more

README.md

@@ -12,3 +12,18 @@ short_description: 'Solve your MAPF problem in one click '
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+## TODO:
+- [ ] Upload perfect heuristics from MongoDB
+- [ ] Create perfect heuristic and update MongoDB
+- [ ] Solve with PrP with the full UI output
+- [ ] Add an option for creating SV sets (also with MongoDB involved)
+- [ ] Add all other algorithms to the stack via MCP
+- [ ] Build a LangGraph flow to decide which algorithm to use
+
+## FINISHED:
+- [x] Add a way to upload a map
+- [x] Add a way to upload a scenario
+- [x] Add a way to upload a solution
+

functions.py

@@ -1,5 +1,6 @@
 import re
 import numpy as np
+import json
 
 def get_dims_from_pic(file_dir):
     with open(f'{file_dir}') as f:
@@ -18,4 +19,13 @@ def get_np_from_dot_map(file_dir):
             for width_index, curr_str in enumerate(line):
                 if curr_str == '.':
                     img_np[height_index, width_index] = 1
-        return img_np, (height, width)
+        return img_np, (height, width)
+
+
+
+def load_locations(loc_string: str):
+    try:
+        loc_dict = json.loads(loc_string)
+        return loc_dict
+    except json.JSONDecodeError:
+        return 'None'

mongodb_draft.ipynb

@@ -0,0 +1,211 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-06-26T10:20:11.807176Z",
+     "start_time": "2025-06-26T10:20:11.800149Z"
+    }
+   },
+   "source": [
+    "\n",
+    "from pymongo.mongo_client import MongoClient\n",
+    "from pymongo.server_api import ServerApi\n",
+    "from dotenv import load_dotenv\n",
+    "load_dotenv()\n",
+    "import os\n",
+    "import json"
+   ],
+   "outputs": [],
+   "execution_count": 9
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-26T10:16:45.879242Z",
+     "start_time": "2025-06-26T10:16:45.876031Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "uri = os.getenv(\"MONGODB_URI\")",
+   "id": "2d2114f7ef19f868",
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-26T10:16:47.798644Z",
+     "start_time": "2025-06-26T10:16:46.158912Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "# Create a new client and connect to the server\n",
+    "client = MongoClient(uri, server_api=ServerApi('1'))\n",
+    "\n",
+    "# Send a ping to confirm a successful connection\n",
+    "try:\n",
+    "    client.admin.command('ping')\n",
+    "    print(\"Pinged your deployment. You successfully connected to MongoDB!\")\n",
+    "except Exception as e:\n",
+    "    print(e)"
+   ],
+   "id": "11b5d4e1c554d3af",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pinged your deployment. You successfully connected to MongoDB!\n"
+     ]
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-26T10:18:48.684105Z",
+     "start_time": "2025-06-26T10:18:48.542880Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "client.list_database_names()",
+   "id": "b26f8f320fe570f4",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['admin', 'local']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-26T13:51:22.341078Z",
+     "start_time": "2025-06-26T13:51:22.338610Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "my_db = client[\"MAPF_Solver\"]\n",
+    "perfect_heuristics_col = my_db[\"perfect_heuristics\"]"
+   ],
+   "id": "fd5280e0c814c23d",
+   "outputs": [],
+   "execution_count": 35
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-26T13:51:22.581004Z",
+     "start_time": "2025-06-26T13:51:22.578972Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "",
+   "id": "9404c7961949bcaa",
+   "outputs": [],
+   "execution_count": 36
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-26T13:56:03.654179Z",
+     "start_time": "2025-06-26T13:55:54.602665Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# map_name = 'empty-32-32'\n",
+    "# map_name = 'maze-32-32-2'\n",
+    "# map_name = 'maze-32-32-4'\n",
+    "# map_name = 'random-32-32-10'\n",
+    "# map_name = 'random-32-32-20'\n",
+    "map_name = 'room-32-32-4'\n",
+    "\n",
+    "full_dir = f'logs_for_heuristics/h_dict_of_{map_name}.json'\n",
+    "with open(full_dir, \"r\") as f:\n",
+    "    h_dict = json.load(f)\n",
+    "    exists = perfect_heuristics_col.find_one({\"name\": map_name}) is not None\n",
+    "    if not exists:\n",
+    "        my_db[\"perfect_heuristics\"].insert_one({\n",
+    "            'name': map_name,\n",
+    "            'h_dict': h_dict\n",
+    "        })\n",
+    "    else:\n",
+    "        print(f'The perfect heuristic of {map_name} already exists in the database.')"
+   ],
+   "id": "368e02123432350d",
+   "outputs": [],
+   "execution_count": 49
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-26T13:52:27.343992Z",
+     "start_time": "2025-06-26T13:52:24.754651Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "doc = perfect_heuristics_col.find_one({\"name\": map_name})",
+   "id": "c5e5ec57cefdbfe0",
+   "outputs": [],
+   "execution_count": 40
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-26T13:52:34.053818Z",
+     "start_time": "2025-06-26T13:52:34.042441Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "h_dict = doc['h_dict']",
+   "id": "c8ebf6cb4e0133b8",
+   "outputs": [],
+   "execution_count": 41
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "29bf5dd9783e2fcd"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

mongodb_drafts.py

@@ -0,0 +1,17 @@
+
+from pymongo.mongo_client import MongoClient
+from pymongo.server_api import ServerApi
+from dotenv import load_dotenv
+load_dotenv()
+import os
+
+uri = os.getenv("MONGODB_URI")
+# Create a new client and connect to the server
+client = MongoClient(uri, server_api=ServerApi('1'))
+
+# Send a ping to confirm a successful connection
+try:
+    client.admin.command('ping')
+    print("Pinged your deployment. You successfully connected to MongoDB!")
+except Exception as e:
+    print(e)