init commit

app.py

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+from google.analytics.data_v1beta import BetaAnalyticsDataClient
+from google.analytics.data_v1beta.types import (
+    DateRange,
+    Dimension,
+    Metric,
+    RunReportRequest,
+    RunRealtimeReportRequest
+)
+
+import gradio as gr
+import os
+import json
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+plt.style.use('fivethirtyeight')
+
+FINISHED_EXERCISE = 'finished_exercise'
+PROPERTY_ID = "384068977"
+
+credentials_json = os.environ['GOOGLE_APPLICATION_CREDENTIALS_JSON']
+credentials_dict = json.loads(credentials_json)
+# write json to file
+with open('credentials.json', 'w') as f:
+    json.dump(credentials_dict, f)
+# set env var to filename
+os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = os.path.join(os.path.dirname(__file__), 'credentials.json')
+
+
+def full_report():
+    client = BetaAnalyticsDataClient()
+
+    request = RunReportRequest(
+        property=f"properties/{PROPERTY_ID}",
+        dimensions=[Dimension(name="hour"),
+                    Dimension(name="nthDay"),
+                    Dimension(name='eventName'),
+                    Dimension(name="continent"),
+                    Dimension(name="country")],
+        metrics=[Metric(name="eventValue")],
+        #return_property_quota=True,
+        date_ranges=[DateRange(start_date="2023-06-30", end_date="today")],
+    )
+    response = client.run_report(request)
+
+    print(response)
+
+    
+
+    total_jumps = 0
+    res = {'day': [], 'jumps': [], 'continent': [], 'country': []}
+
+    for row in response.rows:
+        date = row.dimension_values[0].value  # YYYMMDD (e.g 20230715)
+        day = int(row.dimension_values[1].value)
+        event_name = row.dimension_values[2].value
+        continent = row.dimension_values[3].value
+        country = row.dimension_values[4].value
+        if event_name == FINISHED_EXERCISE:
+            event_value = float(row.metric_values[0].value)
+            total_jumps += int(event_value)
+            res['day'].append(day)
+            res['jumps'].append(event_value)
+            res['continent'].append(continent)
+            res['country'].append(country)
+
+    print(f"Total jumps: {total_jumps}")
+
+    df = pd.DataFrame.from_dict(res)
+    country_df = df.groupby(['country']).sum().reset_index()
+    country_df = country_df.sort_values(by=['jumps'], ascending=False)
+    print(country_df)
+
+    country_avg = df.groupby(['country']).mean().reset_index()
+    country_avg = country_avg.sort_values(by=['jumps'], ascending=False)
+    print(country_avg)
+
+    return f"Total Jumps: {total_jumps}"
+
+
+with gr.Blocks(theme='WeixuanYuan/Soft_dark') as demo:
+    with gr.Row():
+        total_jumps_label = gr.Markdown("Total Jumps: 0")
+        out_plot = gr.Plot(label="Total Jumps")
+
+    dep = demo.load(full_report, None, total_jumps_label, every=1)
+
+if __name__ == "__main__":
+    demo.queue(api_open=True, max_size=15).launch(share=False)

requirements.txt

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+google-analytics-data
+pandas<2.0.0
+seaborn
+matplotlib
+plotly