update time_series_gradio.py

time_series_gradio.py

@@ -4,10 +4,16 @@ from datetime import datetime
 from data import extract_model_data
 import gradio as gr
 
-def get_time_series_summary_dfs(historical_df: pd.DataFrame) -> dict:
-    """Return dataframes for historical summary plots (failure rates, AMD tests, NVIDIA tests)."""
+COLORS = {
+    'passed': '#4CAF50',
+    'failed': '#E53E3E',
+    'skipped': '#FFD54F',
+    'error': '#8B0000',
+    'amd': '#ED1C24',
+    'nvidia': '#76B900'
+}
 
-    # Group by date to get daily statistics
+def get_time_series_summary_dfs(historical_df: pd.DataFrame) -> dict:
     daily_stats = []
     dates = sorted(historical_df['date'].unique())
     for date in dates:
@@ -36,7 +42,6 @@ def get_time_series_summary_dfs(historical_df: pd.DataFrame) -> dict:
             'nvidia_skipped': nvidia_skipped
         })
 
-    # Failure rate dataframe
     failure_rate_data = []
     for i, stat in enumerate(daily_stats):
         amd_change = stat['amd_failure_rate'] - daily_stats[i-1]['amd_failure_rate'] if i > 0 else 0
@@ -47,7 +52,6 @@ def get_time_series_summary_dfs(historical_df: pd.DataFrame) -> dict:
         ])
     failure_rate_df = pd.DataFrame(failure_rate_data)
 
-    # AMD tests dataframe
     amd_data = []
     for i, stat in enumerate(daily_stats):
         passed_change = stat['amd_passed'] - daily_stats[i-1]['amd_passed'] if i > 0 else 0
@@ -60,7 +64,6 @@ def get_time_series_summary_dfs(historical_df: pd.DataFrame) -> dict:
         ])
     amd_df = pd.DataFrame(amd_data)
 
-    # NVIDIA tests dataframe
     nvidia_data = []
     for i, stat in enumerate(daily_stats):
         passed_change = stat['nvidia_passed'] - daily_stats[i-1]['nvidia_passed'] if i > 0 else 0
@@ -80,7 +83,6 @@ def get_time_series_summary_dfs(historical_df: pd.DataFrame) -> dict:
     }
 
 def get_model_time_series_dfs(historical_df: pd.DataFrame, model_name: str) -> dict:
-    """Return dataframes for a specific model's historical plots (AMD, NVIDIA)."""
     model_data = historical_df[historical_df.index.str.lower() == model_name.lower()]
     
     if model_data.empty:
@@ -125,9 +127,7 @@ def get_model_time_series_dfs(historical_df: pd.DataFrame, model_name: str) -> d
     return {'amd_df': pd.DataFrame(amd_data), 'nvidia_df': pd.DataFrame(nvidia_data)}
 
 def create_time_series_summary_gradio(historical_df: pd.DataFrame) -> dict:
-    """Create time-series visualization for overall failure rates over time using Gradio native plots."""
     if historical_df.empty or 'date' not in historical_df.columns:
-        # Return empty plots
         empty_df = pd.DataFrame({'date': [], 'failure_rate': [], 'platform': []})
         return {
             'failure_rates': gr.LinePlot(empty_df, x="date", y="failure_rate", color="platform", title="No historical data available", tooltip=["failure_rate", "date", "change"]),
@@ -135,21 +135,18 @@ def create_time_series_summary_gradio(historical_df: pd.DataFrame) -> dict:
             'nvidia_tests': gr.LinePlot(empty_df, x="date", y="failure_rate", color="platform", title="No historical data available", tooltip=["count", "date", "change"])
         }
     
-    # Group by date to get daily statistics
     daily_stats = []
     dates = sorted(historical_df['date'].unique())
     
     for date in dates:
         date_data = historical_df[historical_df['date'] == date]
         
-        # Calculate AMD stats - use the correct column names from the data structure
         amd_passed = date_data['success_amd'].sum() if 'success_amd' in date_data.columns else 0
         amd_failed = (date_data['failed_multi_no_amd'].sum() + date_data['failed_single_no_amd'].sum()) if 'failed_multi_no_amd' in date_data.columns else 0
         amd_skipped = date_data['skipped_amd'].sum() if 'skipped_amd' in date_data.columns else 0
         amd_total = amd_passed + amd_failed + amd_skipped
         amd_failure_rate = (amd_failed / amd_total * 100) if amd_total > 0 else 0
         
-        # Calculate NVIDIA stats - use the correct column names from the data structure
         nvidia_passed = date_data['success_nvidia'].sum() if 'success_nvidia' in date_data.columns else 0
         nvidia_failed = (date_data['failed_multi_no_nvidia'].sum() + date_data['failed_single_no_nvidia'].sum()) if 'failed_multi_no_nvidia' in date_data.columns else 0
         nvidia_skipped = date_data['skipped_nvidia'].sum() if 'skipped_nvidia' in date_data.columns else 0
@@ -168,12 +165,9 @@ def create_time_series_summary_gradio(historical_df: pd.DataFrame) -> dict:
             'nvidia_skipped': nvidia_skipped
         })
     
-    # Create failure rate data
     failure_rate_data = []
     for i, stat in enumerate(daily_stats):
-        # Calculate change from previous point
-        amd_change = 0
-        nvidia_change = 0
+        amd_change = nvidia_change = 0
         if i > 0:
             amd_change = stat['amd_failure_rate'] - daily_stats[i-1]['amd_failure_rate']
             nvidia_change = stat['nvidia_failure_rate'] - daily_stats[i-1]['nvidia_failure_rate']
@@ -185,13 +179,9 @@ def create_time_series_summary_gradio(historical_df: pd.DataFrame) -> dict:
     
     failure_rate_df = pd.DataFrame(failure_rate_data)
     
-    # Create AMD test results data
     amd_data = []
     for i, stat in enumerate(daily_stats):
-        # Calculate change from previous point for each test type
-        passed_change = 0
-        failed_change = 0
-        skipped_change = 0
+        passed_change = failed_change = skipped_change = 0
         if i > 0:
             passed_change = stat['amd_passed'] - daily_stats[i-1]['amd_passed']
             failed_change = stat['amd_failed'] - daily_stats[i-1]['amd_failed']
@@ -205,13 +195,9 @@ def create_time_series_summary_gradio(historical_df: pd.DataFrame) -> dict:
     
     amd_df = pd.DataFrame(amd_data)
     
-    # Create NVIDIA test results data
     nvidia_data = []
     for i, stat in enumerate(daily_stats):
-        # Calculate change from previous point for each test type
-        passed_change = 0
-        failed_change = 0
-        skipped_change = 0
+        passed_change = failed_change = skipped_change = 0
         if i > 0:
             passed_change = stat['nvidia_passed'] - daily_stats[i-1]['nvidia_passed']
             failed_change = stat['nvidia_failed'] - daily_stats[i-1]['nvidia_failed']
@@ -231,10 +217,10 @@ def create_time_series_summary_gradio(historical_df: pd.DataFrame) -> dict:
             x="date",
             y="failure_rate",
             color="platform",
-            color_map={"AMD": "#FF6B6B", "NVIDIA": "#4ECDC4"},
+            color_map={"AMD": COLORS['amd'], "NVIDIA": COLORS['nvidia']},
             title="Overall Failure Rates Over Time",
             tooltip=["failure_rate", "date", "change"],
-            height=300,
+            height=350,
             x_label_angle=45,
             y_title="Failure Rate (%)"
         ),
@@ -243,10 +229,10 @@ def create_time_series_summary_gradio(historical_df: pd.DataFrame) -> dict:
             x="date",
             y="count",
             color="test_type",
-            color_map={"Passed": "#4CAF50", "Failed": "#E53E3E", "Skipped": "#FFA500"},
+            color_map={"Passed": COLORS['passed'], "Failed": COLORS['failed'], "Skipped": COLORS['skipped']},
             title="AMD Test Results Over Time",
             tooltip=["count", "date", "change"],
-            height=300,
+            height=350,
             x_label_angle=45,
             y_title="Number of Tests"
         ),
@@ -255,10 +241,10 @@ def create_time_series_summary_gradio(historical_df: pd.DataFrame) -> dict:
             x="date",
             y="count",
             color="test_type",
-            color_map={"Passed": "#4CAF50", "Failed": "#E53E3E", "Skipped": "#FFA500"},
+            color_map={"Passed": COLORS['passed'], "Failed": COLORS['failed'], "Skipped": COLORS['skipped']},
             title="NVIDIA Test Results Over Time",
             tooltip=["count", "date", "change"],
-            height=300,
+            height=350,
             x_label_angle=45,
             y_title="Number of Tests"
         )
@@ -266,27 +252,22 @@ def create_time_series_summary_gradio(historical_df: pd.DataFrame) -> dict:
 
 
 def create_model_time_series_gradio(historical_df: pd.DataFrame, model_name: str) -> dict:
-    """Create time-series visualization for a specific model using Gradio native plots."""
     if historical_df.empty or 'date' not in historical_df.columns:
-        # Return empty plots
         empty_df = pd.DataFrame({'date': [], 'count': [], 'test_type': []})
         return {
             'amd_plot': gr.LinePlot(empty_df, x="date", y="count", color="test_type", title=f"{model_name.upper()} - AMD Results Over Time", tooltip=["count", "date", "change"]),
             'nvidia_plot': gr.LinePlot(empty_df, x="date", y="count", color="test_type", title=f"{model_name.upper()} - NVIDIA Results Over Time", tooltip=["count", "date", "change"])
         }
     
-    # Filter data for the specific model (model_name is the index)
     model_data = historical_df[historical_df.index.str.lower() == model_name.lower()]
     
     if model_data.empty:
-        # Return empty plots
         empty_df = pd.DataFrame({'date': [], 'count': [], 'test_type': []})
         return {
             'amd_plot': gr.LinePlot(empty_df, x="date", y="count", color="test_type", title=f"{model_name.upper()} - AMD Results Over Time", tooltip=["count", "date", "change"]),
             'nvidia_plot': gr.LinePlot(empty_df, x="date", y="count", color="test_type", title=f"{model_name.upper()} - NVIDIA Results Over Time", tooltip=["count", "date", "change"])
         }
     
-    # Group by date
     dates = sorted(model_data['date'].unique())
     
     amd_data = []
@@ -296,18 +277,13 @@ def create_model_time_series_gradio(historical_df: pd.DataFrame, model_name: str
         date_data = model_data[model_data['date'] == date]
         
         if not date_data.empty:
-            # Get the first row for this date (should be only one)
             row = date_data.iloc[0]
             
-            # AMD data - use the correct column names from the data structure
             amd_passed = row.get('success_amd', 0)
             amd_failed = row.get('failed_multi_no_amd', 0) + row.get('failed_single_no_amd', 0)
             amd_skipped = row.get('skipped_amd', 0)
             
-            # Calculate change from previous point
-            passed_change = 0
-            failed_change = 0
-            skipped_change = 0
+            passed_change = failed_change = skipped_change = 0
             if i > 0:
                 prev_date_data = model_data[model_data['date'] == dates[i-1]]
                 if not prev_date_data.empty:
@@ -326,15 +302,11 @@ def create_model_time_series_gradio(historical_df: pd.DataFrame, model_name: str
                 {'date': date, 'count': amd_skipped, 'test_type': 'Skipped', 'change': skipped_change}
             ])
             
-            # NVIDIA data - use the correct column names from the data structure
             nvidia_passed = row.get('success_nvidia', 0)
             nvidia_failed = row.get('failed_multi_no_nvidia', 0) + row.get('failed_single_no_nvidia', 0)
             nvidia_skipped = row.get('skipped_nvidia', 0)
             
-            # Calculate change from previous point for NVIDIA
-            nvidia_passed_change = 0
-            nvidia_failed_change = 0
-            nvidia_skipped_change = 0
+            nvidia_passed_change = nvidia_failed_change = nvidia_skipped_change = 0
             if i > 0:
                 prev_date_data = model_data[model_data['date'] == dates[i-1]]
                 if not prev_date_data.empty:
@@ -362,11 +334,11 @@ def create_model_time_series_gradio(historical_df: pd.DataFrame, model_name: str
             x="date",
             y="count",
             color="test_type",
-            color_map={"Passed": "#4CAF50", "Failed": "#E53E3E", "Skipped": "#FFA500"},
+            color_map={"Passed": COLORS['passed'], "Failed": COLORS['failed'], "Skipped": COLORS['skipped']},
             title=f"{model_name.upper()} - AMD Results Over Time",
             x_label_angle=45,
             y_title="Number of Tests",
-            height=300,
+            height=350,
             tooltip=["count", "date", "change"]
         ),
         'nvidia_plot': gr.LinePlot(
@@ -374,11 +346,11 @@ def create_model_time_series_gradio(historical_df: pd.DataFrame, model_name: str
             x="date",
             y="count",
             color="test_type",
-            color_map={"Passed": "#4CAF50", "Failed": "#E53E3E", "Skipped": "#FFA500"},
+            color_map={"Passed": COLORS['passed'], "Failed": COLORS['failed'], "Skipped": COLORS['skipped']},
             title=f"{model_name.upper()} - NVIDIA Results Over Time",
             x_label_angle=45,
             y_title="Number of Tests",
-            height=300,
+            height=350,
             tooltip=["count", "date", "change"]
         )
-    }
+    }