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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+screen_shot.jpg filter=lfs diff=lfs merge=lfs -text
+未命名文件夹/screen_shot_new.jpg filter=lfs diff=lfs merge=lfs -text

1_SpatialParse.py

@@ -0,0 +1,365 @@
+import streamlit as st
+from spacy import displacy
+import spacy
+import geospacy
+from PIL import Image
+import base64
+import sys
+import pandas as pd
+import en_core_web_md
+from spacy.tokens import Span, Doc, Token
+from utils import geoutil
+from utils import llm_coding
+import urllib.parse
+import json
+
+colors = {'GPE': "#43c6fc", "LOC": "#fd9720", "RSE":"#a6e22d"}
+options = {"ents": ['GPE', 'LOC', "RSE"], "colors": colors}
+
+
+HTML_WRAPPER = """<div style="overflow-x: auto; border: none solid #a6e22d; border-radius: 0.25rem; padding: 1rem">{}</div>"""
+model = ""
+
+gpe_selected = "GPE"
+loc_selected = "LOC"
+rse_selected = "RSE"
+
+types = ""
+
+#BASE_URL = "http://localhost:8080/"
+BASE_URL = ""
+
+
+
+def set_header():
+    LOGO_IMAGE = "tetis-1.png"
+
+    st.markdown(
+        """
+        <style>
+        .container {
+            display: flex;
+        }
+        .logo-text {
+            font-weight:700 !important;
+            font-size:50px !important;
+            color: #f9a01b !important;
+            padding-left: 10px !important;
+        }
+        .logo-img {
+            float:right;
+            width: 28%;
+            height: 28%;
+        }
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+    st.markdown(
+        f"""
+        <div class="container">
+            <img class="logo-img" src="data:image/png;base64,{base64.b64encode(open(LOGO_IMAGE, "rb").read()).decode()}">
+            <p class="logo-text">GeOspaCy</p>
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
+
+
+def set_side_menu():
+    global gpe_selected, loc_selected, rse_selected, model, types
+    types =""
+    params = st.query_params
+    st.sidebar.markdown("## Spacy Model")
+    st.sidebar.markdown("You can **select** the values of the *spacy model* from Dropdown.")
+    models = ['en_core_web_sm', 'en_core_web_md', 'en_core_web_lg', 'en_core_web_trf']
+    if "model" in params:
+        default_ix = models.index(params["model"][0])
+    else:
+        default_ix = models.index('en_core_web_sm')
+    model = st.sidebar.selectbox('Spacy Model',models, index=default_ix)
+
+    st.sidebar.markdown("## Spatial Entity Labels")
+    st.sidebar.markdown("**Mark** the Spatial Entities you want to extract?")
+    tpes = ""
+    if "type" in params:
+        tpes = params['type'][0]
+
+    if "g" in tpes:
+        gpe = st.sidebar.checkbox('GPE', value = True)
+    else:
+        gpe = st.sidebar.checkbox('GPE')
+
+    if "l" in tpes:
+        loc = st.sidebar.checkbox('LOC', value = True)
+    else:
+        loc = st.sidebar.checkbox('LOC')
+    if "r" in tpes:
+        rse = st.sidebar.checkbox('RSE', value = True)
+    else:
+        rse = st.sidebar.checkbox('RSE')
+    if(gpe):
+        gpe_selected ="GPE"
+        types+="g"
+
+    if(loc):
+        loc_selected ="LOC"
+        types+="l"
+
+    if(rse):
+        rse_selected ="RSE"
+        types+="r"
+
+
+
+def set_input():
+    params = st.query_params
+
+    # 设置默认文本
+    default_text = params["text"][0] if "text" in params else ""
+
+    # 更友好的提示语
+    # text = st.text_area("Enter a location description to extract spatial entities:", default_text)
+    text = st.text_area("✍️ **Please input your text here:**", default_text)
+    # text = st.text_area("### Please input your text here:", default_text)
+
+    # 提交按钮
+    if st.button("Extract"):
+        return text
+
+
+def set_selected_entities(doc):
+    global gpe_selected, loc_selected, rse_selected, model
+    ents = [ent for ent in doc.ents if ent.label_ == gpe_selected or ent.label_ == loc_selected or ent.label_ == rse_selected]
+
+    doc.ents = ents
+    return doc
+
+def extract_spatial_entities(text):
+    nlp = spacy.load("en_core_web_md")
+    nlp.add_pipe("spatial_pipeline", after="ner")
+    doc = nlp(text)
+
+    # 分句处理
+    sent_ents = []
+    sent_texts = []
+    sent_rse_id = []
+    offset = 0
+    sent_start_positions = [0]
+    doc_copy = doc.copy()
+    for sent in doc.sents:
+
+        sent_doc = nlp(sent.text)
+        sent_doc = set_selected_entities(sent_doc)
+        sent_texts.append(sent_doc.text)
+
+        for ent in sent_doc.ents:
+            sent_rse_id.append(ent._.rse_id)
+        for ent in sent_doc.ents:
+            new_ent = Span(doc, ent.start + offset, ent.end + offset, label=ent.label_)
+            sent_ents.append(new_ent)
+
+        offset += len(sent)
+        sent_start_positions.append(sent_start_positions[-1] + len(sent))
+    # **创建新 Doc**
+    final_doc = Doc(nlp.vocab, words=[token.text for token in doc], spaces=[token.whitespace_ for token in doc])
+    for i in sent_start_positions:
+        if i < len(final_doc):
+            final_doc[i].is_sent_start = True
+
+    final_doc.set_ents(sent_ents)
+
+    for i in range(len(sent_rse_id)):
+        final_doc.ents[i]._.rse_id = sent_rse_id[i]
+    doc = final_doc
+    doc.to_disk("saved_doc.spacy")
+
+    highlight_ents = []
+    if 'g' in types:
+        highlight_ents.append('GPE')
+    if 'l' in types:
+        highlight_ents.append('LOC')
+    if 'r' in types:
+        highlight_ents.append('RSE')
+
+    options = {"ents": highlight_ents, "colors": colors}
+    html = displacy.render(doc, style="ent", options=options)
+    html = html.replace("\n","")
+    st.write(HTML_WRAPPER.format(html),unsafe_allow_html=True)
+    show_spatial_ent_table(doc, text)
+
+    show_sentence_selector_table(doc_copy)
+
+def show_sentence_selector_table(doc_copy):
+    text = doc_copy.text
+    st.markdown("**______________________________________________________________________________________**")
+    st.markdown("**LLM-generated Spatial Composition**")
+
+
+    combo_obj = llm_coding.llmapi(text)
+
+    combo_lines = [json.dumps(item, separators=(", ", ": ")) for item in combo_obj]
+
+    combo_str = json.dumps(combo_obj)
+    combo_encoded = urllib.parse.quote(combo_str)
+    combo_encoded = urllib.parse.quote(combo_str)
+    text_encoded = urllib.parse.quote(text)
+    url = f"{BASE_URL}Locate?mode=geocombo&text={text_encoded}&combo={combo_encoded}"
+
+    rows = [{
+        'LLM Output': f'<pre>{combo_str}</pre>',
+        'Action': f'<a target="_self" href="{url}">Use this spatial composition</a>'
+    }]
+
+    df = pd.DataFrame(rows)
+    custom_style = """
+            <style>
+            table {
+                text-align: left !important;
+            }
+            th, td {
+                text-align: left !important;
+            }
+            </style>
+        """
+    st.markdown(custom_style, unsafe_allow_html=True)
+    st.write(df.to_html(escape=False, index=False), unsafe_allow_html=True)
+
+
+def show_spatial_ent_table(doc, text):
+    global types
+    if len(doc.ents) > 0:
+        st.markdown("**______________________________________________________________________________________**")
+        st.markdown("**Spatial Entities List**")
+
+        df = pd.DataFrame(columns=['Sr.', 'entity', 'label', 'Map', 'GEOJson'])
+        rows = []  # 用于存储所有行
+
+        for ent in doc.ents:
+            url_map = BASE_URL + "Locate?map=true&type=" + types + "&model=" + model + "&text=" + text + "&entity=" + ent._.rse_id
+            url_json = BASE_URL + "Locate?geojson=true&type=" + types + "&model=" + model + "&text=" + text + "&entity=" + ent._.rse_id
+
+            new_row = {
+                'Sr.': len(rows) + 1,
+                'entity': ent.text,
+                'label': ent.label_,
+                'Map': f'<a target="_self" href="{url_map}">View</a>',
+                'GEOJson': f'<a target="_self" href="{url_json}">View</a>'
+            }
+            rows.append(new_row)
+
+        df = pd.DataFrame(rows)
+
+        st.write(df.to_html(escape=False, index=False), unsafe_allow_html=True)
+
+
+def set_header():
+    # LOGO_IMAGE = "title.jpg"
+
+    st.markdown(
+        """
+        <style>
+        .container {
+            display: flex;
+        }
+        .logo-text {
+            font-weight:700 !important;
+            font-size:50px !important;
+            color: #52aee3 !important;
+            padding-left: 10px !important;
+        }
+        .logo-img {
+            float:right;
+            width: 10%;
+            height: 10%;
+        }
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+    st.markdown(
+        """
+        <div class="container">
+            <p class="logo-text">SpatialParse</p>
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
+
+
+def set_side_menu():
+    global gpe_selected, loc_selected, rse_selected, model, types
+    types = ""
+    params = st.query_params
+    st.sidebar.markdown("## Deployment Method")
+    st.sidebar.markdown("You can select the deployment method for the model.")
+    deployment_options = ["API", "Local deployment"]
+    use_local_model = st.sidebar.radio("Choose deployment method:", deployment_options, index=0) == "Local deployment"
+
+    if use_local_model:
+        local_model_path = st.sidebar.text_input("Enter local model path:", "")
+
+    st.sidebar.markdown("## LLM Model")
+    st.sidebar.markdown("You can **select** different  *LLM model* powered by API.")
+    models = ['Llama-3-8B', 'Mistral-7B-0.3', 'Gemma-2-10B', 'GPT-4o', 'Gemini Pro', 'Deepseek-R1', 'en_core_web_sm', 'en_core_web_md', 'en_core_web_lg', 'en_core_web_trf']
+
+    if "model" in params:
+        default_ix = models.index(params["model"][0])
+    else:
+        default_ix = models.index('GPT-4o')
+
+    model = st.sidebar.selectbox('LLM Model', models, index=default_ix)
+
+    st.sidebar.markdown("## Spatial Entity Labels")
+
+    st.sidebar.markdown("Please **Mark** the Spatial Entities you want to extract.")
+    tpes = ""
+    if "type" in params:
+        tpes = params['type'][0]
+
+    # st.sidebar.markdown("### Absolute Spatial Entity:")
+    if "g" in tpes:
+        gpe = st.sidebar.checkbox('GPE', value=True)
+    else:
+        gpe = st.sidebar.checkbox('GPE')
+
+    if "l" in tpes:
+        loc = st.sidebar.checkbox('LOC', value=True)
+    else:
+        loc = st.sidebar.checkbox('LOC')
+
+    st.sidebar.markdown("### Relative Spatial Entity:")
+
+    if "r" in tpes:
+        rse = st.sidebar.checkbox('RSE', value=True)
+    else:
+        rse = st.sidebar.checkbox('RSE')
+    if (gpe):
+        gpe_selected = "GPE"
+        types += "g"
+
+    if (loc):
+        loc_selected = "LOC"
+        types += "l"
+
+    if (rse):
+        rse_selected = "RSE"
+        types += "r"
+
+
+def main():
+    global gpe_selected, loc_selected, rse_selected, model
+    set_header()
+    set_side_menu()
+    text = set_input()
+    if(text is not None):
+        extract_spatial_entities(text)
+    elif "text" in st.session_state:
+        text = st.session_state.text
+        extract_spatial_entities(text)
+
+
+if __name__ == '__main__':
+    main()
+
+

B.geojson

@@ -0,0 +1 @@
+{"type": "FeatureCollection", "features": [{"type": "Feature", "id": "B", "properties": {"centroid": [150.40522373235163, -33.60974105]}, "geometry": {"type": "Polygon", "coordinates": [[[150.1281731, -33.1789387, 233.36930554542403], [150.1762383, -33.8726954, 135.13976188358015], [150.4714959, -33.9553209, 77.65215095718821], [150.6115716, -33.8875169, 49.58969516619197], [150.6417843, -33.7976938, 34.71703633301888], [150.6599804, -33.6643534, 10.58541940551396], [150.6448742, -33.5551287, 348.76542536774076], [150.6871019, -33.2145622, 309.22595120057736], [150.5411909, -33.2027859, 290.9052966020208], [150.1281731, -33.1789387, 233.36930554542403]]]}}]}

LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
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+software and other kinds of works.
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+
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+<https://www.gnu.org/licenses/>.
+
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+may consider it more useful to permit linking proprietary applications with
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+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,12 +0,0 @@
----
-title: SpatialParseback
-emoji: 🏃
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 5.23.3
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

db/poly_db_util.py

@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Sep 22 14:38:28 2022
+
+@author: syed
+"""
+
+import mysql.connector as sql_db 
+import json
+#import mysql.connector
+
+# Initialize connection.
+# Uses st.experimental_singleton to only run once.
+#@st.experimental_singleton
+def init_connection():
+    #return sql_db.connect(**st.secrets["mysql"])
+#    return sql_db.connect(user='root', password='root1234', 
+#                                   host='127.0.0.1',port=3306,
+#                                   database='rsi_polygon_schema')
+    return sql_db.connect(user='freedb_root_mehtab', password='b%9bYQ%5TsK%mAD', 
+                                   host='sql.freedb.tech',port=3306,
+                                   database='freedb_rsi_polygon_schema')
+
+
+
+
+
+
+query_insert_rating = "insert into rating(shape_id,expert_id, ratings) values(%s,%s,%s)"
+
+#query_update_rating = "update rating set ratings = %s where shape_id = %s and expert_id = %s"
+
+
+query_insert_shape = "insert into shape(ase,level_1, level_2, level_3, geojson) values(%s,%s,%s,%s,%s)"
+
+query_insert_expert = "insert into expert(name,expertise, tools_expert) values(%s,%s,%s)"
+
+
+def update_rating_query(shape_id, expert_id, ratings):
+    query_rating = "update rating set ratings = '"+str(ratings)+"' where" 
+    query_rating += " shape_id = '"+str(shape_id)+"' and expert_id = '"+str(expert_id)+"'"
+    return query_rating
+
+
+def get_rating_query(shape_id, expert_id):
+    query_rating = "select * from rating where "
+    if shape_id is not None:
+        query_rating += "shape_id = '"+str(shape_id)+"' "
+    if expert_id is not None:
+        query_rating += "and expert_id = '"+str(expert_id)+"' "
+   
+    return query_rating
+
+def get_shape_query(ase, level_1, level_2, level_3):
+    query_shape = "select * from shape where "
+    if ase is not None:
+        query_shape += "ase = '"+ase+"' "
+    if level_1 is not None:
+        query_shape += "and level_1 = '"+level_1+"' "
+    if level_2 is not None:
+        query_shape += "and level_2 = '"+level_2+"' "
+    if level_3 is not None:
+        query_shape += "and level_3 = '"+level_3+"'"
+    return query_shape
+
+def get_expert_query(name, expertise):
+    query_expert = "select * from expert where "
+    if name is not None:
+        query_expert += "name = '"+name+"' "
+    if expertise is not None:
+        query_expert += "and expertise = '"+expertise+"' "
+    return query_expert
+
+def apply_rating(name, expertise, tools_selected, 
+                              rating_selected, ase, level_1, level_2, level_3, geojson):
+    connection = init_connection()
+    cursor = connection.cursor(prepared=True)
+    
+    query_shape = get_shape_query(ase, level_1, level_2, level_3)
+
+    #tuple_shape = (ase, level_1, level_2, level_3)
+    tuple_expert = (name, expertise, str(tools_selected)[1:-1])
+    cursor.execute(query_shape)
+    record_shape = cursor.fetchone()
+    print("Record Shape:", record_shape)
+    print("Shape Select Query:", cursor.statement)
+    if record_shape is None:
+         tuple_insert_shape = (ase, level_1, level_2, level_3, json.dumps(geojson))
+         print(len(geojson))
+         cursor.execute(query_insert_shape, tuple_insert_shape)
+         connection.commit()
+         shape_id = cursor.lastrowid
+         print(f"Insert query executed with id : {shape_id}")
+    else:
+        shape_id = record_shape[0]
+    
+    query_expert = get_expert_query(name, expertise)
+
+    cursor.execute(query_expert)
+    record_expert = cursor.fetchone()
+    print("Expert Select Query:", cursor.statement)
+    if record_expert is None:
+        cursor.execute(query_insert_expert, tuple_expert)
+        connection.commit()
+        expert_id = cursor.lastrowid
+    else:
+        expert_id = record_expert[0]
+    
+    query_rating = get_rating_query(shape_id, expert_id)
+    cursor.execute(query_rating)
+    record_rating = cursor.fetchone()
+    
+    print("Ratings Select Query:", cursor.statement)
+    
+    
+    print("Shape ID", shape_id, "...Expert ID", expert_id)
+    tuple_rating = (shape_id, expert_id, rating_selected)
+    if record_rating is None:
+        cursor.execute(query_insert_rating, tuple_rating)
+        connection.commit()
+    else:
+        cursor.execute(update_rating_query(shape_id, expert_id, rating_selected))
+        connection.commit()
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    

dependencies.txt

@@ -0,0 +1,102 @@
+Bottleneck==1.4.2
+└── numpy [required: Any, installed: 1.26.4]
+Brotli==1.0.9
+cffi==1.17.1
+└── pycparser [required: Any, installed: 2.22]
+colorama==0.4.6
+folium==0.19.2
+├── branca [required: >=0.6.0, installed: 0.8.1]
+│   └── Jinja2 [required: >=3, installed: 3.1.5]
+│       └── MarkupSafe [required: >=2.0, installed: 3.0.2]
+├── Jinja2 [required: >=2.9, installed: 3.1.5]
+│   └── MarkupSafe [required: >=2.0, installed: 3.0.2]
+├── numpy [required: Any, installed: 1.26.4]
+├── requests [required: Any, installed: 2.32.3]
+│   ├── certifi [required: >=2017.4.17, installed: 2024.12.14]
+│   ├── charset-normalizer [required: >=2,<4, installed: 3.4.1]
+│   ├── idna [required: >=2.5,<4, installed: 3.10]
+│   └── urllib3 [required: >=1.21.1,<3, installed: 2.3.0]
+└── xyzservices [required: Any, installed: 2024.9.0]
+GDAL==3.6.2
+gensim==4.3.3
+├── numpy [required: >=1.18.5,<2.0, installed: 1.26.4]
+├── scipy [required: >=1.7.0,<1.14.0, installed: 1.13.1]
+│   └── numpy [required: >=1.22.4,<2.3, installed: 1.26.4]
+└── smart-open [required: >=1.8.1, installed: 7.1.0]
+    └── wrapt [required: Any, installed: 1.17.0]
+geopandas==0.11.1
+├── fiona [required: >=1.8, installed: 1.10.1]
+│   ├── attrs [required: >=19.2.0, installed: 24.3.0]
+│   ├── certifi [required: Any, installed: 2024.12.14]
+│   ├── click [required: ~=8.0, installed: 8.1.8]
+│   ├── click-plugins [required: >=1.0, installed: 1.1.1]
+│   │   └── click [required: >=4.0, installed: 8.1.8]
+│   └── cligj [required: >=0.5, installed: 0.7.2]
+│       └── click [required: >=4.0, installed: 8.1.8]
+├── packaging [required: Any, installed: 24.2]
+├── pandas [required: >=1.0.0, installed: 2.2.3]
+│   ├── numpy [required: >=1.22.4, installed: 1.26.4]
+│   ├── python-dateutil [required: >=2.8.2, installed: 2.9.0.post0]
+│   │   └── six [required: >=1.5, installed: 1.17.0]
+│   ├── pytz [required: >=2020.1, installed: 2024.2]
+│   └── tzdata [required: >=2022.7, installed: 2024.2]
+├── pyproj [required: >=2.6.1.post1, installed: 3.6.1]
+│   └── certifi [required: Any, installed: 2024.12.14]
+└── Shapely [required: >=1.7,<2, installed: 1.8.4]
+h2==4.1.0
+├── hpack [required: >=4.0,<5, installed: 4.0.0]
+└── hyperframe [required: >=6.0,<7, installed: 6.0.1]
+mapclassify==2.8.1
+├── networkx [required: >=2.7, installed: 3.4.2]
+├── numpy [required: >=1.23, installed: 1.26.4]
+├── pandas [required: >=1.4,!=1.5.0, installed: 2.2.3]
+│   ├── numpy [required: >=1.22.4, installed: 1.26.4]
+│   ├── python-dateutil [required: >=2.8.2, installed: 2.9.0.post0]
+│   │   └── six [required: >=1.5, installed: 1.17.0]
+│   ├── pytz [required: >=2020.1, installed: 2024.2]
+│   └── tzdata [required: >=2022.7, installed: 2024.2]
+├── scikit-learn [required: >=1.0, installed: 1.5.2]
+│   ├── joblib [required: >=1.2.0, installed: 1.4.2]
+│   ├── numpy [required: >=1.19.5, installed: 1.26.4]
+│   ├── scipy [required: >=1.6.0, installed: 1.13.1]
+│   │   └── numpy [required: >=1.22.4,<2.3, installed: 1.26.4]
+│   └── threadpoolctl [required: >=3.1.0, installed: 3.5.0]
+└── scipy [required: >=1.8, installed: 1.13.1]
+    └── numpy [required: >=1.22.4,<2.3, installed: 1.26.4]
+matplotlib==3.9.2
+├── contourpy [required: >=1.0.1, installed: 1.3.1]
+│   └── numpy [required: >=1.23, installed: 1.26.4]
+├── cycler [required: >=0.10, installed: 0.12.1]
+├── fonttools [required: >=4.22.0, installed: 4.55.3]
+├── kiwisolver [required: >=1.3.1, installed: 1.4.4]
+├── numpy [required: >=1.23, installed: 1.26.4]
+├── packaging [required: >=20.0, installed: 24.2]
+├── pillow [required: >=8, installed: 11.0.0]
+├── pyparsing [required: >=2.3.1, installed: 3.2.0]
+└── python-dateutil [required: >=2.7, installed: 2.9.0.post0]
+    └── six [required: >=1.5, installed: 1.17.0]
+munkres==1.1.4
+numexpr==2.10.1
+└── numpy [required: >=1.23.0, installed: 1.26.4]
+pipdeptree==2.24.0
+├── packaging [required: >=24.1, installed: 24.2]
+└── pip [required: >=24.2, installed: 24.3.1]
+PySocks==1.7.1
+Rtree==1.0.1
+setuptools==75.1.0
+tokenizers==0.21.0
+└── huggingface-hub [required: >=0.16.4,<1.0, installed: 0.27.0]
+    ├── filelock [required: Any, installed: 3.16.1]
+    ├── fsspec [required: >=2023.5.0, installed: 2024.12.0]
+    ├── packaging [required: >=20.9, installed: 24.2]
+    ├── PyYAML [required: >=5.1, installed: 6.0.2]
+    ├── requests [required: Any, installed: 2.32.3]
+    │   ├── certifi [required: >=2017.4.17, installed: 2024.12.14]
+    │   ├── charset-normalizer [required: >=2,<4, installed: 3.4.1]
+    │   ├── idna [required: >=2.5,<4, installed: 3.10]
+    │   └── urllib3 [required: >=1.21.1,<3, installed: 2.3.0]
+    ├── tqdm [required: >=4.42.1, installed: 4.67.1]
+    └── typing_extensions [required: >=3.7.4.3, installed: 4.12.2]
+unicodedata2==15.1.0
+wheel==0.44.0
+zstandard==0.23.0

disambiguation/disambiguate.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug  1 13:11:36 2022
+
+@author: syed
+"""
+import requests
+import urllib3
+import json
+from geocoder import geo_level1
+from geocoder import geo_level2
+from geocoder import geo_level3
+
+from utils import geoutil
+import re
+import regex_spatial
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
+
+
+
+def dismabiguate_entities(doc, ent, ase, level_1, level_2, level_3, midmid):
+    return get_coordinates(ent, ase, level_1, level_2, level_3, midmid)
+def get_coordinates(ent, ase, level_1, level_2, level_3, midmid):
+    request_url = 'https://nominatim.openstreetmap.org/search.php?q='+ase+'&polygon_geojson=1&accept-language=en&format=jsonv2'
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/18.3 Safari/605.1.15"
+    }
+    page = requests.get(request_url, headers=headers, verify=False)
+    # page = requests.get(request_url, verify=False)
+    # print(request_url, '++++++')
+    # print(ent, '++++++')
+    # print(ase, '++++++')
+    # print(level_1, '++++++')
+    # print(level_2, '++++++')
+    # print(level_3, '++++++')
+    # print(midmid, '++++++')
+
+
+    json_content = json.loads(page.content)
+    # json_content = json.loads(aa)
+    print(json_content, 'jjjjj')
+    all_coordinates = json_content[0]['geojson']['coordinates'][0]
+    centroid = (float(json_content[0]['lon']), float(json_content[0]['lat']))
+    for p in all_coordinates:
+        p2 = (p[0], p[1])
+        angle = geoutil.calculate_bearing(centroid, p2)
+        p.append(angle)
+    mid1 = None
+    mid2 = None
+    coordinates = all_coordinates
+
+    if level_3 is not None:
+        all_coordinates, centroid = geo_level3.get_level3_coordinates(coordinates, centroid, level_3, level_1)
+    
+    geojson = get_geojson(ent, all_coordinates, centroid)
+    
+    return geojson
+
+
+def dismabiguate_entities_between(doc, ent, ase, level_1, level_2, level_3, midmid):
+    return get_coordinates_between(doc, ent, ase, level_1, level_2, level_3, midmid)
+
+
+def get_coordinates_between(doc, ent, ase, level_1, level_2, level_3, midmid):
+
+    # first ase
+    request_url = 'https://nominatim.openstreetmap.org/search.php?q=' + doc.ents[0].text + '&polygon_geojson=1&accept-language=en&format=jsonv2'
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/18.3 Safari/605.1.15"
+    }
+    page1 = requests.get(request_url, headers=headers, verify=False)
+
+    # second ase
+    request_url = 'https://nominatim.openstreetmap.org/search.php?q=' + doc.ents[1].text + '&polygon_geojson=1&accept-language=en&format=jsonv2'
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/18.3 Safari/605.1.15"
+    }
+    page = requests.get(request_url, headers=headers, verify=False)
+
+
+    request_url = 'https://nominatim.openstreetmap.org/search.php?q=' + ase + '&polygon_geojson=1&accept-language=en&format=jsonv2'
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/18.3 Safari/605.1.15"
+    }
+    page = requests.get(request_url, headers=headers, verify=False)
+
+
+    json_content = json.loads(page.content)
+
+    all_coordinates = json_content[0]['geojson']['coordinates'][0]
+    centroid = (float(json_content[0]['lon']), float(json_content[0]['lat']))
+    for p in all_coordinates:
+        p2 = (p[0], p[1])
+        angle = geoutil.calculate_bearing(centroid, p2)
+        p.append(angle)
+    mid1 = None
+    mid2 = None
+    coordinates = all_coordinates
+    if level_1 is not None:
+
+        all_coordinates, centroid, mid1, mid2 = geo_level1.get_level1_coordinates(all_coordinates, centroid, level_1, midmid)
+
+    if level_2 is not None:
+        if level_1 is not None and level_1.lower() not in geo_level1.center:
+            all_coordinates, centroid = geo_level2.get_level2_coordinates(coordinates, centroid, level_2, level_1)
+        else:
+            print ("Else executed")
+            all_coordinates, centroid = geo_level2.get_level2_coordinates(all_coordinates, centroid, level_2, level_1)
+
+    if level_3 is not None:
+        all_coordinates, centroid = geo_level3.get_level3_coordinates(coordinates, centroid, level_3, level_1)
+
+    geojson = get_geojson(ent, all_coordinates, centroid)
+
+    return geojson
+
+def get_geojson(ent, arr, centroid):
+    poly_json = {}
+    poly_json['type'] = 'FeatureCollection'
+    poly_json['features'] = []
+    coordinates= []
+    coordinates.append(arr)
+    poly_json['features'].append({
+    'type':'Feature',
+    'id': ent,
+    'properties': {
+        'centroid': centroid
+        },
+    'geometry': {
+        'type':'Polygon',
+        'coordinates': coordinates     
+        }
+    })
+    return poly_json
+    
+def export(ent, poly_json):
+    with open(ent+'.geojson', 'w') as outfile:
+        json.dump(poly_json, outfile)
+    
+    

geocoder/geo_level1.py

@@ -0,0 +1,158 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug  2 12:38:31 2022
+
+@author: syed
+"""
+import regex_spatial
+from utils import geoutil
+
+
+north = ["north", "N'", "North", "NORTH"]
+south = ["south", "S'", "South", "SOUTH"]
+east = ["east", "E'", "East", "EAST"]
+west = ["west", "W'", "West", "WEST"]
+northeast = ["north-east", "NE'", "north east", "NORTH-EAST", "North East", "NORTH EAST", 'northeast']
+southeast = ["south-east", "SE'", "south east", "SOUTH-EAST", "South East", "SOUTH EAST", 'southeast']
+northwest = ["north-west", "NW'", "north west", "NORTH-WEST", "North West", "NORTH WEST", 'northwest']
+southwest = ["south-west", "SW'", "south west", "SOUTH-WEST", "South West", "SOUTH WEST", 'southwest']
+center = ["center","central", "downtown","midtown"]
+def get_min_max(direction):
+    regex = regex_spatial.get_directional_regex()
+    direction_list = regex.split("|")
+    if direction in direction_list:
+        if direction in east:
+            return (337, 22)
+        if direction in northeast:
+            return (22, 67)
+        if direction in north:
+            return (67, 112)
+        if direction in northwest:
+            return (112, 157)
+        if direction in west:
+            return (157, 202)
+        if direction in southwest:
+            return (202, 247)
+        if direction in south:
+            return (247, 292)
+        if direction in southeast:
+            return (292, 337)
+        
+    return None
+
+
+def get_min_max(direction):
+    regex = regex_spatial.get_directional_regex()
+    direction_list = regex.split("|")
+    if direction in direction_list:
+        if direction in east:
+            return (337, 22)
+        if direction in northeast:
+            return (292, 337)
+        if direction in north:
+            return (247, 292)
+        if direction in northwest:
+            return (202, 247)
+        if direction in west:
+            return (157, 202)
+        if direction in southwest:
+            return (112, 157)
+        if direction in south:
+            return (67, 112)
+        if direction in southeast:
+            return (22, 67)
+
+    return None
+
+
+def get_directional_coordinates_by_angle(coordinates, direction, minimum, maximum):
+    direction_coordinates = []
+    for p in coordinates:
+        if direction in east:
+            if p[2] >= minimum or p[2] <= maximum:
+                direction_coordinates.append(p)
+                
+        else:
+             if p[2] >= minimum and p[2] <= maximum:
+                direction_coordinates.append(p)
+    return direction_coordinates 
+   
+def get_directional_coordinates(coordinates, direction, centroid , minimum, maximum, is_midmid):
+    direction_coordinates = get_directional_coordinates_by_angle(coordinates, direction, minimum, maximum)
+    midmid1, midmid2= geoutil.get_midmid_point(centroid, direction_coordinates[0],direction_coordinates[-1], is_midmid) 
+    if direction in west:
+          maxi = max(p[2] for p in direction_coordinates)
+          mini = min(p[2] for p in direction_coordinates)
+          index_mini = 0
+          index_maxi = 0
+          for idx,p in enumerate(direction_coordinates):
+              if p[2] == mini:
+                  index_mini = idx
+              if p[2] == maxi:
+                  index_maxi = idx
+          
+          direction_coordinates.insert(index_maxi+1, midmid2)
+          direction_coordinates.insert(index_mini+1, midmid1)
+
+    else:
+        direction_coordinates.append(midmid2)
+        direction_coordinates.append(midmid1)
+    
+    return direction_coordinates, midmid1, midmid2
+
+def get_level1_coordinates(coordinates, centroid, direction, is_midmid):
+        min_max = get_min_max(direction)
+        if min_max is not None:
+           coordinates, mid1, mid2 = get_directional_coordinates(coordinates, direction, centroid,
+                                              min_max[0], min_max[1],is_midmid)
+
+           for idx, p in enumerate(coordinates):
+               print(idx, p)
+           return coordinates, centroid, mid1, mid2
+        elif direction.lower() in center:
+            return  get_central(coordinates, centroid, direction, is_midmid), centroid, None, None
+        else :
+            return coordinates, centroid, None, None
+        
+        
+def get_central(coordinates, centroid, direction, is_midmid):
+   
+    n_min_max = get_min_max("north")
+    n_coordinates=get_directional_coordinates_by_angle(coordinates, "north", n_min_max[0], n_min_max[1])
+    n_mid1, n_mid2 = geoutil.get_midmid_point(centroid,n_coordinates[0],n_coordinates[-1], is_midmid)
+    
+    ne_min_max = get_min_max("north east")
+    ne_coordinates=get_directional_coordinates_by_angle(coordinates, "north east", ne_min_max[0], ne_min_max[1])
+    ne_mid1, ne_mid2 = geoutil.get_midmid_point(centroid,ne_coordinates[0],ne_coordinates[-1], is_midmid)
+    
+    e_min_max = get_min_max("east")
+    e_coordinates=get_directional_coordinates_by_angle(coordinates, "east", e_min_max[0], e_min_max[1])
+    e_mid1, e_mid2 = geoutil.get_midmid_point(centroid,e_coordinates[0],e_coordinates[-1], is_midmid)
+
+    se_min_max = get_min_max("south east")
+    se_coordinates=get_directional_coordinates_by_angle(coordinates, "south east", se_min_max[0], se_min_max[1])
+    se_mid1, se_mid2 = geoutil.get_midmid_point(centroid,se_coordinates[0],se_coordinates[-1], is_midmid)
+
+    s_min_max = get_min_max("south")
+    s_coordinates=get_directional_coordinates_by_angle(coordinates, "south", s_min_max[0], s_min_max[1])
+    s_mid1, s_mid2 = geoutil.get_midmid_point(centroid,s_coordinates[0],s_coordinates[-1], is_midmid)
+
+    sw_min_max = get_min_max("south west")
+    sw_coordinates=get_directional_coordinates_by_angle(coordinates, "south west", sw_min_max[0], sw_min_max[1])
+    sw_mid1, sw_mid2 = geoutil.get_midmid_point(centroid,sw_coordinates[0],sw_coordinates[-1], is_midmid)
+
+    w_min_max = get_min_max("west")
+    w_coordinates=get_directional_coordinates_by_angle(coordinates, "west", w_min_max[0], w_min_max[1])
+    w_mid1, w_mid2 = geoutil.get_midmid_point(centroid,w_coordinates[0],w_coordinates[-1], is_midmid)
+    
+    nw_min_max = get_min_max("north west")
+    nw_coordinates=get_directional_coordinates_by_angle(coordinates, "north west", nw_min_max[0], nw_min_max[1])
+    nw_mid1, nw_mid2 = geoutil.get_midmid_point(centroid,nw_coordinates[0],nw_coordinates[-1], is_midmid)
+
+    central_coordindates =[e_mid1, e_mid2, ne_mid1, ne_mid2, n_mid1, n_mid2, 
+                           nw_mid1, nw_mid2, w_mid1, w_mid2, sw_mid1, sw_mid2,
+                           s_mid1, s_mid2, se_mid1, se_mid2]
+    return central_coordindates        
+    
+    

geocoder/geo_level2.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug  2 12:38:31 2022
+
+@author: syed
+"""
+import regex_spatial
+from utils import geoutil
+import geopandas as gpd
+import pandas as pd
+import re
+from shapely.geometry import Polygon,mapping
+import numpy as np
+from shapely.geometry import Polygon, MultiPoint
+from shapely.geometry.base import geom_factory
+from shapely.geos import lgeos
+from geocoder import geo_level1
+
+#def get_common_coordinates(coordinates, level2):
+    #gdp.
+
+def get_near(level2):
+    near = re.search(regex_spatial.get_near_regex(), level2)
+    if near is not None:
+        return near.group()
+    return None 
+
+def get_surrounding(level2):
+    surrounding = re.search(regex_spatial.get_surrounding_regex(), level2)
+    if surrounding is not None:
+        return surrounding.group()
+    return None 
+
+    
+def sort_west(coords1, poly2, centroid):
+    #coords1 = mapping(poly1)["features"][0]["geometry"]["coordinates"]
+    coords2 = mapping(poly2)["features"][0]["geometry"]["coordinates"]
+    coord1 = []
+    coord2 = []
+    coord = []
+    for c in coords1:
+        pol = list(c[::-1])
+        coord1.extend(pol)
+    for c in coords2:
+        pol = list(c[::-1])
+        coord2.extend(pol)
+    coo1 = []
+    coo2 = []
+    for p in coord1:
+        angle = geoutil.calculate_bearing(centroid, p)
+        if angle >= 157 and angle <= 202:
+            coo1.append((p[0], p[1], angle))
+    for p in coord2:
+        angle = geoutil.calculate_bearing(centroid, p)
+        if angle >= 157 and angle <= 202:
+            coo2.append((p[0], p[1], angle))
+    coo1.extend(coo2)
+    return coo1
+
+def get_directional_coordinates_by_angle(coordinates, centroid, direction, minimum, maximum):
+    direction_coordinates = []
+    for p in coordinates:
+        angle = geoutil.calculate_bearing(centroid, p)
+        if direction in geo_level1.east:
+            if angle >= minimum or angle <= maximum:
+                direction_coordinates.append(p)
+                
+        else:
+             if angle >= minimum and angle <= maximum:
+                direction_coordinates.append(p)
+    #if(direction in geo_level1.west):
+    #    direction_coordinates.sort(key=lambda k: k[2], reverse=True)
+   
+    return direction_coordinates 
+
+def get_direction_coordinates(coordinates, centroid, level1):
+    min_max = geo_level1.get_min_max(level1)
+    if min_max is not None:
+        coord = get_directional_coordinates_by_angle(coordinates, centroid, level1, min_max[0], min_max[1])
+        return coord
+    return coordinates
+
+
+def get_level2_coordinates(coordinates, centroid, level_2, level_1):
+    near = get_near(level_2)   
+    surrounding = get_surrounding(level_2)
+    
+    
+    poly1 = Polygon(coordinates)
+    polygon1 = gpd.GeoSeries(poly1)
+    if near is not None:
+        poly2 = polygon1.buffer(0.0095, join_style=2)
+    if surrounding is not None:
+        poly2 = polygon1.buffer(0.012, join_style=2)
+    
+    poly = poly2.difference(polygon1)
+    coords = mapping(poly)["features"][0]["geometry"]["coordinates"]
+    
+    coord = []
+    for c in coords:
+        pol = list(c[::-1])
+        coord.extend(pol)
+        
+    if level_1 is not None and level_1.lower() not in geo_level1.center:
+        coord = get_direction_coordinates(coord, centroid, level_1)
+        if level_1 in geo_level1.west:
+            coord = sort_west(coordinates,poly2, centroid)
+    
+    print("Level 2 Coordinates")
+    for idx, p in enumerate(coord):
+        print(idx, p)
+    
+    return coord, centroid
+
+
+      

geocoder/geo_level3.py

@@ -0,0 +1,139 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug  2 12:38:31 2022
+
+@author: syed
+"""
+import regex_spatial
+from utils import geoutil
+import geopandas as gpd
+import pandas as pd
+import re
+from shapely.geometry import Polygon,mapping
+import numpy as np
+from shapely.geometry import Polygon, MultiPoint, LineString, Point
+from shapely.geometry.base import geom_factory
+from shapely.geos import lgeos
+from geocoder import geo_level1
+
+
+
+def get_level3(level3):
+    digits = re.findall('[0-9]+', level3)[0]
+    unit = re.findall('[A-Za-z]+', level3)[0]
+    return digits, unit
+
+
+def get_directional_coordinates_by_angle(coordinates, centroid, direction, minimum, maximum):
+    direction_coordinates = []
+    for p in coordinates:
+        angle = geoutil.calculate_bearing(centroid, p)
+        p2= (p[0],p[1],angle)
+        if direction in geo_level1.east:
+            if angle >= minimum or angle <= maximum:
+                direction_coordinates.append(p2)
+                
+        else:
+             if angle >= minimum and angle <= maximum:
+                direction_coordinates.append(p2)
+
+   
+    return direction_coordinates 
+
+def sort_west(poly1, poly2, centroid):
+    coords1 = mapping(poly1)["features"][0]["geometry"]["coordinates"]
+    coords2 = mapping(poly2)["features"][0]["geometry"]["coordinates"]
+    coord1 = []
+    coord2 = []
+    coord = []
+    for c in coords1:
+        pol = list(c[::-1])
+        coord1.extend(pol)
+    for c in coords2:
+        pol = list(c[::-1])
+        coord2.extend(pol)
+    coo1 = []
+    coo2 = []
+    for p in coord1:
+        angle = geoutil.calculate_bearing(centroid, p)
+        if angle >= 157 and angle <= 202:
+            coo1.append((p[0], p[1], angle))
+    for p in coord2:
+        angle = geoutil.calculate_bearing(centroid, p)
+        if angle >= 157 and angle <= 202:
+            coo2.append((p[0], p[1], angle))
+    coo1.extend(coo2)
+    return coo1
+    
+def get_direction_coordinates(coordinates, centroid, level1):
+    min_max = geo_level1.get_min_max(level1)
+    if min_max is not None:
+        coord = get_directional_coordinates_by_angle(coordinates, centroid, level1, min_max[0], min_max[1])
+        return coord
+    return coordinates
+    
+def get_level3_coordinates(coordinates, centroid, level_3, level1):
+    distance, unit = get_level3(level_3)
+    
+    kms = geoutil.get_kilometers(distance, unit)
+
+    
+    coord = []
+    
+    poly1 = Polygon(coordinates)
+    polygon1 = gpd.GeoSeries(poly1)
+    poly2 = polygon1.buffer(0.0095*kms, join_style=2)                       # 扩大小面积
+    poly3 = polygon1.buffer(0.013*kms, join_style=2)                        # 扩大大面积
+    poly = poly3.difference(poly2)                                          # 合二为一成为环状
+    coords = mapping(poly)["features"][0]["geometry"]["coordinates"]        # 改为坐标格式
+
+    
+    for c in coords:
+        pol = list(c[::-1])
+        coord.extend(pol)
+    if level1 is not None:
+        coord = get_direction_coordinates(coord, centroid, level1)
+        if level1 in geo_level1.west:
+            coord = sort_west(poly3, poly2, centroid)
+    print("Level 3 Coordinates")
+    for idx, p in enumerate(coord):
+        print(idx, p)
+    return coord, centroid
+
+
+def get_between_coordinates(coordinates1, coordinates2, centroid1, centroid2):
+    poly1 = Polygon(coordinates1)
+    poly2 = Polygon(coordinates2)
+
+
+    center_line = LineString([centroid1, centroid2])
+
+    def max_perpendicular_distance(poly, line):
+        max_dist = 0
+        farthest_points = None
+
+        for point in poly.exterior.coords:
+            p = Point(point)
+
+            dist = p.distance(line)
+            if dist > max_dist:
+                max_dist = dist
+                farthest_points = p
+
+        return max_dist * 2
+
+    diameter1 = max_perpendicular_distance(poly1, center_line)
+    diameter2 = max_perpendicular_distance(poly2, center_line)
+
+    R = (diameter1 + diameter2) / 2
+
+    midpoint = ((centroid1[0] + centroid2[0]) / 2, (centroid1[1] + centroid2[1]) / 2)
+
+    circle = Point(midpoint).buffer(R / 2, resolution=100)
+
+    circle_coords = list(circle.exterior.coords)
+
+    return [circle_coords], midpoint
+
+

geospacy.py

@@ -0,0 +1,256 @@
+from spacy.tokens import Span
+from spacy.tokens import Doc
+from spacy.tokens import Token
+import regex_spatial
+from spacy.language import Language
+import re
+from utils import llm_ent_extract
+
+id =""
+rse_id = "rse_id"
+def set_extension():
+     Span.set_extension(rse_id, default = "",force = True)
+     Doc.set_extension(rse_id, default = "",force = True)
+     Token.set_extension(rse_id, default = "",force = True)
+     
+def get_level1(doc, sentence, ent):
+    return find_ent_by_regex(doc, sentence, ent, regex_spatial.get_level1_regex())
+
+def get_level2(doc, sentence, ent):
+  return find_ent_by_regex(doc, sentence, ent, regex_spatial.get_level2_regex()) 
+
+def get_level3(doc, sentence, ent):
+  return find_ent_by_regex(doc, sentence, ent, regex_spatial.get_level3_regex())
+
+
+def find_ent_by_regex(doc, sentence, ent, regex):
+  global id
+
+  if id == "":
+      id = ent.text
+  for match in re.finditer(regex, doc.text):
+        start, end = match.span()
+        if(start>= sentence.start_char and start<= sentence.end_char):
+          span = doc.char_span(start, end)
+          if span is not None:
+            id = span.text +"_"+ id
+            if(start > ent.end_char):
+              ent.end_char = end
+            else:
+              ent.start_char = start         
+
+          return ent
+
+  return ent
+
+
+def update_entities(doc, entity_texts, replace=True):
+    """
+    根据给定的文本内容标注实体，并直接修改 doc.ents。
+
+    :param doc: spaCy 解析后的 Doc 对象
+    :param entity_texts: 字典，键是要标注的实体文本，值是对应的实体类别
+    :param replace: 布尔值，True 则替换现有实体，False 则保留现有实体并添加新的
+    """
+    new_ents = list(doc.ents) if not replace else []  # 如果 replace=False，保留已有实体
+
+    for ent_text, ent_label in entity_texts.items():
+        start = doc.text.find(ent_text)  # 在全文中查找文本位置
+        if start != -1:
+            start_token = len(doc.text[:start].split())  # 计算起始 token 索引
+            end_token = start_token + len(ent_text.split())  # 计算结束 token 索引
+
+            if start_token < len(doc) and end_token <= len(doc):  # 确保索引不越界
+                new_ent = Span(doc, start_token, end_token, label=ent_label)
+                new_ents.append(new_ent)
+
+    doc.set_ents(new_ents)  # 更新 doc.ents
+
+
+def get_relative_entity(doc, sentence, ent):
+  global id
+
+  id = ""
+  rel_entity = get_level1(doc, sentence, ent)
+  # print(1111 ,rel_entity)
+  rel_entity = get_level2(doc, sentence, rel_entity)
+  # print(2222 ,rel_entity)
+  rel_entity = get_level3(doc, sentence, rel_entity)
+  # print(3333 ,rel_entity)
+
+  if("_" in id):
+    rel_entity = doc.char_span(rel_entity.start_char, rel_entity.end_char, "RSE")
+    rel_entity._.rse_id = id
+
+    # print(id, 'idid')
+    # print(rel_entity._.rse_id, '._._')
+
+    return rel_entity
+  rel_entity = doc.char_span(ent.start_char, ent.end_char, ent.label_)
+  rel_entity._.rse_id = id
+  # print(4444 ,rel_entity)
+  return rel_entity
+
+@Language.component("spatial_pipeline")
+def get_spatial_ent(doc):
+  set_extension()
+  new_ents = []
+  # ents = [ent for ent in doc.ents if ent.label_ == "GPE" or ent.label_ == "LOC"]        # 筛选出ase
+
+
+  # LLM 输出
+  # GPE = '[###Pyrmont###, ###Glebe###]'                       # LLM 输出的实体
+  GPE = llm_ent_extract.extract_GPE(doc.text)                       # LLM 输出的实体
+  print(doc.text, 'llmin')
+  print(GPE, 'llout')
+
+  GPE = llm_ent_extract.extract(GPE, 'GPE')
+  print(GPE, 'llmout2')
+  update_entities(doc, GPE, True)
+  ents = doc.ents
+  print(ents, 'eee')
+  # print(doc, 'ddd')
+  # print(ents, 'ddd')
+  # GPE = llm_ent_extract.extract(llm_ent_extract.extract_GPE(doc.text), 'gpe')
+  # update_entities(doc, GPE)
+  # LLM 输出完毕
+
+
+  # print(doc.ents, 111)
+  # print(doc.ents[2], 222)
+  # print(type(doc.ents[2]), 222)
+  # print(doc.ents[2].label_, 333)
+  # print('----------')
+  # doc.ents[2] = 'pp'
+  # print(doc.ents[2], 111)
+  # print(doc.ents[2].label_, 222)
+  # print(type(doc.ents), 333)
+  end = None
+  for ent in ents:
+
+    if ent.end != len(doc):
+        next_token = doc[ent.end]
+        if end is not None:
+          start = end
+        else:
+          start = ent.sent.start
+        if next_token.text.lower() in regex_spatial.get_keywords():
+          end = next_token.i
+        else:
+          end = ent.end
+
+    else:
+        start = ent.sent.start
+        end = ent.end
+
+    # print(doc, '//',start, '//', end, 999888)
+    # print(doc[start],'//', doc[end])
+    # print(ents, 999)
+
+
+    rsi_ent = get_relative_entity(doc,Span(doc, start, end), ent)
+    # print(doc.ents[0]._.rse_id, '._._2')
+
+
+    # print(rsi_ent.text, rsi_ent.label_, rsi_ent._.rse_id)
+    new_ents.append(rsi_ent)
+
+  doc.ents = new_ents
+  return doc
+
+# def update_doc_ents(doc, new_dict):
+#     """
+#     更新 doc.ents, 将新的实体文本和标签添加到 doc 中。
+#
+#     参数:
+#     - doc: spaCy 的 Doc 对象
+#     - new_dict: 一个字典，键是实体文本，值是标签
+#     """
+#     modified_ents = []
+#
+#     # 遍历字典中的实体文本和标签
+#     for ent_text, label in new_dict.items():
+#         # 将实体文本拆分成单词
+#         ent_words = ent_text.split()
+#
+#         # 遍历 doc 中的 token 来查找第一个单词
+#         start = None
+#         for i in range(len(doc)):
+#             # 如果当前 token 和实体的第一个单词匹配，确定 start
+#             if doc[i].text == ent_words[0]:
+#                 start = i
+#                 # 然后检查后续的单词是否都匹配
+#                 end = start + len(ent_words)  # 计算 end 为 start + 单词数
+#                 if all(doc[start + j].text == ent_words[j] for j in range(len(ent_words))):
+#                     # 创建 Span 对象
+#                     new_ent = Span(doc, start, end, label=label)
+#                     modified_ents.append(new_ent)
+#                     break  # 找到匹配后跳出循环
+#
+#     # 使用 doc.set_ents() 更新 doc.ents
+#     doc.set_ents(modified_ents)
+#
+#
+# # def llm_extract(doc, model):
+#
+# def split_doc_into_sentences(doc):
+#     """
+#     将 doc 的文本按句子分割，并返回每个句子的字符串列表。
+#     """
+#     sentence_list = [sent.text.strip() for sent in doc.sents]
+#     return sentence_list
+#
+#
+# @Language.component("spatial_pipeline")
+# def get_spatial_ent(doc):
+#
+#     set_extension()
+#
+#     split_sent = split_doc_into_sentences(doc)
+#     for i in range(len(split_sent)):
+#         gpe_dict = llm_ent_extract.extract_GPE(split_sent[i])
+#         loc_dict = llm_ent_extract.extract_LOC(split_sent[i])
+#         new_dict = gpe_dict|loc_dict
+#
+#
+#     print(gpe_dict, '111')
+#     print(loc_dict)
+#     print(new_dict)
+#     # new_dict = {'pp': 'ORG', 'France': 'GPE', 'Paris': 'GPE'}
+#
+#
+#     # 调用新的函数更新 doc 的实体
+#     update_doc_ents(doc, new_dict)
+#
+#     # 继续处理 doc.ents
+#     ents = [ent for ent in doc.ents if ent.label_ == "GPE" or ent.label_ == "LOC"]
+#     print(ents[1].label_)
+#
+#     end = None
+#     new_ents = []
+#
+#     for ent in ents:
+#         if ent.end != len(doc):
+#             next_token = doc[ent.end + 1]
+#             if end is not None:
+#                 start = end
+#             else:
+#                 start = ent.sent.start
+#             if next_token.text.lower() in regex_spatial.get_keywords():
+#                 end = next_token.i
+#             else:
+#                 end = ent.end
+#         else:
+#             start = ent.sent.start
+#             end = ent.end
+#
+#         # 调用 get_relative_entity 来获得新的实体信息
+#         rsi_ent = get_relative_entity(doc, Span(doc, start, end), ent)
+#
+#         # 将处理后的实体添加到新的实体列表中
+#         new_ents.append(rsi_ent)
+#
+#     doc.ents = new_ents  # 更新 doc.ents
+#     print(new_ents, '111222')
+#
+#     return doc

regex_spatial.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jul 26 14:57:18 2022
+
+@author: syed
+"""
+
+from quantities import units as u
+from quantities import Quantity
+
+one_plus = "+"
+zero_plus = "*"
+
+
+def get_quantities_regex():
+  # myList = [unit for unit in dir(u.length)
+  #           if type(getattr(u.length, unit)) is u.length ]
+  myList = [unit for unit in dir(u.length) if isinstance(getattr(u.length, unit), Quantity)]
+
+  units = [ x for x in myList if "_" not in x ]
+  units_regex = '|'.join(units)
+  return "["+units_regex+"]"
+def get_number_regex():
+  regex = "[0-9]"
+  return regex
+def get_space_regex():
+  regex = "\s"
+  return regex
+
+def get_directional_regex():
+  cardinals_kwds = "north|south|east|west"
+  ordinals_kwds = "north-east|north-west|south-east|south-west|north east|north west|south east|south west|northeast|northwest|southeast|southwest"
+  symbols_kwds = "N'|S'|E'|W'|NE'|NW'|SE'|SW'"
+  return ordinals_kwds+"|"+symbols_kwds+"|"+cardinals_kwds
+
+def get_center_regex():
+  center_kwds = "center|central|downtown|midtown"
+  return center_kwds
+
+def get_near_regex():
+  near_kwds = "nearby|near|vicinity|close|beside|next|adjacent|immediate|border"
+  return near_kwds
+
+def get_surrounding_regex():
+  surrounding_kwds = "surrounding|neigbourhood|proximity|territory|locality"
+  return surrounding_kwds
+def get_level1_regex():
+  level_1_regex = "(?i)("+get_directional_regex()+"|"+get_center_regex()+")"
+  return level_1_regex
+
+def get_level2_regex():
+  level_2_regex = "(?i)("+get_near_regex()+"|"+get_surrounding_regex()+")"
+  return level_2_regex
+
+def get_level3_regex():
+  level_3_regex = "(?i)("+get_number_regex()+one_plus+get_space_regex()+zero_plus+get_quantities_regex()+one_plus+")"
+  return level_3_regex
+
+
+
+def get_keywords():
+   keywords = []
+   keywords = get_directional_regex().split("|")
+   keywords.extend(get_near_regex().split("|"))
+   keywords.extend(get_surrounding_regex().split("|"))
+   keywords.extend(get_center_regex().split("|"))
+   keywords.append(",")
+   keywords.append("and")
+   keywords.append(".")
+   
+   return keywords

requirements.txt

@@ -0,0 +1,133 @@
+altair==4.2.2
+annotated-types==0.7.0
+anyio==4.8.0
+attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1734348785146/work
+blinker==1.9.0
+blis==0.7.11
+Bottleneck @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_55txi4fy1u/croot/bottleneck_1731058642212/work
+branca @ file:///home/conda/feedstock_root/build_artifacts/branca_1734433375112/work
+Brotli @ file:///private/var/folders/k1/30mswbxs7r1g6zwn8y4fyt500000gp/T/abs_27zk0eqdh0/croot/brotli-split_1714483157007/work
+cachetools==5.5.0
+catalogue==2.0.10
+certifi==2024.12.14
+cffi @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_06ndtibm2c/croot/cffi_1726856446111/work
+charset-normalizer==3.4.1
+click==7.1.2
+click-plugins @ file:///home/conda/feedstock_root/build_artifacts/click-plugins_1733731077999/work
+cligj @ file:///home/conda/feedstock_root/build_artifacts/cligj_1733749956636/work
+cloudpathlib==0.20.0
+colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1733218098505/work
+confection==0.1.5
+contourpy @ file:///private/var/folders/k1/30mswbxs7r1g6zwn8y4fyt500000gp/T/abs_2cvjf0v4ux/croot/contourpy_1732540055997/work
+cycler @ file:///home/conda/feedstock_root/build_artifacts/cycler_1733332471406/work
+cymem==2.0.10
+distro==1.9.0
+en_core_web_md @ https://github.com/explosion/spacy-models/releases/download/en_core_web_md-3.0.0/en_core_web_md-3.0.0.tar.gz#sha256=45757583a3c9d15c6895cf4bf9512e86243bab8cda51512c127f5cbdc34ac630
+en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz#sha256=0673b3ce4a28a19e99dc0fc3f8f7fe67b936c41749e6b70f778ec5f9f78f3a66
+entrypoints==0.4
+exceptiongroup==1.2.2
+filelock==3.16.1
+fiona @ file:///private/var/folders/k1/30mswbxs7r1g6zwn8y4fyt500000gp/T/abs_10va3k3ghg/croot/fiona_1735554358779/work
+folium==0.19.4
+fonttools @ file:///home/conda/feedstock_root/build_artifacts/fonttools_1735335791296/work
+fsspec==2024.12.0
+GDAL @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_0at39q0ks3/croot/gdal-split_1704913257208/work/build/swig/python
+gensim==4.3.3
+geopandas @ file:///home/conda/feedstock_root/build_artifacts/geopandas_1658688831518/work
+gitdb==4.0.11
+GitPython==3.1.43
+h11==0.14.0
+h2 @ file:///home/conda/feedstock_root/build_artifacts/h2_1733298745555/work
+hpack @ file:///home/conda/feedstock_root/build_artifacts/hpack_1733299205993/work
+httpcore==1.0.7
+httpx==0.28.1
+huggingface-hub==0.27.0
+hyperframe @ file:///home/conda/feedstock_root/build_artifacts/hyperframe_1733298771451/work
+idna==3.10
+importlib_metadata==8.5.0
+Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1734823942230/work
+jiter==0.8.2
+joblib @ file:///home/conda/feedstock_root/build_artifacts/joblib_1733736026804/work
+jsonschema==4.23.0
+jsonschema-specifications==2024.10.1
+kiwisolver @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_93o8te804v/croot/kiwisolver_1672387163224/work
+langcodes==3.5.0
+language_data==1.3.0
+mapclassify @ file:///home/conda/feedstock_root/build_artifacts/mapclassify_1733731066416/work
+marisa-trie==1.2.1
+markdown-it-py==3.0.0
+MarkupSafe @ file:///home/conda/feedstock_root/build_artifacts/markupsafe_1733219583522/work
+matplotlib==3.9.2
+mdurl==0.1.2
+munkres==1.1.4
+murmurhash==1.0.11
+mysql-connector-python==8.0.30
+narwhals==1.20.1
+networkx @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_networkx_1731521053/work
+numexpr @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_b3kvvt6tc6/croot/numexpr_1730215947700/work
+numpy @ file:///private/var/folders/k1/30mswbxs7r1g6zwn8y4fyt500000gp/T/abs_a51i_mbs7m/croot/numpy_and_numpy_base_1708638620867/work/dist/numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl#sha256=c4b11b3c4d4fdb810039503fe01f311ade06cd1d675fcd6d208800a393f19b69
+openai==1.61.1
+packaging==24.2
+pandas @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_4aifrweohv/croot/pandas_1732735109535/work/dist/pandas-2.2.3-cp310-cp310-macosx_11_0_arm64.whl#sha256=6c5bbde94d3ac89a919939bdecfabca704888cdaf6251a6b101db7024fed27ab
+pathlib_abc==0.1.1
+pathy==0.11.0
+pillow @ file:///private/var/folders/k1/30mswbxs7r1g6zwn8y4fyt500000gp/T/abs_92egn12how/croot/pillow_1731594702114/work
+pipdeptree==2.24.0
+preshed==3.0.9
+protobuf==3.20.1
+pyarrow==18.1.0
+pycparser @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_pycparser_1733195786/work
+pydantic==1.10.0
+pydantic_core==2.27.2
+pydeck==0.9.1
+Pygments==2.18.0
+Pympler==1.1
+pyparsing @ file:///home/conda/feedstock_root/build_artifacts/pyparsing_1733222594562/work
+pyproj @ file:///private/var/folders/k1/30mswbxs7r1g6zwn8y4fyt500000gp/T/abs_acukan6udl/croot/pyproj_1704901274181/work
+PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1733217236728/work
+python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1733215673016/work
+pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1733215667876/work
+PyYAML==6.0.2
+quantities==0.13.0
+referencing==0.35.1
+requests==2.32.3
+rich==13.9.4
+rpds-py==0.22.3
+Rtree @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_a4tjgnr1ag/croot/rtree_1675157863029/work
+scikit-learn @ file:///private/var/folders/k1/30mswbxs7r1g6zwn8y4fyt500000gp/T/abs_cdv8p6f6u3/croot/scikit-learn_1734547683501/work
+scipy==1.13.1
+semver==3.0.2
+Shapely @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_8arrf7j2ot/croots/recipe/shapely_1664521590671/work
+shellingham==1.5.4
+six @ file:///home/conda/feedstock_root/build_artifacts/six_1733380938961/work
+smart-open==6.4.0
+smmap==5.0.1
+sniffio==1.3.1
+spacy==3.0.9
+spacy-legacy==3.0.12
+spacy-loggers==1.0.5
+spacy-streamlit==1.0.1
+srsly==2.5.0
+streamlit==1.41.1
+streamlit_folium==0.24.0
+tenacity==9.0.0
+thinc==8.0.17
+threadpoolctl @ file:///home/conda/feedstock_root/build_artifacts/threadpoolctl_1714400101435/work
+tokenizers==0.21.0
+toml==0.10.2
+toolz==1.0.0
+tornado==6.4.2
+tqdm==4.67.1
+typer==0.3.2
+typing_extensions==4.12.2
+tzdata @ file:///home/conda/feedstock_root/build_artifacts/python-tzdata_1733235305708/work
+tzlocal==5.2
+unicodedata2 @ file:///private/var/folders/k1/30mswbxs7r1g6zwn8y4fyt500000gp/T/abs_a3epjto7gs/croot/unicodedata2_1713212955584/work
+urllib3==2.3.0
+validators==0.34.0
+wasabi==0.10.1
+weasel==0.4.1
+wrapt==1.17.0
+xyzservices @ file:///home/conda/feedstock_root/build_artifacts/xyzservices_1733143442470/work
+zipp==3.21.0
+zstandard @ file:///private/var/folders/k1/30mswbxs7r1g6zwn8y4fyt500000gp/T/abs_a5_i6g4o6n/croot/zstandard_1731356352787/work

utils/config.py

@@ -0,0 +1 @@
+api_key = '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'

utils/geoutil.py

@@ -0,0 +1,262 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug  2 12:45:21 2022
+
+@author: syed
+"""
+
+import math
+import re
+import regex_spatial
+import quantities as pq
+from math import radians, cos, sin, asin, sqrt
+import quantities as pq
+
+
+
+def get_kilometers(d, unit):
+    q = float(d) * pq.CompoundUnit(unit)
+    q.units = pq.km
+    return q.magnitude
+
+def ConvertToRadian(input):
+    return input * math.pi / 180
+
+def get_level1(ent):
+    level_1 = re.search(regex_spatial.get_level1_regex(), ent)
+    if level_1 is not None:
+        return level_1.group()
+    return None
+def get_level2(ent):
+    level_2 = re.search(regex_spatial.get_level2_regex(), ent)
+    if level_2 is not None:
+        return level_2.group()
+    return None
+def get_level3(ent):
+    level_3 = re.search(regex_spatial.get_level3_regex(), ent)
+    if level_3 is not None:
+        return level_3.group()
+    return None
+
+def get_ase(ent):
+    abs_sp = ent.split("_")
+    return abs_sp[len(abs_sp)-1]
+
+def get_ent(ent):
+    return get_ase(ent), get_level1(ent), get_level2(ent), get_level3(ent)
+
+def get_centroid(coordinates, centroid, mini, maxi):
+    average = (mini + maxi)/2
+    diff = []
+    ind = 0
+    for p in coordinates:
+        diff.append(abs( p[2] - average))
+   
+    ind = diff.index(min(diff))    
+   
+    return midpoint(centroid[0], centroid[1], coordinates[ind][0], coordinates[ind][1], average)
+
+
+def calculateArea(coordinates):
+    area = 0
+    if (len(coordinates) > 2):
+        i = 0
+        for i in range(len(coordinates) - 1):
+            p1 = coordinates[i]
+            p2 = coordinates[i + 1]
+            area += math.radians(p2[0] - p1[0]) * (2 + math.sin(ConvertToRadian(p1[1])) + math.sin(math.radians(p2[0])))
+        
+        
+        area = area * 6378137 * 6378137 / 1000000
+    
+    area = abs(round(area, 2)) + 2
+    
+    return area
+
+def get_midmid_point(centroid, point1, point2, is_midmid):
+    mid1 = midpoint(centroid[0], centroid[1], 
+                            point1[0], point1[1]
+                            , point1[2])
+    mid2 = midpoint(centroid[0], centroid[1], 
+                            point2[0], point2[1],
+                            point2[2])
+    midmid1 = midpoint(centroid[0], centroid[1], 
+                            mid1[0], mid1[1]
+                            , mid1[2])
+    midmid2 = midpoint(centroid[0], centroid[1], 
+                            mid2[0], mid2[1],
+                            mid2[2])
+    if is_midmid:
+        return midmid1, midmid2
+    else:
+        return mid1, mid2
+
+def getPointByDistanceAngle(lat, ln, angle, distanceInKm):
+
+    R = 6378.1 #Radius of the Earth
+    brng = angle * math.pi /180 #Bearing is 90 degrees converted to radians.
+    d = distanceInKm #Distance in km
+    
+    #lat2  52.20444 - the lat result I'm hoping for
+    #lon2  0.36056 - the long result I'm hoping for.
+    
+    lat1 = math.radians(lat) #Current lat point converted to radians
+    lon1 = math.radians(ln) #Current long point converted to radians
+    
+    lat2 = math.asin( math.sin(lat1)*math.cos(d/R) +
+         math.cos(lat1)*math.sin(d/R)*math.cos(brng))
+    
+    lon2 = lon1 + math.atan2(math.sin(brng)*math.sin(d/R)*math.cos(lat1),
+                 math.cos(d/R)-math.sin(lat1)*math.sin(lat2))
+    
+    lat2 = math.degrees(lat2)
+    lon2 = math.degrees(lon2)
+    
+    return [lon2, lat2, angle]
+
+def midpoint(x1, y1, x2, y2, angle):
+
+    lonA = math.radians(y1)
+    lonB = math.radians(y2)
+    latA = math.radians(x1)
+    latB = math.radians(x2)
+
+    dLon = lonB - lonA
+
+    Bx = math.cos(latB) * math.cos(dLon)
+    By = math.cos(latB) * math.sin(dLon)
+
+    latC = math.atan2(math.sin(latA) + math.sin(latB),
+                  math.sqrt((math.cos(latA) + Bx) * (math.cos(latA) + Bx) + By * By))
+    lonC = lonA + math.atan2(By, math.cos(latA) + Bx)
+    lonC = (lonC + 3 * math.pi) % (2 * math.pi) - math.pi
+    latitude = round(math.degrees(latC), 8)
+    longitude = round(math.degrees(lonC),8)
+    return [latitude, longitude, angle]
+
+def midpoint(y1, x1, y2, x2, angle):
+
+    lonA = math.radians(y1)
+    lonB = math.radians(y2)
+    latA = math.radians(x1)
+    latB = math.radians(x2)
+
+    dLon = lonB - lonA
+
+    Bx = math.cos(latB) * math.cos(dLon)
+    By = math.cos(latB) * math.sin(dLon)
+
+    latC = math.atan2(math.sin(latA) + math.sin(latB),
+                      math.sqrt((math.cos(latA) + Bx) * (math.cos(latA) + Bx) + By * By))
+    lonC = lonA + math.atan2(By, math.cos(latA) + Bx)
+    lonC = (lonC + 3 * math.pi) % (2 * math.pi) - math.pi
+    latitude = round(math.degrees(latC), 8)
+    longitude = round(math.degrees(lonC) ,8)
+    print([longitude, latitude, angle], 'midmid')
+    return [longitude, latitude, angle
+
+    ]
+
+def calculate_bearing(pointA, pointB):
+  
+    if (type(pointA) != tuple) or (type(pointB) != tuple):
+        return 400
+    if (type(pointB[0]) != float) or (type(pointB[0]) != float):
+        return 400
+    
+    lat1 = math.radians(pointA[0])
+    lat2 = math.radians(pointB[0])
+
+    diffLong = math.radians(pointB[1] - pointA[1])
+
+    x = math.sin(diffLong) * math.cos(lat2)
+    y = math.cos(lat1) * math.sin(lat2) - (math.sin(lat1)
+            * math.cos(lat2) * math.cos(diffLong))
+
+    initial_bearing = math.atan2(x, y)
+
+  
+    initial_bearing = math.degrees(initial_bearing)
+    compass_bearing = (initial_bearing + 360) % 360
+
+    return compass_bearing
+
+def getPointByDistanceAngle(lat, ln, angle, distance, unit):
+    
+    #distanceInKm = distance
+    R = 6378.1 #Radius of the Earth
+    brng = float(angle) * math.pi /180 #Bearing is 90 degrees converted to radians.
+    d = get_kilometers(distance, unit) #Distance in km
+    
+    
+    lat1 = math.radians(lat) #Current lat point converted to radians
+    lon1 = math.radians(ln) #Current long point converted to radians
+    
+    lat2 = math.asin( math.sin(lat1)*math.cos(d/R) +
+         math.cos(lat1)*math.sin(d/R)*math.cos(brng))
+    
+    lon2 = lon1 + math.atan2(math.sin(brng)*math.sin(d/R)*math.cos(lat1),
+                 math.cos(d/R)-math.sin(lat1)*math.sin(lat2))
+    
+    lat2 = math.degrees(lat2)
+    lon2 = math.degrees(lon2)
+    
+    return (round(lon2,8), round(lat2,8), angle)
+
+
+def calculatePointByDistance(lat, ln, angle, distance, unit):
+    coff = 100/(6378*1.56)
+    kms = get_kilometers(distance, unit)
+    
+    d = kms * coff
+    
+    angle_x = math.cos( angle ) # * math.pi/180
+    angle_y = math.sin( angle) # * math.pi/180
+    lat_new =  lat + (d * angle_x) 
+    ln_new =  ln + (d * angle_y) 
+
+    return (round(ln_new,8), round(lat_new,8), angle)
+
+
+
+def pointByAngle(lat, ln, angle, distance, unit):
+
+    R = 6378.1 #Radius of the Earth
+    brng = angle * math.pi /180 #Bearing is 90 degrees converted to radians.
+    d = get_kilometers(distance, unit) #Distance in km
+    
+    #lat2  52.20444 - the lat result I'm hoping for
+    #lon2  0.36056 - the long result I'm hoping for.
+    
+    lat1 = math.radians(lat) #Current lat point converted to radians
+    lon1 = math.radians(ln) #Current long point converted to radians
+    
+    lat2 = math.asin( math.sin(lat1)*math.cos(d/R) +
+         math.cos(lat1)*math.sin(d/R)*math.cos(brng))
+    
+    lon2 = lon1 + math.atan2(math.sin(brng)*math.sin(d/R)*math.cos(lat1),
+                 math.cos(d/R)-math.sin(lat1)*math.sin(lat2))
+    
+    lat2 = math.degrees(lat2)
+    lon2 = math.degrees(lon2)
+    
+    return (lon2, lat2, angle)
+
+
+def getPointByDistance(lat, ln, angle, distance, unit):
+    kms = get_kilometers(distance, unit)
+    coef = kms / 111.32
+    new_lat = lat + coef
+    new_long = ln + coef / math.cos(lat * 0.01745)
+    return (round(new_lat,8), round(new_long,8), angle)
+
+def haversine(lon1, lat1, lon2, lat2):
+    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
+    dlon = lon2 - lon1 
+    dlat = lat2 - lat1 
+    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
+    c = 2 * asin(sqrt(a)) 
+    km = 6371* c
+    return km
+    

utils/llm_coding.py

@@ -0,0 +1,345 @@
+import requests
+import urllib3
+import json
+from utils import geoutil
+import regex_spatial
+from shapely.geometry import Polygon, MultiPoint, LineString, Point, mapping
+import re
+import geopandas as gpd
+from geocoder import geo_level1
+from openai import OpenAI
+from utils.config import api_key
+import numpy as np
+
+
+client = OpenAI(
+    api_key=api_key
+)
+
+
+model = "gpt-4o"
+
+north = ["north", "N'", "North", "NORTH"]
+south = ["south", "S'", "South", "SOUTH"]
+east = ["east", "E'", "East", "EAST"]
+west = ["west", "W'", "West", "WEST"]
+northeast = ["north-east", "NE'", "north east", "NORTH-EAST", "North East", "NORTH EAST"]
+southeast = ["south-east", "SE'", "south east", "SOUTH-EAST", "South East", "SOUTH EAST"]
+northwest = ["north-west", "NW'", "north west", "NORTH-WEST", "North West", "NORTH WEST"]
+southwest = ["south-west", "SW'", "south west", "SOUTH-WEST", "South West", "SOUTH WEST"]
+center = ["center","central", "downtown","midtown"]
+
+
+def to_standard_2d_list(data):
+    arr = np.array(data)
+    flat = arr.flatten()
+    if flat.size % 2 != 0:
+        raise ValueError("元素个数不是2的倍数，不能 reshape 成 [N, 2] 格式")
+
+    return flat.reshape(-1, 2).tolist()
+
+
+def get_geojson(ent, arr, centroid):
+    poly_json = {}
+    poly_json['type'] = 'FeatureCollection'
+    poly_json['features'] = []
+    coordinates= []
+    coordinates.append(arr)
+    poly_json['features'].append({
+    'type':'Feature',
+    'id': ent,
+    'properties': {
+        'centroid': centroid
+        },
+    'geometry': {
+        'type':'Polygon',
+        'coordinates': coordinates
+        }
+    })
+    return poly_json
+
+
+def get_coordinates(ent):
+    request_url = 'https://nominatim.openstreetmap.org/search.php?q= ' +ent +'&polygon_geojson=1&accept-language=en&format=jsonv2'
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/18.3 Safari/605.1.15"
+    }
+    page = requests.get(request_url, headers=headers, verify=False)
+    json_content = json.loads(page.content)
+    all_coordinates = json_content[0]['geojson']['coordinates'][0]
+    centroid = (float(json_content[0]['lon']), float(json_content[0]['lat']))
+    for p in all_coordinates:
+        p2 = (p[0], p[1])
+        angle = geoutil.calculate_bearing(centroid, p2)
+        p.append(angle)
+
+    geojson = get_geojson(ent, all_coordinates, centroid)
+
+    return geojson['features'][0]['geometry']['coordinates'][0], geojson['features'][0]['properties']['centroid']
+
+def get_coordinates(location):
+    request_url = f'https://nominatim.openstreetmap.org/search.php?q={location}&polygon_geojson=1&accept-language=en&format=jsonv2'
+
+    print(request_url)
+    headers = {"User-Agent": "Mozilla/5.0"}
+    response = requests.get(request_url, headers=headers, verify=False)
+    json_content = json.loads(response.content)
+    if json_content[0]['geojson']['type'] == 'Polygon':
+        coordinates = json_content[0]['geojson']['coordinates'][0]
+    elif json_content[0]['geojson']['type'] == 'Point':
+        coordinates = json_content[0]['geojson']['coordinates']
+    else:
+        print(json_content[0]['geojson']['type'])
+    centroid = (float(json_content[0]['lon']), float(json_content[0]['lat']))
+    return (coordinates, centroid)
+
+
+# level3
+def get_directional_coordinates_by_angle(coordinates, centroid, direction, minimum, maximum):
+    direction_coordinates = []
+    for p in coordinates:
+        angle = geoutil.calculate_bearing(centroid, p)
+        p2 = (p[0], p[1], angle)
+        if direction in geo_level1.east:
+            if angle >= minimum or angle <= maximum:
+                direction_coordinates.append(p2)
+
+        else:
+            if angle >= minimum and angle <= maximum:
+                direction_coordinates.append(p2)
+    return direction_coordinates
+def get_level3(level3):
+    digits = re.findall('[0-9]+', level3)[0]
+    unit = re.findall('[A-Za-z]+', level3)[0]
+    return digits, unit
+
+def get_direction_coordinates(coordinates, centroid, level1):
+    min_max = geo_level1.get_min_max(level1)
+    if min_max is not None:
+        coord = get_directional_coordinates_by_angle(coordinates, centroid, level1, min_max[0], min_max[1])
+        return coord
+    return coordinates
+def sort_west(poly1, poly2, centroid):
+    coords1 = mapping(poly1)["features"][0]["geometry"]["coordinates"]
+    coords2 = mapping(poly2)["features"][0]["geometry"]["coordinates"]
+    coord1 = []
+    coord2 = []
+    coord = []
+    for c in coords1:
+        pol = list(c[::-1])
+        coord1.extend(pol)
+    for c in coords2:
+        pol = list(c[::-1])
+        coord2.extend(pol)
+    coo1 = []
+    coo2 = []
+    for p in coord1:
+        angle = geoutil.calculate_bearing(centroid, p)
+        if angle >= 157 and angle <= 202:
+            coo1.append((p[0], p[1], angle))
+    for p in coord2:
+        angle = geoutil.calculate_bearing(centroid, p)
+        if angle >= 157 and angle <= 202:
+            coo2.append((p[0], p[1], angle))
+    coo1.extend(coo2)
+    return coo1
+
+
+def get_level3_coordinates(coordinates, level_3, level1):
+    distance, unit = get_level3(level_3)
+    kms = geoutil.get_kilometers(distance, unit)
+    coord = []
+
+    coords0, center = coordinates
+
+    if not isinstance(coords0, list) or len(coords0) < 3:
+        lat_km = 111.32
+        lon_km = 111.32 * np.cos(np.radians(center[1]))
+
+        dx = dy = 0
+
+        if level1 is not None:
+            if level1 in geo_level1.east:
+                dx = kms / lon_km
+            elif level1 in geo_level1.west:
+                dx = -kms / lon_km
+            elif level1 in geo_level1.north:
+                dy = kms / lat_km
+            elif level1 in geo_level1.south:
+                dy = -kms / lat_km
+        new_center = (center[0] + dx, center[1] + dy)
+        r_km = 1
+
+        circle_points = []
+        for theta in np.linspace(0, 360, num=100):
+            theta_rad = np.radians(theta)
+            d_lat = (np.sin(theta_rad) * r_km) / lat_km
+            d_lon = (np.cos(theta_rad) * r_km) / lon_km
+            circle_points.append((new_center[0] + d_lon, new_center[1] + d_lat))
+
+        if circle_points:
+            center_point = MultiPoint(circle_points).centroid
+            center = (center_point.x, center_point.y)
+        else:
+            center = new_center
+
+        return circle_points, center
+
+    poly1 = Polygon(coords0)
+    polygon1 = gpd.GeoSeries(poly1)
+
+    poly2 = polygon1.buffer(0.0095 * kms, join_style=2)
+    poly3 = polygon1.buffer(0.013 * kms, join_style=2)
+    poly = poly3.difference(poly2)
+
+    coords = mapping(poly)["features"][0]["geometry"]["coordinates"]
+    for c in coords:
+        pol = list(c[::-1])
+        coord.extend(pol)
+
+    if level1 is not None:
+        coord = get_direction_coordinates(coord, coordinates[1], level1)
+        if level1 in geo_level1.west:
+            coord = sort_west(poly3, poly2, coordinates[1])
+
+    if coord:
+        center_point = MultiPoint(coord).centroid
+        center = (center_point.x, center_point.y)
+    else:
+        center = coordinates[1]
+
+    return coord, center
+
+# between
+def get_between_coordinates(coordinates1, coordinates2):
+
+    def is_valid_polygon(coords):
+        return isinstance(coords, list) and len(coords) >= 3
+
+    coords1, center1 = coordinates1
+    coords2, center2 = coordinates2
+
+    if is_valid_polygon(coords1):
+        poly1 = Polygon(coords1)
+        area1 = poly1.area
+    else:
+        area1 = 0
+
+    if is_valid_polygon(coords2):
+        poly2 = Polygon(coords2)
+        area2 = poly2.area
+    else:
+        area2 = 0
+
+    midpoint = (
+        (center1[0] + center2[0]) / 2,
+        (center1[1] + center2[1]) / 2
+    )
+
+    if area1 == 0 and area2 == 0:
+        r_km = 2
+    else:
+        avg_area = (area1 + area2) / 2
+        r_km = np.sqrt(avg_area / np.pi) * 111.32  # 近似 km 半径
+
+    lat_km = 111.32
+    lon_km = 111.32 * np.cos(np.radians(midpoint[1]))
+
+    circle_points = []
+    for theta in np.linspace(0, 360, num=100):
+        theta_rad = np.radians(theta)
+        d_lat = (np.sin(theta_rad) * r_km) / lat_km
+        d_lon = (np.cos(theta_rad) * r_km) / lon_km
+        circle_points.append((midpoint[0] + d_lon, midpoint[1] + d_lat))
+
+    return circle_points, midpoint
+
+
+def llmapi(text):
+    system_prompt = (
+        "You are an experienced geographer. Your task is to determine the correct sequence of positioning functions and their inputs based on a given piece of natural language.\n"
+        "The positioning functions you can choose from are:\n"
+        "1. Relative Positioning: Inputs is (location coordinate or location name, direction, and distance). Outputs the coordinates that are in the given 'direction' and 'distance' from the input location.\n"
+        "2. Between Positioning: Inputs is (location 1 coordinates or location 1 name, location 2 coordinates or location 2 name). Outputs the midpoint coordinate between the two locations.\n"
+        "You can only use the given functions, and the inputs to the functions must obey the above properties. The given functions can be combined to solve complex situations."
+        "First, perform chain-of-thought (CoT) reasoning, and finally output your answer in JSON format, wrapped between `<<<JSON>>>` and `<<<END>>>`.\n"
+        "Make sure all inputs only include: location names (strings), step indices (integers), directions (strings, must be in English), or distances (strings with units). Do not return expressions like 'the coordinate 4 km south of Chatswood'.\n"
+        "Each step must have an 'id'. If the input of a step is the output of a previous step, use that step’s 'id' as the input.\n"
+        "All directions must be in English (e.g., south, west, northeast, etc.).\n"
+        "Example output:\n"
+        "<<<JSON>>>\n"
+        "[{\"id\": 1, \"function\": \"Relative\", \"inputs\": [\"Chatswood\", \"south\", \"4 km\"]},"
+        "{\"id\": 2, \"function\": \"Relative\", \"inputs\": [\"North Sydney\", \"west\", \"2 km\"]},"
+        "{\"id\": 3, \"function\": \"Between\", \"inputs\": [1, 2]},"
+        "{\"id\": 4, \"function\": \"Relative\", \"inputs\": [3, \"southwest\", \"5 km\"]}]\n"
+        "<<<END>>>")
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": text},
+    ]
+
+    chat_completion = client.chat.completions.create(
+        messages=messages,
+        model=model,
+    )
+
+    result = chat_completion.choices[0].message.content
+
+    json_match = re.search(r'<<<JSON>>>\n(.*?)\n<<<END>>>', result, re.DOTALL)
+
+    if json_match:
+        return json.loads(json_match.group(1))
+    else:
+        raise ValueError("The LLM output does not contain the expected JSON formatted data. Please try again.")
+
+
+
+
+
+def execute_steps(steps):
+    data = {}
+    locations_history = []
+    for step in steps:
+        step_id = step['id']
+        function = step['function']
+        inputs = step['inputs']
+        resolved_inputs = []
+        for inp in inputs:
+            if isinstance(inp, int):
+                resolved_inputs.append(data[inp])
+            else:
+                resolved_inputs.append(inp)
+        if function == "Relative":
+            location, direction, distance = resolved_inputs
+            if isinstance(location, str):
+                location = get_coordinates(location)
+                locations_history.append(location)
+            location = [to_standard_2d_list(location[0])] + list(location[1:])
+            result = get_level3_coordinates(location, distance, direction)
+            locations_history.append(result)
+            data[step_id] = result
+        elif function == "Between":
+            location1, location2 = resolved_inputs
+
+            if isinstance(location1, str):
+                location1 = get_coordinates(location1)
+                locations_history.append(location1)
+                location1 = [to_standard_2d_list(location1[0])] + list(location1[1:])
+            if isinstance(location2, str):
+
+                location2 = get_coordinates(location2)
+                locations_history.append(location2)
+                location2 = [to_standard_2d_list(location2[0])] + list(location2[1:])
+            result = get_between_coordinates(location1, location2)
+            locations_history.append(result)
+            data[step_id] = result
+
+    return [data, locations_history]
+
+
+
+if __name__ == '__main__':
+    parsed_steps = []
+    step_loc = execute_steps(parsed_steps)
+    result = step_loc[0]

utils/llm_ent_extract.py

@@ -0,0 +1,145 @@
+from openai import OpenAI
+import re
+from utils.config import api_key
+
+client = OpenAI(
+    api_key=api_key
+)
+
+model = "gpt-3.5-turbo"
+model = "gpt-4o"
+
+def extract_GPE(text):
+    system_prompt = '''You are a professional geographer. Your task is to extract all geopolitical entities from a given text. Geopolitical entities can include countries, regions, cities, autonomous regions, or other administrative divisions. For each geopolitical entity, wrap the name in a unique character sequence, such as [###ENTITY###]. If there are multiple entities, output them in the following format:\n[###ENTITY1###, ###ENTITY2###, ###ENTITY3###]\nHere is an example:\n Example:\n\nText:\n"China and India are two of the most populous countries in Asia."\n\nExpected Output:\n[###China###, ###India###]'''
+    sent = 'Where is France?'
+
+    math_bot_messages = [
+        {"role": "system",
+         "content": system_prompt},
+        {"role": "user", "content": text},
+    ]
+
+    chat_completion = client.chat.completions.create(
+        messages=math_bot_messages,
+        model=model,
+    )
+
+    result = chat_completion.choices[0].message.content
+    return result
+
+
+def extract_LOC(text):
+    system_prompt = '''You are a professional geographer. Your task is to extract all location entities (LOC) from a given text. Location entities can include physical locations such as landmarks, geographical features, mountains, rivers, oceans, and places, but do not include political or administrative divisions such as countries or cities (these are considered geopolitical entities). For each location entity, wrap the name in a unique character sequence, such as [###ENTITY###]. If there are multiple entities, output them in the following format:\n[###ENTITY1###, ###ENTITY2###, ###ENTITY3###]\nHere is an example:\n Example:\n\nText:\n"China and India are two of the most populous countries in Asia."\n\nExpected Output:\n[###China###, ###India###]'''
+    sent = 'The Grand Canyon is one of the most spectacular natural wonders in the world, located in the state of Arizona. Nearby, the Colorado River flows through the canyon, carving its way through the rugged terrain. In the north, the Rocky Mountains stretch across several states, including Colorado and Wyoming.'
+
+    math_bot_messages = [
+        {"role": "system",
+         "content": system_prompt},
+        {"role": "user", "content": text},
+    ]
+
+    chat_completion = client.chat.completions.create(
+        messages=math_bot_messages,
+        model=model,
+    )
+
+    result = chat_completion.choices[0].message.content
+    return result
+
+
+def extract_RSE_1(text):
+    system_prompt = '''You are a professional geographer. Your task is to extract all spatial entities (directional keywords) from a given text. Spatial entities can include directional keywords such as north, south, east, west, and more specific terms like northeast, northwest, southeast, southwest, as well as terms indicating locations like center, central, downtown, and midtown. For each spatial entity, wrap the name in a unique character sequence, such as [###ENTITY###]. If there are multiple entities, output them in the following format:\n[###ENTITY1###, ###ENTITY2###, ###ENTITY3###]\nHere is an example:\n Example:\n\nText:\n"The hotel is located in the downtown area of New York, just south of Central Park, with a beautiful view of the southeast corner."\n\nExpected Output:\n[###downtown###, ###south###, ###southeast###]'''
+    sent = 'The train station is situated in the central part of the city, just north of the river and east of the main square.'
+
+    math_bot_messages = [
+        {"role": "system",
+         "content": system_prompt},
+        {"role": "user", "content": text},
+    ]
+
+    chat_completion = client.chat.completions.create(
+        messages=math_bot_messages,
+        model=model,
+    )
+
+    result = chat_completion.choices[0].message.content
+    return result
+
+
+def extract_RSE_2(text):
+    system_prompt = '''You are a professional geographer. Your task is to extract all fuzzy spatial entities (keywords) from a given text. Fuzzy spatial keywords can include terms like nearby, near, vicinity, close, beside, next, adjacent, immediate, border, surrounding, neighbourhood, proximity, territory, locality, and similar terms. For each fuzzy spatial keyword, wrap the name in a unique character sequence, such as [###ENTITY###]. If there are multiple entities, output them in the following format:\n[###ENTITY1###, ###ENTITY2###, ###ENTITY3###]\nHere is an example:\n Example:\n\nText:\n"The park is located nearby the lake, with several cafes close to the walking paths, and a small garden adjacent to the main entrance."\n\nExpected Output:\n[###nearby###, ###close###, ###adjacent###]'''
+    sent = 'The village is situated in the vicinity of the mountain range, with a small river flowing beside the houses and several farms next to the road.'
+
+    math_bot_messages = [
+        {"role": "system",
+         "content": system_prompt},
+        {"role": "user", "content": text},
+    ]
+
+    chat_completion = client.chat.completions.create(
+        messages=math_bot_messages,
+        model=model,
+    )
+
+    result = chat_completion.choices[0].message.content
+    return result
+
+
+def extract_RSE_3(text):
+    system_prompt = '''You are a professional geographer. Your task is to extract all fuzzy distance keywords from a given text. Fuzzy distance keywords include numeric values followed by distance units such as kilometer, mile, meter, foot, inch, centimeter, and other related units. The distance units can be in different formats, such as km, m, mi, ft, yd, cm, mm, or even in full words like kilometer, mile, or inch. For each fuzzy distance keyword, wrap the entire expression (number and unit) in a unique character sequence, such as [###ENTITY###]. If there are multiple entities, output them in the following format:\n[###ENTITY1###, ###ENTITY2###, ###ENTITY3###]\nHere is an example:\n Example:\n\nText:\n"The park is located 3 km away from the city center, while the nearest supermarket is only 500 meters from here, and the lake is about 1 mile further down the road."\n\nExpected Output:\n[###3 km###, ###500 meters###, ###1 mile###]'''
+    sent = 'The school is located approximately 2 miles from the station, while the nearest bus stop is 200 meters away. The hiking trail is about 5 kilometers east of the town center.'
+
+    math_bot_messages = [
+        {"role": "system",
+         "content": system_prompt},
+        {"role": "user", "content": text},
+    ]
+
+    chat_completion = client.chat.completions.create(
+        messages=math_bot_messages,
+        model=model,
+    )
+
+    result = chat_completion.choices[0].message.content
+    return result
+
+
+def extract(respond, entity_type):
+    """
+    从输入字符串中提取被 ### 包裹的实体，并将其映射到给定的实体类型。
+
+    :param respond: 包含实体的字符串，例如 '[###2 miles###, ###200 meters###, ###5 kilometers###]'
+    :param entity_type: 给所有提取的实体赋予的类型（字符串）
+    :return: 一个字典，键是提取出的实体，值是相同的 entity_type
+    """
+    # 提取被 ### 包裹的内容
+    extracted_entities = re.findall(r'###(.*?)###', respond)
+
+    # 生成字典，将所有实体映射到同一个类型
+    entity_dict = {entity.strip(): entity_type for entity in extracted_entities}
+
+    return entity_dict
+
+
+def llmapi(text):
+    system_prompt = '请你填入需要的提示'
+    math_bot_messages = [
+        {"role": "system",
+         "content": system_prompt},
+        {"role": "user", "content": text},
+    ]
+
+    chat_completion = client.chat.completions.create(
+        messages=math_bot_messages,
+        model=model,
+    )
+
+    result = chat_completion.choices[0].message.content
+    return result
+
+
+# print(extract('[###2 miles###, ###200 meters###, ###5 kilometers###]', 'rse'))
+# print(extract(extract_GPE('Between Burwood and Glebe.'), 'gpe'))
+
+
+