analytics / main.py
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import os
from datetime import datetime, timedelta
from fastapi import FastAPI, HTTPException, Query
from fastapi.middleware.cors import CORSMiddleware
from pymongo import MongoClient
from bson import ObjectId
from dotenv import load_dotenv
load_dotenv()
app = FastAPI(title="QuickTask Analytics Service")
# CORS middleware
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# MongoDB connection
client = MongoClient(os.getenv("MONGO_URI"))
db = client.quicktask
tasks_collection = db.tasks
@app.get("/")
async def root():
return {"message": "QuickTask Analytics Service API"}
@app.get("/analytics/stats/{user_id}")
async def get_user_stats(user_id: str):
try:
user_oid = ObjectId(user_id)
# Total tasks
total_tasks = tasks_collection.count_documents({"user": user_oid})
if total_tasks == 0:
return {
"total_tasks": 0,
"avg_completion_time_hrs": 0,
"overdue_tasks": 0,
"productivity_score": 0
}
# Completed tasks
completed_tasks = list(tasks_collection.find({"user": user_oid, "status": "completed"}))
completed_count = len(completed_tasks)
# Average completion time
total_completion_time_sec = 0
for task in completed_tasks:
# Re-calculating from timestamps
created_at = task.get("createdAt")
updated_at = task.get("updatedAt")
if created_at and updated_at:
diff = (updated_at - created_at).total_seconds()
total_completion_time_sec += diff
avg_completion_time = (total_completion_time_sec / completed_count / 3600) if completed_count > 0 else 0
# Overdue tasks
now = datetime.now()
overdue_tasks = tasks_collection.count_documents({
"user": user_oid,
"status": {"$ne": "completed"},
"dueDate": {"$lt": now}
})
# Productivity score
productivity_score = (completed_count / total_tasks * 100)
return {
"total_tasks": total_tasks,
"avg_completion_time_hrs": round(avg_completion_time, 2),
"overdue_tasks": overdue_tasks,
"productivity_score": round(productivity_score, 2)
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/analytics/trends/{user_id}")
async def get_productivity_trends(user_id: str, days: int = Query(7, ge=1, le=30)):
try:
user_oid = ObjectId(user_id)
end_date = datetime.now()
start_date = end_date - timedelta(days=days)
# Aggregate tasks completed per day
pipeline = [
{
"$match": {
"user": user_oid,
"status": "completed",
"updatedAt": {"$gte": start_date, "$lte": end_date}
}
},
{
"$group": {
"_id": {
"$dateToString": {"format": "%Y-%m-%d", "date": "$updatedAt"}
},
"count": {"$sum": 1}
}
},
{"$sort": {"_id": 1}}
]
results = list(tasks_collection.aggregate(pipeline))
# Fill in missing dates
trend_data = []
current_date = start_date
results_dict = {item["_id"]: item["count"] for item in results}
while current_date <= end_date:
date_str = current_date.strftime("%Y-%m-%d")
trend_data.append({
"date": date_str,
"count": results_dict.get(date_str, 0)
})
current_date += timedelta(days=1)
return trend_data
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
import uvicorn
port = int(os.getenv("PORT", 8000))
uvicorn.run(app, host="0.0.0.0", port=port)