core: data shape

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+from datetime import datetime
 
 from src.ui import sidebar_ui, dashboard_ui
 from src.ui.session import session_state
@@ -12,10 +13,10 @@ custom_theme = gr.themes.Base(
 
 STATE = {
     "running": False,
-    "current_time": None,
-    "part_id": None,
+    "date": datetime.now(),
+    "part_id": 0,
+    "status": {},
     "data": {},
-    "efficiency": {},
 }
 
 with gr.Blocks(theme=custom_theme) as demo:

src/production/flow.py

@@ -13,7 +13,7 @@ async def generate_data(state):
     """
     Generate synthetic production data for a manufacturing process.
     """
-    current_time = state["current_time"] if state["current_time"] else datetime.now()
+    current_time = state["date"] if state["date"] else datetime.now()
     part_id = state["part_id"] if state["part_id"] else 0
 
     non_compliance_rates = {
@@ -95,5 +95,5 @@ async def generate_data(state):
 
         current_time += timedelta(seconds=1)
 
-    state["current_time"] = current_time
+    state["date"] = current_time
     state["part_id"] = part_id

src/production/metrics/machine.py

@@ -35,6 +35,14 @@ async def machine_metrics(raw_data):
     mtbf = operating_time / downtime_count if downtime_count > 0 else pd.Timedelta(0)
     mttr = unplanned_stop_time / downtime_count if downtime_count > 0 else pd.Timedelta(0)
 
+    # Quality rate per tool ID
+    quality_by_tool = {}
+    for tool_id in [1, 2]:
+        tool_df = df[df["Tool ID"] == tool_id]
+        total = len(tool_df)
+        ok_count = (tool_df["Compliance"] == "OK").sum()
+        quality_by_tool[f"quality_rate_tool_{tool_id}"] = round(((ok_count / total) * 100), 2) if total > 0 else 0
+
     return {
         "opening_time": str(opening_time),
         "required_time": str(required_time),
@@ -42,10 +50,11 @@ async def machine_metrics(raw_data):
         "operating_time": str(operating_time),
         "net_time": str(net_time),
         "useful_time": str(useful_time),
-        "quality_rate": quality_rate,
-        "operating_rate": operating_rate,
-        "availability_rate": availability_rate,
-        "OEE": OEE,
+        "quality_rate": round(quality_rate, 2),
+        **quality_by_tool,
+        "operating_rate": round(operating_rate, 2),
+        "availability_rate": round(availability_rate, 2),
+        "OEE": round(OEE, 2),
         "MTBF": str(mtbf),
         "MTTR": str(mttr)
     }

src/ui/dashboard.py

@@ -31,7 +31,7 @@ async def dataflow(state):
         state['data']['tools'].setdefault(f'tool_{i}', pd.DataFrame())
 
     state['data'].setdefault('issues', {})
-    state.setdefault('efficiency', {})
+    state.setdefault('status', {})
 
     # Check running state
     if state.get('running'):
@@ -64,7 +64,7 @@ async def dataflow(state):
         ] + [
             pd.DataFrame(state['data']['issues'])
         ] + [
-            state['efficiency']
+            state['status']
         ]
     state['last_hash'] = current_hash
 
@@ -74,12 +74,38 @@ async def dataflow(state):
     for tool, df in tools_data.items():
         state['data']['tools'][tool] = df
 
+    # Get machine metrics
     machine_data = await machine_metrics(raw_data)
-    state['efficiency'] = machine_data
+    state['status'] = machine_data
 
+    # Get tools stats
+    for tool in ['tool_1', 'tool_2', 'all']:
+        df = state['data']['tools'].get(tool, pd.DataFrame())
+        if df.empty or 'Timestamp' not in df.columns:
+            continue
+
+        df = df.copy()
+        df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')
+        df.dropna(subset=['Timestamp'], inplace=True)
+
+        if df.empty:
+            continue
+
+        idx = df['Timestamp'].idxmax()
+
+        for cote in ['pos', 'ori']:
+            for metric_type in ['cp', 'cpk']:
+                column = f"{cote}_rolling_{metric_type}"
+                if column in df.columns:
+                    value = df.at[idx, column]
+                    key = f"{tool}_{metric_type}_{cote}"
+                    state['status'][key] = round(value, 4)
+
+    # Get issues
     issues = await fetch_issues(raw_data)
     state['data']['issues'] = issues
 
+    # Update situation
     return (
         [
             pd.DataFrame(state['data']['tools'].get(f'tool_{i}', pd.DataFrame()))
@@ -89,7 +115,7 @@ async def dataflow(state):
         ] + [
             pd.DataFrame(state['data']['issues'])
         ] + [
-            state['efficiency']
+            state['status']
         ]
     )
 
@@ -108,20 +134,18 @@ def init_components(n=TOOLS_COUNT):
     tool_plots = []
     general_plots = []
 
-    # Tool metrics displays and their plots
-    for i in range(1, n + 1):
+    for i in range(1, n + 1):                   # Tool metrics displays
         display = ToolMetricsDisplay()
         displays.append(display)
         tool_plots.extend(display.tool_block(df=pd.DataFrame(), id=i))
 
-    # General metrics display and its plots
-    main_display = GeneralMetricsDisplay()
+    main_display = GeneralMetricsDisplay()      # General metrics display
     displays.append(main_display)
     general_plots.extend(
             main_display.general_block(
             all_tools_df=pd.DataFrame(),
             issues_df=pd.DataFrame(),
-            efficiency_data={}
+            status={}
         )
     )
     return displays, tool_plots, general_plots
@@ -141,18 +165,16 @@ async def on_tick(state, displays):
         tool_dfs = data[:-3]             # all individual tool DataFrames
         all_tools_df = data[-3]          # 'all' tools DataFrame
         issues_df = data[-2]             # issues DataFrame
-        efficiency_data = data[-1]       # efficiency dict
+        status = data[-1]                # status dict
 
-        # General plots
-        general_display = displays[-1]
+        general_display = displays[-1]                      # General plots
         general_plots = general_display.refresh(
             all_tools_df=all_tools_df,
             issues_df=issues_df,
-            efficiency_data=efficiency_data
+            status=status
         )
 
-        # Tool-specific plots
-        tool_plots = []
+        tool_plots = []                                     # Tool-specific plots
         for df, display in zip(tool_dfs, displays[:-1]):
             tool_plots.extend(display.refresh(df=df))
 

src/ui/graphs/general_graphs.py

@@ -209,7 +209,7 @@ class GeneralMetricsDisplay:
         )
         return fig
 
-    def general_block(self, all_tools_df, issues_df, efficiency_data):
+    def general_block(self, all_tools_df, issues_df, status):
         header = f"Metrics Summary"
         html_content = f"""
         <div style="display: flex; align-items: center; justify-content: flex-start; width: 100%;">
@@ -225,24 +225,24 @@ class GeneralMetricsDisplay:
                     total_count = gr.Plot(
                         self.kpi_value(
                             value=self.get_max_part_id(all_tools_df),
-                            title="Total Count"
+                            title="Total Count (parts)"
                         )
                     )
                     total_time = gr.Plot(
                         self.kpi_value(
-                            value=efficiency_data.get("opening_time", "0 days 00:00:00"),
+                            value=status.get("opening_time", "0 days 00:00:00"),
                             title="Total Time"
                         )
                     )
                     mtbf_plot = gr.Plot(
                         self.kpi_value(
-                            value=efficiency_data.get("MTBF", "0 days 00:00:00"),
+                            value=status.get("MTBF", "0 days 00:00:00"),
                             title="MTBF"
                         )
                     )
                     mttr_plot = gr.Plot(
                         self.kpi_value(
-                            value=efficiency_data.get("MTTR", "0 days 00:00:00"),
+                            value=status.get("MTTR", "0 days 00:00:00"),
                             title="MTTR"
                         )
                     )
@@ -252,21 +252,21 @@ class GeneralMetricsDisplay:
                     with gr.Row(height=150):
                         oee_plot = gr.Plot(
                             self.kpi_rate(
-                                percentage=efficiency_data.get('OEE', 0),
+                                percentage=status.get('OEE', 0),
                                 title="OEE"
                             )
                         )
                     with gr.Row(height=150):
                         quality_rate_plot = gr.Plot(
                             self.kpi_rate(
-                                percentage=efficiency_data.get("quality_rate", 0),
+                                percentage=status.get("quality_rate", 0),
                                 title="Quality Rate"
                             )
                         )
                     with gr.Row(height=150):
                         availability_plot = gr.Plot(
                             self.kpi_rate(
-                                percentage=efficiency_data.get("availability_rate", 0),
+                                percentage=status.get("availability_rate", 0),
                                 title="Availability"
                             )
                         )
@@ -284,14 +284,14 @@ class GeneralMetricsDisplay:
         ]
         return self.plots
 
-    def refresh(self, all_tools_df, issues_df, efficiency_data):
+    def refresh(self, all_tools_df, issues_df, status):
         return [
-            self.kpi_value(value=self.get_max_part_id(all_tools_df), title="Total Count"),
-            self.kpi_value(value=efficiency_data.get("opening_time", "0 days 00:00:00"), title="Total Time"),
-            self.kpi_rate(percentage=efficiency_data.get('OEE', 0), title="OEE"),
-            self.kpi_rate(percentage=efficiency_data.get("quality_rate", 0), title="Quality Rate"),
-            self.kpi_rate(percentage=efficiency_data.get("availability_rate", 0), title="Availability"),
-            self.kpi_value(value=efficiency_data.get("MTBF", "0 days 00:00:00"), title="MTBF"),
-            self.kpi_value(value=efficiency_data.get("MTTR", "0 days 00:00:00"), title="MTTR"),
+            self.kpi_value(value=self.get_max_part_id(all_tools_df), title="Total Count (parts)"),
+            self.kpi_value(value=status.get("opening_time", "0 days 00:00:00"), title="Total Time"),
+            self.kpi_rate(percentage=status.get('OEE', 0), title="OEE"),
+            self.kpi_rate(percentage=status.get("quality_rate", 0), title="Quality Rate"),
+            self.kpi_rate(percentage=status.get("availability_rate", 0), title="Availability"),
+            self.kpi_value(value=status.get("MTBF", "0 days 00:00:00"), title="MTBF"),
+            self.kpi_value(value=status.get("MTTR", "0 days 00:00:00"), title="MTTR"),
             self.pareto(issues_df, error_col='Error Code')
         ]