add cache

app.py

@@ -45,45 +45,57 @@ city_county_name_map = cities['county_name'].to_dict()
 
 cached_report = None
 cache_time = 0
+reload_cache = False
+reload_every = 10 * 60  # 10 minutes
 
 def full_report():
-    client = BetaAnalyticsDataClient()
-
-    request = RunReportRequest(
-        property=f"properties/{PROPERTY_ID}",
-        dimensions=[Dimension(name="nthDay"),
-                    Dimension(name='eventName'),
-                    Dimension(name="continent"),
-                    Dimension(name="country"),
-                    Dimension(name="countryId"),
-                    Dimension(name="city")],
-        metrics=[Metric(name="eventValue")],
-        #return_property_quota=True,
-        date_ranges=[DateRange(start_date="2023-06-30", end_date="today")],
-    )
-    response = client.run_report(request)
-
-    res = {'day': [], 'jumps': [], 'continent': [], 'country': [], 'iso': [], 'city': []}
-
-    for row in response.rows:
-        event_name = row.dimension_values[1].value
-        if event_name == FINISHED_EXERCISE:
-            day = int(row.dimension_values[0].value)
-            continent = row.dimension_values[2].value
-            country = row.dimension_values[3].value
-            country_iso = row.dimension_values[4].value
-            city = row.dimension_values[5].value
-            event_value = float(row.metric_values[0].value)
-            res['day'].append(day)
-            res['jumps'].append(event_value)
-            res['continent'].append(continent)
-            res['country'].append(country)
-            res['iso'].append(country_iso)
-            res['city'].append(city)
+    global cached_report, cache_time, reload_cache
+    if time.time() - cache_time > reload_every:
+        reload_cache = False
+    if not reload_cache:
+        print("Loading report...")
+        reload_cache = True
+        cache_time = time.time()
+        client = BetaAnalyticsDataClient()
+
+        request = RunReportRequest(
+            property=f"properties/{PROPERTY_ID}",
+            dimensions=[Dimension(name="nthDay"),
+                        Dimension(name='eventName'),
+                        Dimension(name="continent"),
+                        Dimension(name="country"),
+                        Dimension(name="countryId"),
+                        Dimension(name="city")],
+            metrics=[Metric(name="eventValue")],
+            #return_property_quota=True,
+            date_ranges=[DateRange(start_date="2023-06-30", end_date="today")],
+        )
+        response = client.run_report(request)
+
+        res = {'day': [], 'jumps': [], 'continent': [], 'country': [], 'iso': [], 'city': []}
+
+        for row in response.rows:
+            event_name = row.dimension_values[1].value
+            if event_name == FINISHED_EXERCISE:
+                day = int(row.dimension_values[0].value)
+                continent = row.dimension_values[2].value
+                country = row.dimension_values[3].value
+                country_iso = row.dimension_values[4].value
+                city = row.dimension_values[5].value
+                event_value = float(row.metric_values[0].value)
+                res['day'].append(day)
+                res['jumps'].append(event_value)
+                res['continent'].append(continent)
+                res['country'].append(country)
+                res['iso'].append(country_iso)
+                res['city'].append(city)
+
+        df = pd.DataFrame.from_dict(res)
+        cached_report = df.copy(deep=True)
+    else:
+        print("Using cached report...")
+        df = cached_report.copy(deep=True)
 
-    
-
-    df = pd.DataFrame.from_dict(res)
     total_jumps = int(df['jumps'].sum())
     print(f"Total jumps: {total_jumps}")
     df['iso'] = df['iso'].map(alpha_2_map)
@@ -117,7 +129,6 @@ def full_report():
     county_df['county'] = county_df['city'].map(city_county_map)
     county_df['count_name'] = county_df['city'].map(city_county_name_map)
     county_df = county_df.groupby(['county', 'count_name']).sum().reset_index()
-    print(county_df)
 
     county_map = px.choropleth(county_df, geojson=counties, locations='county', color='jumps',
                            color_continuous_scale="plasma",