on_grid_load=True)
+
+with tab2:
+    st.header("Filtered Experiment Results")
+    AgGrid(filtered_df.sort_values(['start_time'], ascending=False), 
+           gridOptions=configure_grid(filtered_df), 
+           height=600, 
+           fit_columns_on_grid_load=True)
+
+with tab3:
+    st.header("Grouped Performance Analysis")
+    AgGrid(grouped_df.sort_values([('start_time', 'max')], ascending=False), 
+           gridOptions=configure_grid(grouped_df), 
+           height=600, 
+           fit_columns_on_grid_load=True)

requirements.txt

@@ -1,3 +1,4 @@
 streamlit
 pandas
 plotly
+streamlit-aggrid