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{
"cells": [
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"import gplace\n",
"\n",
"location = \"13.744677,100.5295593\" # Latitude and Longitude\n",
"keyword = \"ร้านกาแฟ\"\n",
"result = gplace.nearby_search(keyword, location)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"from typing import TypedDict, Optional\n",
"\n",
"class NearbyDenseCommunityInput(TypedDict):\n",
" location_name: str\n",
" radius: int"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"def find_place_from_text(location:str):\n",
" \"\"\"Finds a place and related data from the query text\"\"\"\n",
" \n",
" result = gplace.find_place_from_text(location)\n",
" r = result['candidates'][0]\n",
" return f\"\"\"\n",
" address: {r['formatted_address']}\\n\n",
" location: {r['geometry']['location']}\\n\n",
" name: {r['name']}\\n\n",
" opening hours: {r['opening_hours']}\\n\n",
" rating: {r['rating']}\\n\n",
" \"\"\"\n",
" \n",
"def nearby_search(keyword:str, location:str, radius=2000, place_type=None):\n",
" \"\"\"Searches for many places nearby the location based on a keyword. using keyword like \\\"coffee shop\\\", \\\"restaurants\\\". radius is the range to search from the location\"\"\"\n",
" location = gplace.find_location(location, radius=radius)\n",
" result = gplace.nearby_search(keyword, location, radius)\n",
" \n",
" strout = \"\"\n",
" for r in result:\n",
" # Use .get() to handle missing keys\n",
" address = r.get('vicinity', 'N/A')\n",
" location_info = r.get('geometry', {}).get('location', 'N/A')\n",
" name = r.get('name', 'N/A')\n",
" opening_hours = r.get('opening_hours', 'N/A')\n",
" rating = r.get('rating', 'N/A')\n",
" plus_code = r.get('plus_code', {}).get('global_code', 'N/A')\n",
" \n",
" strout += f\"\"\"\n",
" address: {address}\\n\n",
" location: {location_info}\\n\n",
" name: {name}\\n\n",
" opening hours: {opening_hours}\\n\n",
" rating: {rating}\\n\n",
" plus code: {plus_code}\\n\\n\n",
" \"\"\"\n",
" return strout\n",
"\n",
"def nearby_dense_community(input_dict: NearbyDenseCommunityInput) -> str:\n",
" \"\"\" getting nearby dense community such as (community mall, hotel, school, etc), by location name, radius(in meters)\n",
" return list of location community nearby, name, community type.\n",
" \"\"\"\n",
" location = input_dict['location_name']\n",
" radius = input_dict['radius']\n",
" \n",
" location_coords = gplace.find_location(location, radius=radius)\n",
" result = gplace.nearby_dense_community(location_coords, radius)\n",
" \n",
" strout = \"\"\n",
" for r in result:\n",
" # Use .get() to handle missing keys\n",
" address = r.get('vicinity', 'N/A')\n",
" location_types = r.get('types', 'N/A')\n",
" name = r.get('name', 'N/A')\n",
" opening_hours = r.get('opening_hours', 'N/A')\n",
" rating = r.get('rating', 'N/A')\n",
" plus_code = r.get('plus_code', {}).get('global_code', 'N/A')\n",
" \n",
" strout += f\"\"\"\n",
" name: {name}\\n\n",
" types: {location_types}\\n\n",
" \"\"\"\n",
" return strout\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# gplace_tools.py\n",
"from langgraph.prebuilt import ToolNode\n",
"from langchain_core.tools import tool\n",
"from langchain_core.tools import Tool\n",
"from langchain_google_community import GoogleSearchAPIWrapper\n",
"from langchain_community.document_loaders import WebBaseLoader\n",
"\n",
"import utils\n",
"\n",
"utils.load_env()\n",
"\n",
"search = GoogleSearchAPIWrapper()\n",
"\n",
"find_place_from_text = tool(find_place_from_text)\n",
"nearby_search = tool(nearby_search)\n",
"google_search = Tool(\n",
" name=\"google_search\",\n",
" description=\"Search Google for recent results.\",\n",
" func=search.run,\n",
")\n",
"web_loader = Tool(\n",
" name=\"google_search\",\n",
" description=\"Search Google for recent results.\",\n",
" func=WebBaseLoader,\n",
")\n",
"\n",
"tools = [find_place_from_text, nearby_search]\n",
"\n",
"# Create ToolNodes for each tool\n",
"tool_node = ToolNode(tools)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"\\n name: Bangkok\\n\\n types: ['locality', 'political']\\n\\n \\n name: Metropoint Bangkok Hotel\\n\\n types: ['lodging', 'point_of_interest', 'establishment']\\n\\n \\n name: The Grand Fourwings Convention Hotel\\n\\n types: ['lodging', 'point_of_interest', 'establishment']\\n\\n \\n name: Hua Mak Indoor Stadium\\n\\n types: ['point_of_interest', 'establishment']\\n\\n \\n name: B2 Bangkok Srinagarindra Boutique & Budget Hotel\\n\\n types: ['clothing_store', 'lodging', 'point_of_interest', 'store', 'establishment']\\n\\n \\n name: HappyLand Mansion\\n\\n types: ['lodging', 'point_of_interest', 'establishment']\\n\\n \\n name: Bangkok Swimming by Kru Jin\\n\\n types: ['point_of_interest', 'establishment']\\n\\n \\n name: Aunchaleena grand Hotel\\n\\n types: ['lodging', 'point_of_interest', 'establishment']\\n\\n \\n name: Anda Hotel\\n\\n types: ['lodging', 'point_of_interest', 'establishment']\\n\\n \\n name: Grand Mandarin Residence\\n\\n types: ['lodging', 'point_of_interest', 'establishment']\\n\\n \\n name: Wallada Place Hotel\\n\\n types: ['lodging', 'point_of_interest', 'establishment']\\n\\n \\n name: NIDA Rooms Plubpla Bangkapi 591\\n\\n types: ['lodging', 'point_of_interest', 'establishment']\\n\\n \\n name: Bangkok Interplace\\n\\n types: ['lodging', 'restaurant', 'food', 'point_of_interest', 'establishment']\\n\\n \\n name: Vejthani Hospital\\n\\n types: ['hospital', 'doctor', 'point_of_interest', 'health', 'establishment']\\n\\n \\n name: โรงแรม ชาลีน่า ปริ้นเซส Chaleena princess\\n\\n types: ['lodging', 'point_of_interest', 'establishment']\\n\\n \\n name: Royal Pimand\\n\\n types: ['lodging', 'point_of_interest', 'establishment']\\n\\n \\n name: Baron Residence Hotel\\n\\n types: ['lodging', 'point_of_interest', 'establishment']\\n\\n \\n name: Ridwanun Islam Mosque\\n\\n types: ['mosque', 'place_of_worship', 'point_of_interest', 'establishment']\\n\\n \\n name: Thep Phanom Building\\n\\n types: ['point_of_interest', 'establishment']\\n\\n \\n name: Bang Kapi District\\n\\n types: ['sublocality_level_1', 'sublocality', 'political']\\n\\n \""
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nearby_dense_community({'location_name': 'ลุมพินี เซ็นเตอร์ ลาดพร้าว', 'radius': 8000})"
]
}
],
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"file_extension": ".py",
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