1_SpatialParse.py

# 这不会失败
import subprocess
subprocess.run(["pip", "install", "streamlit"])
import streamlit


# import subprocess
# import importlib.util
# import os

# # 只在 geospacy 没有被安装时执行安装（避免重复装）
# if importlib.util.find_spec("geospacy") is None:
#     subprocess.run(
#         ["pip", "install", "--no-deps", "-r", "requirements_geospacy.txt"],
#         check=True
#     )


# 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
# import llm_coding
# import urllib.parse


# 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.experimental_get_query_params()
#     # params = st.query_params
#     # print(params, 777)

#     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.experimental_get_query_params()
#     # params = st.query_params

#     if "text" not in params:
#         text = st.text_area("Input unstructured text:", "")
#     else:
#         text = st.text_area("Enter the text to extract {Spatial Entities}", params["text"][0])
#     if(st.button("Extract")):

#         # return 'France has detected a highly pathogenic strain of bird flu in a pet shop near Paris, days after an identical outbreak in one of Corsica’s main cities.'


#         return 'I would like to know where is the area between Burwood and Glebe. Pyrmont.'
#         return '5 km east of Burwood. 3 km south of Glebe. Between Pyrmont and Glebe.'
#         # return 'Between Burwood and Pyrmont.'
#         # return 'Between Burwood and Glebe.'
#         # return 'Between Burwood and Darling Harbour.'
#         # return 'Between China and USA.'
#         # return 'The Burwood city.'
#         # text = "New York is north of Washington. Between Burwood and Pyrmont city."
#         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 = en_core_web_md.load()

#     # nlp = spacy.load("en_core_web_md")
#     # nlp.add_pipe("spatial_pipeline", after="ner")
#     # doc = nlp(text)
#     # doc = set_selected_entities(doc)
#     # 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)

#     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                              # 记录当前 token 偏移量
#     sent_start_positions = [0]              # 记录句子信息
#     doc_copy = doc.copy()                   # 用于展示方程组合
#     for sent in doc.sents:

#         sent_doc = nlp(sent.text)  # 逐句处理
#         sent_doc = set_selected_entities(sent_doc)  # 这里处理实体
#         sent_texts.append(sent_doc.text)

#         for ent in sent_doc.ents:
#             sent_rse_id.append(ent._.rse_id)
#         # **调整每个实体的索引，使其匹配完整文本**
#         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)  # 更新偏移量
#         sent_start_positions.append(sent_start_positions[-1] + len(sent))           # 记录句子起点
#     # **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:                      # 手动标记句子起始点
#         if i < len(final_doc):
#             final_doc[i].is_sent_start = True
#     # **设置实体**
#     final_doc.set_ents(sent_ents)

#     for i in range(len(sent_rse_id)):
#         final_doc.ents[i]._.rse_id = sent_rse_id[i]
#     print(doc.ents[0].sent, '原始')
#     doc = final_doc
#     print(doc.ents[0].sent, '新')
#     # 分句处理完毕

#     # doc = set_selected_entities(doc)
#     doc.to_disk("saved_doc.spacy")




#     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)

#     st.markdown("123123")

#     show_sentence_selector_table(doc_copy)

# def show_sentence_selector_table(doc_copy):
#     st.markdown("**______________________________________________________________________________________**")
#     st.markdown("**Sentence Selector for Geographic Composition**")

#     # 提取句子
#     sentences = list(doc_copy.sents)

#     # 构建表格数据
#     rows = []
#     for idx, sent in enumerate(sentences):
#         sentence_text = sent.text.strip()
#         # 生成跳转链接（定位到Tagger）
#         url = BASE_URL + "Tagger?mode=geocombo&text=" + urllib.parse.quote(sentence_text)
#         new_row = {
#             'Sr.': idx + 1,
#             'sentence': sentence_text,
#             'Select': f'<a target="_self" href="{url}">Select this sentence</a>'
#         }
#         rows.append(new_row)

#     # 转为 DataFrame 并渲染为 HTML
#     df = pd.DataFrame(rows)
#     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**")

#         # 初始化一个空 DataFrame
#         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 + "Tagger?map=true&type=" + types + "&model=" + model + "&text=" + text + "&entity=" + ent._.rse_id
#             print(url_map, 'uuurrr')
#             print(ent._.rse_id, 'pppp')
#             url_json = BASE_URL + "Tagger?geojson=true&type=" + types + "&model=" + model + "&text=" + text + "&entity=" + ent._.rse_id

#             # 创建新行
#             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)  # 将新行添加到列表中

#         # 将所有行转为 DataFrame
#         df = pd.DataFrame(rows)

#         # 使用 Streamlit 显示 HTML 表格
#         st.write(df.to_html(escape=False, index=False), unsafe_allow_html=True)

#     # params = st.experimental_get_query_params()
#     # params = st.query_params
#     # ase, level_1, level_2, level_3 = geoutil.get_ent(params["entity"][0])
#     # print(geoutil.get_ent(params), 'ppppp')

# def set_header():       # tetis Geospacy LOGO
#     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(
#         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">SpatialParse</p>
#         </div>
#         """,
#         unsafe_allow_html=True
#     )


# def set_side_menu():
#     global gpe_selected, loc_selected, rse_selected, model, types
#     types = ""
#     params = st.experimental_get_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
#     #print(displacy.templates.TPL_ENT)
#     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()