Add server file path import mode to bypass upload size limits

work_trace/wizards/wt_import_timeline_wizard.py

@@ -2,6 +2,7 @@ from odoo import models, fields, api, _
 from odoo.exceptions import UserError
 import json
 import base64
+import os
 import re
 from datetime import datetime, timedelta
 from math import radians, sin, cos, sqrt, atan2
@@ -41,7 +42,6 @@ def _get_travel_mode(activity_type):
 
 
 def _parse_latlng(latlng_str):
-    """Parse coordinate string like '30.0381046 deg, -95.5899101 deg' handling encoding issues."""
     nums = re.findall(r'-?\d+\.\d+', latlng_str)
     if len(nums) >= 2:
         return float(nums[0]), float(nums[1])
@@ -49,7 +49,6 @@ def _parse_latlng(latlng_str):
 
 
 def _parse_ts(ts_str):
-    """Parse ISO 8601 timestamp to naive datetime."""
     if not ts_str:
         return None
     try:
@@ -62,17 +61,25 @@ class WtImportTimelineWizard(models.TransientModel):
     _name = 'wt.import.timeline.wizard'
     _description = 'Import Google Timeline'
 
-    timeline_file = fields.Binary(string='Timeline JSON File', required=True)
+    import_mode = fields.Selection([
+        ('upload', 'Upload File'),
+        ('server_path', 'Server File Path'),
+    ], string='Import Mode', default='upload', required=True)
+
+    timeline_file = fields.Binary(string='Timeline JSON File')
     timeline_filename = fields.Char(string='Filename')
+    server_file_path = fields.Char(
+        string='Server File Path',
+        help='Full path to Timeline.json on the Odoo server (bypasses upload size limits)'
+    )
     min_stop_minutes = fields.Integer(
         string='Minimum Stop Duration (minutes)',
         default=5,
-        help='Ignore stops shorter than this duration'
     )
     proximity_meters = fields.Integer(
         string='Location Proximity (meters)',
         default=200,
-        help='For raw signal files only: GPS positions within this distance are grouped as one location'
+        help='For Timeline Edits.json only: GPS positions within this distance are grouped as one location'
     )
     geocode = fields.Boolean(
         string='Resolve Addresses via OpenStreetMap',
@@ -81,11 +88,27 @@ class WtImportTimelineWizard(models.TransientModel):
 
     def action_import(self):
         self.ensure_one()
-        try:
-            raw = base64.b64decode(self.timeline_file)
-            data = json.loads(raw)
-        except Exception as e:
-            raise UserError(_('Invalid JSON file: %s') % str(e))
+
+        # Load data from file upload or server path
+        if self.import_mode == 'server_path':
+            if not self.server_file_path:
+                raise UserError(_('Please enter the server file path.'))
+            path = self.server_file_path.strip()
+            if not os.path.exists(path):
+                raise UserError(_('File not found on server: %s') % path)
+            try:
+                with open(path, 'r', encoding='utf-8') as f:
+                    data = json.load(f)
+            except Exception as e:
+                raise UserError(_('Error reading file: %s') % str(e))
+        else:
+            if not self.timeline_file:
+                raise UserError(_('Please upload a Timeline JSON file.'))
+            try:
+                raw = base64.b64decode(self.timeline_file)
+                data = json.loads(raw)
+            except Exception as e:
+                raise UserError(_('Invalid JSON file: %s') % str(e))
 
         # Auto-detect format
         if 'semanticSegments' in data:
@@ -96,7 +119,7 @@ class WtImportTimelineWizard(models.TransientModel):
             raise UserError(_('Unrecognized format. Expected Timeline.json (semanticSegments) or Timeline Edits.json (timelineEdits).'))
 
         if not stops:
-            raise UserError(_('No location stops found in the uploaded file.'))
+            raise UserError(_('No location stops found in the file.'))
 
         # Filter by minimum stop duration
         min_secs = self.min_stop_minutes * 60
@@ -109,16 +132,14 @@ class WtImportTimelineWizard(models.TransientModel):
 
         stops.sort(key=lambda s: s['arrived_at'])
 
-        # Compute distances and travel times between consecutive stops
+        # Compute distances and travel times
         for i, stop in enumerate(stops):
             if i > 0:
                 prev = stops[i - 1]
-                if prev.get('lat') and stop.get('lat'):
-                    stop['distance_from_previous'] = _haversine_miles(
-                        prev['lat'], prev['lng'], stop['lat'], stop['lng']
-                    )
-                else:
-                    stop['distance_from_previous'] = 0.0
+                stop['distance_from_previous'] = (
+                    _haversine_miles(prev['lat'], prev['lng'], stop['lat'], stop['lng'])
+                    if prev.get('lat') and stop.get('lat') else 0.0
+                )
                 travel_delta = stop['arrived_at'] - prev['departed_at']
                 stop['travel_time_from_previous'] = max(travel_delta.total_seconds() / 3600, 0.0)
             else:
@@ -140,7 +161,6 @@ class WtImportTimelineWizard(models.TransientModel):
             if arrived_str in existing:
                 skipped += 1
                 continue
-
             log = LocationLog.create({
                 'date': stop['arrived_at'].date(),
                 'arrived_at': stop['arrived_at'],
@@ -174,31 +194,22 @@ class WtImportTimelineWizard(models.TransientModel):
         }
 
     def _parse_semantic_timeline(self, data):
-        """
-        Parse Timeline.json semanticSegments format.
-        Only 'visit' segments are location stops.
-        'timelinePath' segments are travel (ignored — distance calculated from stop coords).
-        """
         stops = []
         for seg in data.get('semanticSegments', []):
             visit = seg.get('visit')
             if not visit:
                 continue
-
             start_ts = _parse_ts(seg.get('startTime'))
             end_ts = _parse_ts(seg.get('endTime'))
             if not start_ts or not end_ts:
                 continue
-
             candidate = visit.get('topCandidate', {})
             semantic_type = candidate.get('semanticType', '')
             latlng_str = candidate.get('placeLocation', {}).get('latLng', '')
             lat, lng = _parse_latlng(latlng_str) if latlng_str else (None, None)
-
             category = SEMANTIC_TYPE_CATEGORY.get(semantic_type, '')
             if not category and semantic_type:
                 category = semantic_type.replace('_', ' ').title()
-
             stops.append({
                 'arrived_at': start_ts,
                 'departed_at': end_ts,
@@ -208,14 +219,11 @@ class WtImportTimelineWizard(models.TransientModel):
                 'category': category,
                 'travel_mode': 'unknown',
             })
-
         return stops
 
     def _parse_raw_timeline(self, data, proximity_meters=200):
-        """Parse Timeline Edits.json raw signal format using proximity clustering."""
         positions = []
         activities = []
-
         for entry in data.get('timelineEdits', []):
             raw = entry.get('rawSignal', {}).get('signal', {})
             if 'position' in raw:
@@ -251,7 +259,6 @@ class WtImportTimelineWizard(models.TransientModel):
 
         stops = []
         current_cluster = [positions[0]]
-
         for pos in positions[1:]:
             prev = current_cluster[-1]
             if _distance_meters(prev['lat'], prev['lng'], pos['lat'], pos['lng']) <= proximity_meters: