kg done

README.md

@@ -9,12 +9,40 @@ app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Medical Knowledge Graph Construction (medKGC)
 
+## English Version
 
-用streamlit做一个网站。
-1. 输入是文本。
-   1. 先用mockData.json的text测试。
-2. 功能是进行调用api对文本进行实体识别，得到list of entity。
-   1. 先mock这个功能。输出mockData.json的entities。
-3. 一个输出模块，使用streamlit_text_label，高亮文本中的list of entity。
+medKGC is a Streamlit-based application for medical text analysis and knowledge graph construction. It demonstrates the process of entity recognition and relation extraction from medical texts, visualizing the results in an interactive graph.
+
+### Features:
+1. Text Input: Users can input medical text for analysis.
+2. Entity Recognition: The app identifies and highlights various medical entities such as diseases, procedures, anatomy, etc.
+3. Relation Extraction: It extracts relationships between the identified entities.
+4. Interactive Visualization: Utilizes streamlit-agraph to create an interactive graph representation of entities and their relationships.
+5. Labeled Text Display: Shows the input text with highlighted entities using streamlit_text_label.
+
+### How to Use:
+1. Enter medical text in the provided text area.
+2. Click "Recognize Entities" to process the text.
+3. View the recognized entities, extracted relations, and the entity relationship graph.
+
+Note: Currently, the app uses mock data for demonstration purposes. Integration with actual NLP models is planned for future development.
+
+## 中文版本
+
+medKGC 是一个基于 Streamlit 的医疗文本分析和知识图谱构建应用。它演示了从医疗文本中进行实体识别和关系提取的过程，并将结果以交互式图形可视化。
+
+### 功能特点：
+1. 文本输入：用户可以输入医疗文本进行分析。
+2. 实体识别：应用程序识别并高亮显示各种医疗实体，如疾病、医疗程序、解剖结构等。
+3. 关系提取：提取识别出的实体之间的关系。
+4. 交互式可视化：使用 streamlit-agraph 创建实体及其关系的交互式图形表示。
+5. 标记文本显示：使用 streamlit_text_label 显示带有高亮实体的输入文本。
+
+### 使用方法：
+1. 在提供的文本区域输入医疗文本。
+2. 点击"识别实体"按钮处理文本。
+3. 查看识别出的实体、提取的关系以及实体关系图。
+
+注意：目前，应用程序使用模拟数据进行演示。未来开发计划将集成实际的自然语言处理模型。

app.py

@@ -1,6 +1,15 @@
 import streamlit as st
 from streamlit_text_label import label_select, Selection
+from streamlit_agraph import agraph, Node, Edge, Config
 
+# Import Relation class
+from dataclasses import dataclass
+
+@dataclass
+class Relation:
+    source: Selection
+    target: Selection
+    label: str
 
 def mock_entity_recognition(text):
     # Simulate entity recognition functionality
@@ -18,6 +27,31 @@ def mock_entity_recognition(text):
     return entities
 
 
+def mock_relation_extraction(entities):
+    # Simulate relation extraction functionality
+    relations = [
+        Relation(source=entities[0],
+                 target=entities[1], label="DISEASE_CAUSE"),
+        Relation(source=entities[1], target=entities[2],
+                 label="PROCEDURE_EFFECT"),
+        Relation(source=entities[2], target=entities[3], label="EXAM_RESULT"),
+        Relation(source=entities[3], target=entities[4],
+                 label="SEVERITY_LEVEL"),
+        Relation(source=entities[4], target=entities[5],
+                 label="ANATOMY_LOCATION"),
+    ]
+
+    return relations
+
+
+def create_graph(entities, relations):
+    nodes = [Node(id=e.text, label=e.text, size=25, color=f"#{hash(e.labels[0]) % 0xFFFFFF:06x}") for e in entities]
+    edges = [Edge(source=r.source.text, target=r.target.text, label=r.label) for r in relations]
+    
+    config = Config(width=750, height=500, directed=True, physics=True, hierarchical=False)
+    return agraph(nodes=nodes, edges=edges, config=config)
+
+
 def main():
     st.title("Medical Text Entity Recognition")
 
@@ -41,6 +75,17 @@ def main():
         for entity in entities:
             st.write(f"{entity.text} ({entity.labels[0]})")
 
+        # 4. Call the simulated relation extraction function
+        relations = mock_relation_extraction(entities)
+
+        # 5. Display relations
+        st.subheader("Extracted Relations:")
+        for relation in relations:
+            st.write(f"{relation.source.text} --{relation.label}--> {relation.target.text}")
+
+        # 6. Create and display graph using streamlit-agraph
+        st.subheader("Entity Relationship Graph:")
+        create_graph(entities, relations)
 
 if __name__ == "__main__":
     main()

requirements.txt

@@ -1,2 +1,3 @@
 streamlit
 streamlit_text_label
+streamlit-agraph