Update app.py

app.py

@@ -7,14 +7,238 @@ import pandas as pd
 
 
 
+# nlp = spacy.load("en_core_web_md")
+# def process_api(input_text):
+#     # 这里编写实际的后端处理逻辑
+
+#     return {
+#         "status": "success",
+#         # "result": f"Processed: {input_text.upper()}",
+#         "result": f"Processed: {nlp(input_text).to_json()}",
+#         "timestamp": time.time()
+#     }
+
+# # 设置API格式为JSON
+# gr.Interface(
+#     fn=process_api,
+#     inputs="text",
+#     outputs="json",
+#     title="Backend API",
+#     allow_flagging="never"
+# ).launch()
+
+
+
+
+# nlp = spacy.import gradio as gr
+import time
+import spacy
+from spacy.tokens import Span, Doc, Token
+from spacy import displacy
+import streamlit as st
+import pandas as pd
+from spacy.language import Language
+import llm_ent_extract
+import regex_spatial
+import re
+
+
+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>"""
+BASE_URL = ""
+model = ""
+types = ""
 nlp = spacy.load("en_core_web_md")
+gpe_selected = 'GPE'
+loc_selected = 'loc'
+rse_selected = 'rse'
+rse_id = "rse_id"
+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 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 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 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 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')
+  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 extract_spatial_entities(text):
+
+
+    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]
+
+    doc = final_doc
+    return doc.to_json()
+
+
+
 def process_api(input_text):
     # 这里编写实际的后端处理逻辑
 
     return {
         "status": "success",
         # "result": f"Processed: {input_text.upper()}",
-        "result": f"Processed: {nlp(input_text).to_json()}",
+        # "result": f"Processed: {nlp(input_text).to_json()}",
+        "result": f"Processed: {extract_spatial_entities(input_text)}",
         "timestamp": time.time()
     }
 
@@ -25,36 +249,4 @@ gr.Interface(
     outputs="json",
     title="Backend API",
     allow_flagging="never"
-).launch()
-
-
-
-
-# nlp = spacy.load("en_core_web_md")
-# HTML_WRAPPER = "<div style='padding: 10px;'>{}</div>"
-
-# def show_spatial_ent_table(doc):
-#     rows = []
-#     for i, ent in enumerate(doc.ents):
-#         rows.append(f"<tr><td>{i+1}</td><td>{ent.text}</td><td>{ent.label_}</td></tr>")
-#     table_html = "<table border='1'><tr><th>Index</th><th>Entity</th><th>Label</th></tr>" + "".join(rows) + "</table>"
-#     return table_html
-
-# def process_api(input_text):
-#     doc = nlp(input_text)
-#     html_ent = displacy.render(doc, style="ent")
-#     html_ent = HTML_WRAPPER.format(html_ent.replace("\n", ""))
-#     html_table = show_spatial_ent_table(doc)
-#     final_html = html_ent + "<br>" + html_table
-#     return {
-#         "data": [{"html": final_html}],
-#         "timestamp": time.time()
-#     }
-
-# gr.Interface(
-#     fn=process_api,
-#     inputs="text",
-#     outputs="json",
-#     allow_flagging="never",
-#     title="Backend API"
-# ).launch()
+).launch()