Update 1_SpatialParse.py

1_SpatialParse.py

@@ -1,416 +1,426 @@
-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
-    )
 
+# 这不会失败
+def main():
+    import streamlit
+subprocess.run(["pip", "install", "streamlit"])
+main()
+
+
+
+
+# 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']
-
+
+# 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')
 
+#     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]
+#     model = st.sidebar.selectbox('LLM Model', models, index=default_ix)
 
-    st.sidebar.markdown("### Absolute Spatial Entity:")
-    if "g" in tpes:
-        gpe = st.sidebar.checkbox('GPE', value=True)
-    else:
-        gpe = st.sidebar.checkbox('GPE')
+#     st.sidebar.markdown("## Spatial Entity Labels")
 
-    if "l" in tpes:
-        loc = st.sidebar.checkbox('LOC', value=True)
-    else:
-        loc = st.sidebar.checkbox('LOC')
+#     st.sidebar.markdown("Please **Mark** the Spatial Entities you want to extract.")
+#     tpes = ""
+#     if "type" in params:
+#         tpes = params['type'][0]
 
-    st.sidebar.markdown("### Relative Spatial Entity:")
+#     st.sidebar.markdown("### Absolute Spatial Entity:")
+#     if "g" in tpes:
+#         gpe = st.sidebar.checkbox('GPE', value=True)
+#     else:
+#         gpe = st.sidebar.checkbox('GPE')
 
-    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 "l" in tpes:
+#         loc = st.sidebar.checkbox('LOC', value=True)
+#     else:
+#         loc = st.sidebar.checkbox('LOC')
 
-    if (loc):
-        loc_selected = "LOC"
-        types += "l"
+#     st.sidebar.markdown("### Relative Spatial Entity:")
 
-    if (rse):
-        rse_selected = "RSE"
-        types += "r"
+#     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()
+# 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(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()
+# if __name__ == '__main__':
+#     main()