Add routes and functionality for inventory management, user management, worklogs, and settings

- Created a new Blueprint for main routes in `routes/__init__.py`.
- Implemented inventory listing and item management in `routes/inventory.py`.
- Added user listing and detail views in `routes/user.py`.
- Developed worklog listing and entry views in `routes/worklog.py`.
- Introduced search functionality across inventory, users, and worklogs in `routes/search.py`.
- Established settings management for brands, items, rooms, and functions in `routes/settings.py`.
- Enhanced helper functions for rendering headers and managing data in `routes/helpers.py`.
- Updated index route to display active worklogs and inventory conditions in `routes/index.py`.

routes/index.py

@@ -0,0 +1,86 @@
+from flask import render_template
+import pandas as pd
+
+from . import main
+from .helpers import worklog_headers
+from .. import db
+from ..models import WorkLog, Inventory
+from ..utils.load import eager_load_worklog_relationships, eager_load_inventory_relationships
+
+@main.route("/")
+def index():
+    worklog_query = eager_load_worklog_relationships(
+        db.session.query(WorkLog)
+    ).filter(
+        (WorkLog.complete == False)
+    )
+    
+    active_worklogs = worklog_query.all()
+
+    active_count = len(active_worklogs)
+    active_worklog_headers = {
+        k: v for k, v in worklog_headers.items()
+        if k not in ['End Time', 'Quick Analysis?', 'Complete?', 'Follow Up?']
+    }
+
+    inventory_query = eager_load_inventory_relationships(
+        db.session.query(Inventory)
+    )
+
+    results = inventory_query.all()
+    
+    data = [{
+        'id': item.id,
+        'condition': item.condition
+    } for item in results]
+
+    df = pd.DataFrame(data)
+
+    # Count items per condition
+    expected_conditions = [
+        'Deployed','Inoperable', 'Partially Inoperable',
+        'Unverified', 'Working'
+    ]
+    print(df)
+    if 'condition' in df.columns:
+        pivot = df['condition'].value_counts().reindex(expected_conditions, fill_value=0)
+    else:
+        pivot = pd.Series([0] * len(expected_conditions), index=expected_conditions)
+
+    # Convert pandas/numpy int64s to plain old Python ints
+    pivot = pivot.astype(int)
+    labels = list(pivot.index)
+    data = [int(x) for x in pivot.values]  
+
+    datasets = [{
+        'type': 'pie',
+        'labels': labels,
+        'values': data,
+        'name': 'Inventory Conditions'
+    }]
+
+    active_worklog_rows = []
+    for log in active_worklogs:
+        # Create a dictionary of {column name: cell dict}
+        cells_by_key = {k: fn(log) for k, fn in worklog_headers.items()}
+
+        # Use original, full header set for logic
+        highlight = cells_by_key.get("Follow Up?", {}).get("highlight", False)
+
+        # Use only filtered headers — and in exact order
+        cells = [cells_by_key[k] for k in active_worklog_headers]
+
+        active_worklog_rows.append({
+            "id": log.id,
+            "cells": cells,
+            "highlight": highlight
+        })
+
+    return render_template(
+        "index.html", 
+        active_count=active_count, 
+        active_worklog_headers=active_worklog_headers,
+        active_worklog_rows=active_worklog_rows,
+        labels=labels,
+        datasets=datasets
+    )