Enhance report input handling and UI updates

- Added a new input selection method to allow users to choose between dataset selection and manual text input.
- Updated the report content display function to handle both dictionary and string formats for report data.
- Integrated user input processing for radiology reports, including error handling for empty inputs.
- Adjusted the main application flow to accommodate the new input method and ensure proper entity display and review submission.

This commit improves user experience by providing more flexible input options and enhances the overall functionality of the application.

app.py

@@ -5,7 +5,8 @@ from app_ui import (
     display_report_content,
     display_entities,
     display_relationship_graph,
-    handle_review_submission
+    handle_review_submission,
+    setup_input_selection
 )
 
 def initialize_session_state():
@@ -29,15 +30,28 @@ def main():
     # Initialize session state
     initialize_session_state()
 
-    # Setup report selection
-    selected_report = setup_report_selection()
-
-    if selected_report:
-        report_data = st.session_state.reports_json[selected_report]
-        entities_data = report_data['entities']
-        
-        # Setup entity annotation
-        selections_og = entities2Selection(report_data['text'], entities_data)
+    # 获取输入方式
+    input_selection = setup_input_selection()
+    
+    if input_selection:
+        if input_selection["type"] == "dataset":
+            # 原有的数据集选择逻辑
+            selected_report = setup_report_selection()
+            if selected_report:
+                report_data = st.session_state.reports_json[selected_report]
+                entities_data = report_data['entities']
+                selections_og = entities2Selection(report_data['text'], entities_data)
+        else:
+            # 处理用户输入的文本
+            user_text = input_selection["text"]
+            if user_text:
+                from ie import process_sample
+                entities_data = process_sample(user_text, num_shots=5)
+                report_data = user_text
+                selections_og = entities2Selection(user_text, entities_data)
+            else:
+                st.warning("请输入文本内容")
+                return
 
         # 创建两列布局
         col1, col2 = st.columns([2, 2])  # 调整列宽比例
@@ -53,10 +67,14 @@ def main():
 
         # Display entities
         with col2:
-            selections = display_entities(report_data['text'], selections_og)
+            selections = display_entities(
+                report_data['text'] if isinstance(report_data, dict) else report_data, 
+                selections_og
+            )
 
-        # Handle review submission
-        handle_review_submission(selected_report, selections, entities_data)
+        # 仅对数据集的报告显示提交按钮
+        if input_selection["type"] == "dataset":
+            handle_review_submission(selected_report, selections, entities_data)
 
 if __name__ == "__main__":
     main()

app_ui.py

@@ -96,7 +96,10 @@ def setup_report_selection():
 def display_report_content(report_data):
     """Display the report text content"""
     st.subheader("Report Content:")
-    st.markdown(report_data['text'])
+    if isinstance(report_data, dict):
+        st.markdown(report_data['text'])
+    else:
+        st.markdown(report_data)
 
 
 def display_entities(report_text, entities):
@@ -137,3 +140,27 @@ def handle_review_submission(selected_report, selections, entities_data):
         save_data(st.session_state.reports_json)
         st.success("Review status saved!")
         st.rerun()
+
+
+def setup_input_selection():
+    """设置输入方式选择"""
+    st.subheader("选择输入方式")
+    input_method = st.radio(
+        "请选择输入方式",
+        ["从数据集选择", "手动输入文本"],
+        key="input_method"
+    )
+    
+    if input_method == "手动输入文本":
+        user_text = st.text_area(
+            "请输入放射学报告文本",
+            height=200,
+            placeholder="在此输入报告文本...",
+            key="user_input_text"
+        )
+        if st.button("分析文本"):
+            return {"type": "user_input", "text": user_text}
+    else:
+        return {"type": "dataset"}
+    
+    return None

ie.py

@@ -0,0 +1,189 @@
+"""
+医学放射学报告命名实体识别(NER)评估脚本
+
+该脚本用于从放射学报告中提取临床术语实体，包括解剖部位(ANAT)和观察结果(OBS)。
+主要功能:
+1. 使用LLM模型进行实体抽取
+2. 支持few-shot学习，可以从训练数据中加载示例
+3. 包含错误重试机制
+4. 结果输出格式化处理
+
+实体标签说明:
+- OBS-DP: 明确存在的观察结果
+- ANAT-DP: 明确存在的解剖部位
+- OBS-U: 不确定的观察结果
+- OBS-DA: 明确不存在的观察结果
+
+输出格式：
+{
+    "1": {"tokens": "...", "label": "..."},
+    "2": {"tokens": "...", "label": "..."},
+    ...
+}
+"""
+
+import json
+import requests
+import pprint
+import time  # 添加time模块用于重试机制
+from typing import Dict, List, Any  # 添加类型提示支持
+
+
+# Prepare prompt with instructions
+system_prompt = '''You are a radiologist performing clinical term extraction from the FINDINGS and IMPRESSION sections 
+    in the radiology report. Here a clinical term can be either anatomy or observation that is related to a finding or an impression. The anatomy term refers to an anatomical body part such as a 'lung'. The observation terms refer to observations made when referring to the associated radiology image. Observations are associated with visual features, identifiable pathophysiologic processes, or diagnostic disease classifications. For example, an observation could be 'effusion' or description phrases like 'increased'. You also need to assign a label to indicate whether the clinical term is present, absent or uncertain. The labels are:
+    - OBS-DP: Observation definitely present
+    - ANAT-DP: Anatomy definitely present  
+    - OBS-U: Observation uncertain
+    - OBS-DA: Observation definitely absent
+
+    Given a piece of radiology text input in the format:
+
+    <INPUT>
+    <text>
+    </INPUT>
+
+    reply with the following structure:
+
+    <OUTPUT>
+    ANSWER: tuples separated by newlines. Each tuple has the format: (<clinical term text>, <label: observation-present |observation-absent|observation-uncertain|anatomy-present>). If there are no extraction related to findings or impression, return ()
+    </OUTPUT>
+    '''
+
+# Create a session object for reuse
+session = requests.Session()
+
+
+def get_question_prompt(text):
+    return f'''<INPUT>
+        {text}
+        </INPUT> 
+
+        What are the clinical terms and their labels in this text? Discard sections other than FINDINGS and IMPRESSION: eg. INDICATION, HISTORY, TECHNIQUE, COMPARISON sections. If there is no extraction from findings and impression, return (). Please only output the tuples without additional notes or explanations.
+
+        <OUTPUT> ANSWER:
+        '''
+
+
+def extract_entities(text, num_shots=5):
+    try:
+        url = 'http://ollama.corinth.informatik.rwth-aachen.de/api/chat'
+
+        messages = [{'role': 'system', 'content': system_prompt}]
+        messages = create_messages_with_shots(num_shots, messages)
+        messages.append({'role': 'user', 'content': get_question_prompt(text)})
+
+        data = {
+            "model": "llama3.1:8b",
+            "messages": messages,
+            "stream": False
+        }
+
+        response = session.post(url, json=data)
+        response.raise_for_status()  # 检查HTTP错误
+        entities = response.json()['message']['content']
+
+        # Parse response into JSON format
+        result = {}
+        lines = entities.split('\n')
+        for i, line in enumerate(lines):
+            if '(' in line and ')' in line:
+                try:
+                    term, label = eval(line.strip())
+                    result[str(i+1)] = {
+                        "tokens": term,
+                        "label": label
+                    }
+                except (ValueError, SyntaxError):
+                    continue
+
+        return result
+
+    except Exception as e:
+        raise e
+
+
+def create_messages_with_shots(num_shots, messages):
+    """
+    从train.json创建few-shot示例消息列表
+
+    Args:
+        num_shots (int): 需要的few-shot示例数量
+        messages (list): 现有的消息列表
+
+    Returns:
+        list: 包含few-shot示例的消息列表
+    """
+    # 加载训练数据
+    with open('train.json', 'r') as f:
+        train_data = json.load(f)
+
+    # 获取前num_shots个示例
+    shot_count = 0
+    for key, value in train_data.items():
+        if shot_count >= num_shots:
+            break
+
+        text = value['text']
+        entities = value['entities']
+
+        # 构建答案字符串
+        answer_parts = []
+        for entity_id, entity_info in entities.items():
+            tokens = entity_info['tokens']
+            label = entity_info['label']
+            answer_parts.append(f"('{tokens}', '{label}')")
+
+        answer = '\n'.join(answer_parts)
+        answer += '\n</OUTPUT>'
+
+        # 添加用户问题
+        messages.append({
+            'role': 'user',
+            'content': get_question_prompt(text)
+        })
+
+        # 添加助手回答
+        messages.append({
+            'role': 'assistant',
+            'content': answer
+        })
+
+        shot_count += 1
+
+    return messages
+
+
+def main():
+    # Load test data
+    with open('dev.json', 'r') as f:
+        data = json.load(f)
+
+    # Get first example
+    for key, value in data.items():
+        text = value['text']
+        # 移除未使用的shots_path参数，添加num_shots参数
+        entities = extract_entities(text, num_shots=50)
+        break
+
+    print(entities)
+
+
+if __name__ == '__main__':
+    main()
+
+
+def process_sample(text: str, num_shots: int) -> list:
+    """处理单个样本，包含重试机制"""
+    max_retries = 3
+
+    for attempt in range(max_retries):
+        try:
+            entities = extract_entities(text, num_shots)
+            return entities
+        except Exception as e:
+            if attempt < max_retries - 1:
+                print(f'第{attempt + 1}次尝试失败: {e}')
+                continue
+            else:
+                raise e