remove some unused functions

time_series_gradio.py

@@ -7,76 +7,6 @@ import gradio as gr
 import plotly.express as px
 import plotly.graph_objects as go
 
-def get_time_series_summary_dfs(historical_df: pd.DataFrame) -> dict:
-    daily_stats = []
-    dates = sorted(historical_df['date'].unique())
-    
-    for date in dates:
-        dd = historical_df[historical_df['date'] == date]
-        stats = {}
-        for platform in ['amd', 'nvidia']:
-            p, f, s = (dd[f'success_{platform}'].sum() if f'success_{platform}' in dd.columns else 0,
-                      (dd[f'failed_multi_no_{platform}'].sum() + dd[f'failed_single_no_{platform}'].sum()) if f'failed_multi_no_{platform}' in dd.columns else 0,
-                      dd[f'skipped_{platform}'].sum() if f'skipped_{platform}' in dd.columns else 0)
-            tot = p + f + s
-            stats.update({f'{platform}_passed': p, f'{platform}_failed': f, f'{platform}_skipped': s, 
-                         f'{platform}_failure_rate': (f / tot * 100) if tot > 0 else 0})
-        stats['date'] = date
-        daily_stats.append(stats)
-
-    failure_rate_data = []
-    for i, s in enumerate(daily_stats):
-        for p in ['amd', 'nvidia']:
-            chg = s[f'{p}_failure_rate'] - daily_stats[i-1][f'{p}_failure_rate'] if i > 0 else 0
-            failure_rate_data.append({'date': s['date'], 'failure_rate': s[f'{p}_failure_rate'], 
-                                     'platform': p.upper(), 'change': chg})
-
-    def build_test_data(platform):
-        data = []
-        for i, s in enumerate(daily_stats):
-            for tt in ['passed', 'failed', 'skipped']:
-                chg = s[f'{platform}_{tt}'] - daily_stats[i-1][f'{platform}_{tt}'] if i > 0 else 0
-                data.append({'date': s['date'], 'count': s[f'{platform}_{tt}'], 
-                           'test_type': tt.capitalize(), 'change': chg})
-        return pd.DataFrame(data)
-
-    return {'failure_rates_df': pd.DataFrame(failure_rate_data), 
-            'amd_tests_df': build_test_data('amd'), 
-            'nvidia_tests_df': build_test_data('nvidia')}
-
-def get_model_time_series_dfs(historical_df: pd.DataFrame, model_name: str) -> dict:
-    md = historical_df[historical_df.index.str.lower() == model_name.lower()]
-    if md.empty:
-        empty = pd.DataFrame({'date': [], 'count': [], 'test_type': [], 'change': []})
-        return {'amd_df': empty.copy(), 'nvidia_df': empty.copy()}
-
-    dates = sorted(md['date'].unique())
-    
-    def build_platform_data(platform):
-        data = []
-        for i, date in enumerate(dates):
-            dd = md[md['date'] == date]
-            if dd.empty:
-                continue
-            r = dd.iloc[0]
-            p = r.get(f'success_{platform}', 0)
-            f = r.get(f'failed_multi_no_{platform}', 0) + r.get(f'failed_single_no_{platform}', 0)
-            s = r.get(f'skipped_{platform}', 0)
-            
-            pr = md[md['date'] == dates[i-1]].iloc[0] if i > 0 and not md[md['date'] == dates[i-1]].empty else None
-            pc = pr.get(f'success_{platform}', 0) if pr is not None else 0
-            fc = (pr.get(f'failed_multi_no_{platform}', 0) + pr.get(f'failed_single_no_{platform}', 0)) if pr is not None else 0
-            sc = pr.get(f'skipped_{platform}', 0) if pr is not None else 0
-            
-            data.extend([
-                {'date': date, 'count': p, 'test_type': 'Passed', 'change': p - pc},
-                {'date': date, 'count': f, 'test_type': 'Failed', 'change': f - fc},
-                {'date': date, 'count': s, 'test_type': 'Skipped', 'change': s - sc}
-            ])
-        return pd.DataFrame(data)
-    
-    return {'amd_df': build_platform_data('amd'), 'nvidia_df': build_platform_data('nvidia')}
-
 def create_time_series_summary_gradio(historical_df: pd.DataFrame) -> dict:
     empty_fig = lambda title: go.Figure().update_layout(title=title, height=500, 
         font=dict(size=16, color='#CCCCCC'), paper_bgcolor='#000000',