Usage Analysis Beam App

ai_ta_backend/beam/usage_analysis.py

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+"""
+To deploy: beam deploy usage_analysis.py
+For local testing w/ hot reload: beam serve usage_analysis.py
+Use CAII gmail to auth.
+"""
+
+import os
+from datetime import datetime
+from typing import Any, Dict, List
+
+import beam
+import supabase
+from beam import App, Runtime
+from posthog import Posthog
+
+requirements = [
+    "posthog==3.1.0",
+    "supabase==2.0.2",
+]
+
+app = App("usage_analysis",
+          runtime=Runtime(
+              cpu=1,
+              memory="2Gi",
+              image=beam.Image(
+                  python_version="python3.10",
+                  python_packages=requirements,
+              ),
+          ))
+
+
+@app.schedule(when="2 2 * * *")  # 2:02 AM daily
+def usage_analysis(**inputs: Dict[str, Any]):
+  """
+    This function queries supabase for the latest usage data and sends it to Posthog.
+    Details to report: 
+    Args:
+        course_name (str): The name of the course.
+    """
+  print("Running usage analysis for course")
+  posthog = Posthog(sync_mode=True, project_api_key=os.environ['POSTHOG_API_KEY'], host='https://app.posthog.com')
+  supabase_client = supabase.create_client(os.environ['SUPABASE_URL'], os.environ['SUPABASE_API_KEY'])  # type: ignore
+  print("Instantiated Posthog and Supabase clients")
+
+  posthog.capture('distinct_id_of_the_user', event='usage_analysis_ran', properties={
+      'hi_josh': "👋",
+  })
+
+  # if course_name:
+  #     # single course
+  #     print("Single course")
+  #     metrics = get_usage_data(course_name, supabase)
+  #     print("Metrics:", metrics)
+
+  #     posthog.capture('usage_metrics', metrics)
+
+  #     # upload to Supabase
+  #     response = supabase.table('usage_metrics').insert(metrics).execute()
+  #     print("Response:", response)
+
+  # else:
+  #     # all courses
+  #     print("All courses")
+
+  return "Success"
+
+
+def get_usage_data(course_name, supabase_client) -> Dict[str, Any]:
+  """
+    Get usage data from Supabase.
+    """
+  # get total documents
+  total_docs = supabase_client.table('documents').select('id', count='exact').eq('course_name', course_name).execute()
+  print("Total docs:", total_docs.count)
+
+  # get total conversations
+  total_conversations = supabase_client.table('llm-convo-monitor').select('id',
+                                                                          count='exact').eq('course_name',
+                                                                                            course_name).execute()
+  print("Total conversations:", total_conversations.count)
+
+  # get most recent conversation
+  most_recent_conversation = supabase_client.table('llm-convo-monitor').select('id', 'created_at').eq(
+      'course_name', course_name).order('created_at', desc=True).limit(1).execute()
+  print("Most recent conversation:", most_recent_conversation)
+
+  # extract datetime
+  print(type(most_recent_conversation.data[0]['created_at']))
+  dt_str = most_recent_conversation.data[0]['created_at']
+  dt_object = datetime.fromisoformat(dt_str)
+
+  metrics = {
+      "course_name": course_name,
+      "total_docs": total_docs.count,
+      "total_convos": total_conversations.count,
+      "most_recent_convo": dt_str,
+  }
+
+  return metrics