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import streamlit as st |
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from PIL import Image |
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import base64 |
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import time |
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import streamlit.components.v1 as components |
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import json |
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from typing import Dict, List, Optional |
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from pathlib import Path |
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from streamlit_autorefresh import st_autorefresh |
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import pandas as pd |
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import re |
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import streamlit_image_select as sis |
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from fpdf import FPDF |
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import io |
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import markdown |
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import os |
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from openai import OpenAI |
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from http import HTTPStatus |
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import dashscope |
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PAGE_CONFIG = { |
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"page_title": "Knee OA Demo", |
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"layout": "wide" |
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} |
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CHAT_CONFIG = { |
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"update_interval": 2, |
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"height": 640, |
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"max_width": "80%" |
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} |
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IMAGE_PATHS = { |
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"logo": "images/logo.png", |
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"framework": "images/framework.png", |
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"status_framework": "images/status_framework.png", |
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"predicting_framework": "images/predicting_framework.png", |
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"recommendation_framework": "images/Recommendation_framework.png" |
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} |
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CASES_FILE = "cases.json" |
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PARAMS_FILE = "predict_params.json" |
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@st.cache_data |
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def get_base64_image(image_path: str) -> Optional[str]: |
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"""安全获取图片base64编码""" |
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try: |
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if Path(image_path).exists(): |
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with open(image_path, "rb") as f: |
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data = f.read() |
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return base64.b64encode(data).decode() |
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except Exception as e: |
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st.error(f"无法加载图片 {image_path}: {e}") |
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return None |
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@st.cache_data |
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def load_initial_chat_history(file_path: str = "initial_chat.json") -> List[Dict]: |
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"""从 JSON 文件加载初始聊天历史""" |
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try: |
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if Path(file_path).exists(): |
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with open(file_path, "r", encoding="utf-8") as f: |
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return json.load(f) |
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else: |
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st.warning(f"找不到初始对话文件：{file_path}") |
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except Exception as e: |
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st.error(f"加载初始聊天对话失败: {e}") |
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return [] |
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@st.cache_data |
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def load_analysis_report(json_file="assess_result.json") -> Dict: |
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try: |
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with open(json_file, "r", encoding="utf-8") as f: |
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return json.load(f) |
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except Exception as e: |
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st.error(f"Unable to load the analysis report file: {e}") |
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return {} |
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@st.cache_data |
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def load_case_data() -> Dict: |
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"""加载病例数据""" |
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try: |
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if Path(CASES_FILE).exists(): |
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with open(CASES_FILE, "r", encoding="utf-8") as f: |
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return json.load(f) |
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except Exception as e: |
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st.error(f"无法加载病例数据: {e}") |
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return {} |
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def load_plan(agent_type: str): |
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path = f"{agent_type}_plan.json" |
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with open(path, "r", encoding="utf-8") as f: |
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return json.load(f) |
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def clean_text_for_pdf(text: str) -> str: |
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replacements = { |
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"–": "-", |
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"—": "-", |
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"“": "\"", |
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"”": "\"", |
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"’": "'", |
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"•": "-", |
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"→": "->", |
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"…": "...", |
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"©": "(c)", |
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} |
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for old, new in replacements.items(): |
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text = text.replace(old, new) |
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return text.encode("latin-1", "ignore").decode("latin-1") |
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def strip_non_latin1(text: str) -> str: |
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"""去除无法被 Latin-1 编码的字符（如 emoji、中文）""" |
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return text.encode("latin-1", errors="ignore").decode("latin-1") |
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def generate_pdf(text: str) -> bytes: |
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pdf = FPDF() |
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pdf.add_page() |
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pdf.set_font("Arial", size=12) |
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for line in text.split("\n"): |
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clean_line = strip_non_latin1(line) |
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pdf.multi_cell(0, 10, txt=clean_line) |
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return pdf.output(dest="S").encode("latin1") |
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def generate_report_text_from_prediction(params: dict) -> str: |
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lines = ["Prediction Report\n", "=" * 30 + "\n"] |
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for k, v in params.items(): |
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lines.append(f"{k}: {json.dumps(v, ensure_ascii=False) if isinstance(v, (dict, list)) else v}") |
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return "\n".join(lines) |
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def safe_image_display(image_path: str, caption: str = "", **kwargs): |
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"""显示图片""" |
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try: |
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if Path(image_path).exists(): |
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st.image(image_path, caption=caption, **kwargs) |
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else: |
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st.warning(f"⚠️ 图片未找到: {image_path}") |
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except Exception as e: |
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st.error(f"显示图片时出错: {e}") |
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def generate_report_text_from_prediction(params: dict) -> str: |
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lines = [] |
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lines.append("📊 Symptom Trajectory Forecast (KOOS, 0–100)") |
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symptom_rows = [ |
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("Right Knee Pain", "symptom_trajectory.right_knee.pain"), |
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("Right Knee Symptoms", "symptom_trajectory.right_knee.symptoms"), |
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("Sport/Recreation Function", "symptom_trajectory.right_knee.sport_recreation_function"), |
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("Quality of Life", "symptom_trajectory.right_knee.quality_of_life"), |
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("Left Knee Pain", "symptom_trajectory.left_knee.pain") |
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] |
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for label, base_key in symptom_rows: |
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v00 = params.get(f"{base_key}.v00", "N/A") |
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v01 = params.get(f"{base_key}.v01", "N/A") |
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v04 = params.get(f"{base_key}.v04", "N/A") |
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lines.append(f"- {label}: Current={v00}, Year 2={v01}, Year 4={v04}") |
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lines.append("") |
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lines.append("🦴 Imaging Trajectory (KL grade, 0–4)") |
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for side in ["right", "left"]: |
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v00 = params.get(f"imaging_trajectory.{side}_knee.pain.v00", "N/A") |
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v01 = params.get(f"imaging_trajectory.{side}_knee.pain.v01", "N/A") |
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v04 = params.get(f"imaging_trajectory.{side}_knee.pain.v04", "N/A") |
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lines.append(f"- {side.capitalize()} Knee: Current={v00}, Year 2={v01}, Year 4={v04}") |
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lines.append("") |
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lines.append("💡 Key Contributing Factors (SHAP)") |
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shap_data = params.get("key_factors.right_knee_symptoms_year2", []) |
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for item in shap_data: |
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feature = item.get("feature", "Unknown") |
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impact = item.get("impact", "N/A") |
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effect = item.get("effect", "") |
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lines.append(f"- {feature}: {impact} ({effect})") |
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return "\n".join(lines) |
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def get_navigation_styles(logo_base64: str) -> str: |
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"""获取导航栏样式""" |
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return f""" |
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<style> |
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.nav-container {{ |
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display: flex; |
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justify-content: space-between; |
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align-items: center; |
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border-bottom: 1px solid #ddd; |
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flex-wrap: wrap; |
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margin-bottom: 0; |
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}} |
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.left-section {{ |
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display: flex; |
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align-items: center; |
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gap: 20px; |
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flex: 1; |
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min-width: 300px; |
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}} |
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.app-title {{ |
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color: #4B6EAF; |
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font-family: "Segoe UI", sans-serif; |
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}} |
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.app-title div:first-child {{ |
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font-size: 20px; |
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font-weight: bold; |
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}} |
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.app-title div:last-child {{ |
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font-size: 18px; |
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}} |
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.logo-img {{ |
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height: 70px; |
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}} |
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.nav-buttons {{ |
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display: flex; |
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align-items: center; |
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gap: 12px; |
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flex-wrap: wrap; |
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justify-content: flex-end; |
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}} |
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.nav-buttons form {{ margin: 0; }} |
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.agent-button {{ |
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font-size: 16px; |
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padding: 10px 16px; |
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display: inline-flex; |
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align-items: center; |
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background-color: #f0f0f0; |
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border: 2px solid #ccc; |
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border-radius: 6px; |
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cursor: pointer; |
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transition: background-color 0.2s ease; |
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box-shadow: 1px 1px 5px rgba(0,0,0,0.1); |
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}} |
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.agent-button:hover {{ background-color: #e0e0e0; }} |
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.agent-button img {{ |
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width: 24px; |
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height: 24px; |
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margin-right: 8px; |
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}} |
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.arrow-icon {{ |
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font-size: 20px; |
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color: #888; |
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}} |
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</style> |
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<div class="nav-container"> |
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<div class="left-section"> |
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<img src="data:image/png;base64,{logo_base64}" class="logo-img" /> |
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<div class="app-title"> |
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<div>Knee Osteoarthritis Management Platform</div> |
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<div>膝关节骨关节炎人工智能平台</div> |
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</div> |
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</div> |
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<div class="nav-buttons"> |
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<form action="/" method="get" title="Home Page"> |
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<input type="hidden" name="page" value="Home"> |
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<button type="submit" class="agent-button" style="font-weight: 600;">Home</button> |
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</form> |
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<form action="/" method="get" title="Assess status"> |
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<input type="hidden" name="page" value="Assessing Current Status"> |
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<button type="submit" class="agent-button"> |
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<img src="https://www.svgrepo.com/download/285252/robot.svg" alt="robot icon"> |
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Assess Agent |
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</button> |
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</form> |
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<div class="arrow-icon">➡️</div> |
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<form action="/" method="get" title="Predict risk"> |
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<input type="hidden" name="page" value="Predicting Progression Risk"> |
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<button type="submit" class="agent-button"> |
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<img src="https://www.svgrepo.com/download/285252/robot.svg" alt="robot icon"> |
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Predict Agent |
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</button> |
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</form> |
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<div class="arrow-icon">➡️</div> |
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<form action="/" method="get" title="Recommend therapy"> |
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<input type="hidden" name="page" value="Tailored Therapy Recommendation"> |
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<button type="submit" class="agent-button"> |
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<img src="https://www.svgrepo.com/download/285252/robot.svg" alt="robot icon"> |
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Therapy Agent |
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</button> |
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</form> |
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</div> |
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</div> |
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""" |
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def get_chat_styles() -> str: |
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"""返回优化后的聊天气泡样式""" |
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return """ |
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<style> |
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.chat-bubble { |
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border-radius: 10px; |
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padding: 14px 18px; |
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margin-bottom: 20px; |
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font-size: 15px; |
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line-height: 1.6; |
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box-shadow: 0 2px 6px rgba(0, 0, 0, 0.04); |
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border-left: 5px solid transparent; |
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background-color: #f9f9f9; |
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} |
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.chat-content { |
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white-space: normal !important; |
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word-break: break-word; |
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overflow-wrap: break-word; |
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line-height: 1.6; |
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} |
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.exercise { |
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background-color: #f0fff4; |
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border-left-color: #34c759; |
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} |
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.pharma { |
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background-color: #f0f8ff; |
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border-left-color: #1e90ff; |
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} |
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.nutrition { |
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background-color: #fffaf0; |
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border-left-color: #f4b400; |
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} |
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.summary { |
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background-color: #fff0f0; |
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border-left-color: #ff6b6b; |
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} |
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.chat-icon { |
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font-weight: bold; |
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margin-bottom: 6px; |
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display: block; |
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color: #333; |
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} |
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.chat-bubble strong { |
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display: inline-block; |
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margin-bottom: 4px; |
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color: #222; |
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} |
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</style> |
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""" |
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class ChatManager: |
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def __init__(self, initial_chat_file: str = "assess_chat.json"): |
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self.initial_history = load_initial_chat_history(initial_chat_file) |
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def initialize_state(self): |
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if "chat_history" not in st.session_state: |
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st.session_state.chat_history = [] |
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for msg in self.initial_history: |
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if msg["role"] == "user": |
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st.session_state.chat_history.append(msg) |
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else: |
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st.session_state.chat_history.append({"role": "assistant", "content": None}) |
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st.session_state.chat_step = 1 |
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st.session_state.last_update_time = time.time() |
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def update_progress(self): |
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now = time.time() |
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if now - st.session_state.last_update_time < CHAT_CONFIG["update_interval"]: |
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return |
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step = st.session_state.chat_step |
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initial = self.initial_history |
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history = st.session_state.chat_history |
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if step >= len(initial): |
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return |
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next_msg = initial[step] |
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if next_msg["role"] == "user": |
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if len(history) <= step or history[step]["content"] != next_msg["content"]: |
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if len(history) > step: |
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history[step] = next_msg |
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else: |
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history.append(next_msg) |
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st.session_state.chat_step += 1 |
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st.session_state.last_update_time = now |
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elif next_msg["role"] == "assistant": |
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if step == 0 or history[step - 1]["role"] != "user" or not history[step - 1]["content"]: |
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st.warning("⚠️ 无法生成 AI 回答：找不到上一条用户消息") |
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return |
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messages = history[:step] |
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messages.append(history[step - 1]) |
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app_id = st.secrets["DASHSCOPE_AGENT_ID"] |
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api_key = st.secrets["DASHSCOPE_API_KEY"] |
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reply = self.generate_response(messages, app_id, api_key) |
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if not reply: |
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st.warning("⚠️ AI 未返回内容，请稍后重试") |
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return |
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if len(history) > step: |
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history[step]["content"] = reply |
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else: |
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history.append({"role": "assistant", "content": reply}) |
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st.session_state.chat_step += 1 |
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st.session_state.last_update_time = now |
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def render_message(self, role: str, content: str) -> str: |
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"""渲染单条消息（AI左侧，用户右侧）""" |
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if role == "user": |
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return f""" |
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<div style="display: flex; justify-content: flex-end; margin: 5px 0;"> |
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<div style="background-color: #DCF8C6; color: black; |
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padding: 8px 12px; border-radius: 12px; max-width: {CHAT_CONFIG['max_width']}; |
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text-align: left;"> |
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🧍 {content} |
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</div> |
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</div> |
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""" |
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else: |
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return f""" |
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<div style="display: flex; justify-content: flex-start; margin: 5px 0;"> |
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<div style="background-color: #F1F0F0; color: black; |
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padding: 8px 12px; border-radius: 12px; max-width: {CHAT_CONFIG['max_width']}; |
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text-align: left;"> |
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👨⚕️ {content} |
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</div> |
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</div> |
|
|
""" |
|
|
|
|
|
def render_chat_interface(self): |
|
|
"""自动逐条渲染聊天界面""" |
|
|
chat_html = "".join([ |
|
|
self.render_message(msg["role"], msg["content"]) |
|
|
for msg in st.session_state.chat_history[:st.session_state.chat_step] |
|
|
]) |
|
|
|
|
|
height = CHAT_CONFIG["height"] |
|
|
components.html(f""" |
|
|
<div style="height: {height}px; overflow-y: auto; padding: 10px 10px 40px 10px; border: 1px solid #ccc; |
|
|
border-radius: 8px; background-color: white;box-sizing: border-box;" id="chat-box"> |
|
|
{chat_html} |
|
|
<div id="bottom"></div> |
|
|
</div> |
|
|
<script> |
|
|
const chatBox = document.getElementById("chat-box"); |
|
|
chatBox.scrollTop = chatBox.scrollHeight; |
|
|
setTimeout(() => {{ |
|
|
window.parent.postMessage({{ isStreamlitMessage: true, type: 'streamlit:rerun' }}, '*'); |
|
|
}}, {int(CHAT_CONFIG['update_interval'] * 1000)}); |
|
|
</script> |
|
|
""", height=height) |
|
|
|
|
|
def handle_user_input(self): |
|
|
user_input = st.chat_input("Please enter your symptoms, medical history or problems...") |
|
|
if user_input: |
|
|
st.session_state.chat_history.append({"role": "user", "content": user_input}) |
|
|
api_key = st.secrets["DASHSCOPE_API_KEY"] |
|
|
app_id = st.secrets["DASHSCOPE_AGENT_ID"] |
|
|
|
|
|
|
|
|
messages = [{"role": "user", "content": user_input}] |
|
|
response = self.generate_response(messages, app_id, api_key) |
|
|
|
|
|
st.session_state.chat_history.append({"role": "assistant", "content": response}) |
|
|
|
|
|
st.session_state.chat_step = len(st.session_state.chat_history) |
|
|
st.rerun() |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def generate_response(self, messages: list, app_id: str, api_key: str) -> str: |
|
|
api_key = st.secrets["DASHSCOPE_API_KEY"] |
|
|
app_id = st.secrets["DASHSCOPE_AGENT_ID"] |
|
|
dashscope.api_key = api_key |
|
|
dashscope.base_http_api_url = 'https://dashscope.aliyuncs.com/api/v1' |
|
|
|
|
|
|
|
|
prompt = "\n".join([ |
|
|
f"{msg['role'].capitalize()}: {msg['content']}" for msg in messages if msg["content"] |
|
|
]) |
|
|
|
|
|
try: |
|
|
response = dashscope.Application.call( |
|
|
app_id=app_id, |
|
|
prompt=prompt |
|
|
) |
|
|
except Exception as e: |
|
|
return f"⚠️ 智能体调用异常: {e}" |
|
|
|
|
|
if response.status_code != HTTPStatus.OK: |
|
|
return f"⚠️ 智能体调用失败: {response.message}" |
|
|
|
|
|
return response.output.get("text", "⚠️ 没有返回内容") |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def render_navigation(): |
|
|
"""渲染导航栏""" |
|
|
logo_base64 = get_base64_image(IMAGE_PATHS["logo"]) |
|
|
if logo_base64: |
|
|
st.markdown(get_navigation_styles(logo_base64), unsafe_allow_html=True) |
|
|
else: |
|
|
st.title("Knee Osteoarthritis Management Platform") |
|
|
|
|
|
|
|
|
def inject_agent_styles(): |
|
|
"""注入各智能体颜色样式""" |
|
|
st.markdown(""" |
|
|
<style> |
|
|
.chat-bubble { |
|
|
border-radius: 12px; |
|
|
padding: 16px; |
|
|
margin: 16px 0; |
|
|
box-shadow: 0 2px 6px rgba(0,0,0,0.1); |
|
|
} |
|
|
.chat-bubble.exercise { |
|
|
background-color: #e6f4ea; |
|
|
border-left: 6px solid #34a853; |
|
|
} |
|
|
.chat-bubble.surgical, .chat-bubble.pharma { |
|
|
background-color: #e8f0fe; |
|
|
border-left: 6px solid #4285f4; |
|
|
} |
|
|
.chat-bubble.nutrition, .chat-bubble.psychology { |
|
|
background-color: #fff8e1; |
|
|
border-left: 6px solid #fbbc04; |
|
|
} |
|
|
.chat-icon { |
|
|
font-weight: bold; |
|
|
margin-bottom: 8px; |
|
|
} |
|
|
.chat-bubble.decision { |
|
|
background-color: #f1f3f4; |
|
|
border-left: 6px solid #5f6368; |
|
|
} |
|
|
</style> |
|
|
""", unsafe_allow_html=True) |
|
|
|
|
|
|
|
|
def render_home_page(): |
|
|
"""渲染首页""" |
|
|
abstract_col, figure_col = st.columns([0.9, 1.1]) |
|
|
|
|
|
with abstract_col: |
|
|
st.markdown('<h4 style="font-size:22px;">About</h4>', unsafe_allow_html=True) |
|
|
st.markdown(""" |
|
|
平台使用流程等 |
|
|
""") |
|
|
|
|
|
with figure_col: |
|
|
st.markdown('<h4 style="font-size:22px;">General Framework</h4>', unsafe_allow_html=True) |
|
|
safe_image_display(IMAGE_PATHS["framework"], "Framework Overview", use_container_width=True) |
|
|
|
|
|
st.markdown("---") |
|
|
st.markdown( |
|
|
"⚠️This website is at an early stage of development and intended for research purposes only. For collaboration or to report bugs, please contact us at ……. Thank you! 本网页仅用于研究用途") |
|
|
|
|
|
|
|
|
|
|
|
def generate_report_text_from_json(json_path: str = "structured_report_template.json") -> str: |
|
|
with open(json_path, "r", encoding="utf-8") as f: |
|
|
report_dict = json.load(f) |
|
|
|
|
|
lines = [] |
|
|
for section, contents in report_dict.items(): |
|
|
lines.append(section) |
|
|
for line in contents: |
|
|
lines.append(line) |
|
|
lines.append("") |
|
|
return "\n".join(lines) |
|
|
|
|
|
|
|
|
def render_centered_image_full(image_path, width=300): |
|
|
import base64 |
|
|
with open(image_path, "rb") as img_file: |
|
|
img_bytes = img_file.read() |
|
|
encoded = base64.b64encode(img_bytes).decode("utf-8") |
|
|
|
|
|
html = f''' |
|
|
<div style="width: 100%; text-align: center; margin-top: 20px;"> |
|
|
<img src="data:image/png;base64,{encoded}" width="{width}" /> |
|
|
</div> |
|
|
''' |
|
|
st.markdown(html, unsafe_allow_html=True) |
|
|
|
|
|
|
|
|
def spacer(height_px=24): |
|
|
st.markdown(f"<div style='height: {height_px}px;'></div>", unsafe_allow_html=True) |
|
|
|
|
|
|
|
|
def render_assessment_page(): |
|
|
"""渲染评估页面""" |
|
|
st.markdown(""" |
|
|
<style> |
|
|
/* 按钮字体 */ |
|
|
.stButton > button, |
|
|
.stDownloadButton > button { |
|
|
font-size: 16px !important; |
|
|
} |
|
|
/* Expander 标题 */ |
|
|
.st-expanderHeader { |
|
|
font-size: 16px !important; |
|
|
} |
|
|
/* 下拉框输入框和选中项 */ |
|
|
div[data-baseweb="select"] > div > div > input, |
|
|
div[data-baseweb="select"] > div > div > div, |
|
|
div[data-baseweb="select"] ul > li { |
|
|
font-size: 16px !important; |
|
|
} |
|
|
/* 标签字体 */ |
|
|
label, .stTextInput label, .stSelectbox label, .stMultiSelect label { |
|
|
font-size: 16px !important; |
|
|
} |
|
|
</style> |
|
|
""", unsafe_allow_html=True) |
|
|
|
|
|
chat_manager = ChatManager() |
|
|
chat_manager.initialize_state() |
|
|
chat_manager.update_progress() |
|
|
|
|
|
|
|
|
if st.session_state.chat_step < len(st.session_state.chat_history): |
|
|
st_autorefresh(interval=1500, key="chat_autorefresh") |
|
|
|
|
|
for key, default in { |
|
|
"show_sidebar": False, |
|
|
"selected_image_path": None, |
|
|
"selected_image_label": None, |
|
|
"typing_index": 0, |
|
|
"chat_history": None, |
|
|
"chat_step": 1, |
|
|
"last_update_time": time.time(), |
|
|
}.items(): |
|
|
if key not in st.session_state: |
|
|
st.session_state[key] = default |
|
|
|
|
|
if st.session_state.show_sidebar: |
|
|
with st.sidebar: |
|
|
st.markdown("### 📂 Select a sample knee image") |
|
|
PREDEFINED_IMAGES = { |
|
|
"Knee Image A": "images/knee_sample_1.png", |
|
|
} |
|
|
for idx, (label, path) in enumerate(PREDEFINED_IMAGES.items()): |
|
|
col1, col2, col3 = st.columns([1, 2, 1]) |
|
|
with col2: |
|
|
st.image(path, width=150, caption=label) |
|
|
if st.button("✅ Select", key=f"select_{idx}"): |
|
|
st.session_state.selected_image_path = path |
|
|
st.session_state.selected_image_label = label |
|
|
st.session_state.show_sidebar = False |
|
|
st.rerun() |
|
|
|
|
|
col1, col2 = st.columns([1.2, 0.8]) |
|
|
|
|
|
with col1: |
|
|
chat_manager.render_chat_interface() |
|
|
chat_manager.update_progress() |
|
|
chat_manager.handle_user_input() |
|
|
|
|
|
st.divider() |
|
|
|
|
|
if st.button("📷 Upload Image"): |
|
|
st.session_state.show_sidebar = True |
|
|
|
|
|
if st.session_state.selected_image_path and st.session_state.selected_image_label: |
|
|
st.success(f"✅ Selected: {st.session_state.selected_image_label}") |
|
|
|
|
|
render_centered_image_full(st.session_state.selected_image_path, width=450) |
|
|
|
|
|
spacer(24) |
|
|
|
|
|
analysis_data = load_analysis_report() |
|
|
report = analysis_data.get("default") |
|
|
|
|
|
if report: |
|
|
with st.expander("📝 View Structured Analysis Report"): |
|
|
for knee, sections in report.items(): |
|
|
st.markdown(f"### 🦵 {knee}") |
|
|
for section_title, items in sections.items(): |
|
|
st.markdown(f"**{section_title}**") |
|
|
for item in items: |
|
|
st.markdown(f"- {item}") |
|
|
report_text = generate_report_text_from_json() |
|
|
report_text = clean_text_for_pdf(report_text) |
|
|
pdf_bytes = generate_pdf(report_text) |
|
|
|
|
|
with open("custom_patient_report.json", "r", encoding="utf-8") as f: |
|
|
custom_json_data = json.load(f) |
|
|
json_bytes = io.BytesIO(json.dumps(custom_json_data, indent=2).encode('utf-8')) |
|
|
|
|
|
|
|
|
|
|
|
st.download_button( |
|
|
label="📄 Download Structured Analysis Report as PDF", |
|
|
data=pdf_bytes, |
|
|
file_name="knee_report.pdf", |
|
|
mime="application/pdf" |
|
|
) |
|
|
|
|
|
|
|
|
st.download_button( |
|
|
label="📄 Download the JSON file", |
|
|
data=json_bytes, |
|
|
file_name="knee_report.json", |
|
|
mime="application/json" |
|
|
) |
|
|
else: |
|
|
st.warning("⚠️ The structured analysis report failed to load") |
|
|
|
|
|
with col2: |
|
|
safe_image_display(IMAGE_PATHS["status_framework"], "Status framework", use_container_width=True) |
|
|
|
|
|
|
|
|
def render_chat(role, message=None, table_df=None): |
|
|
avatar = { |
|
|
"User": "🧑", |
|
|
"AI": "🩺" |
|
|
}.get(role, "💬") |
|
|
|
|
|
bg_color = { |
|
|
"User": "#f1f8e9", |
|
|
"AI": "#F2F2F2" |
|
|
}.get(role, "#eeeeee") |
|
|
|
|
|
content_html = "" |
|
|
if message: |
|
|
content_html += f"<div style='margin-bottom:10px;'>{message}</div>" |
|
|
if table_df is not None: |
|
|
table_html = table_df.to_html(index=False) |
|
|
table_html = f""" |
|
|
<style> |
|
|
table {{ width: 100%; border-collapse: collapse; }} |
|
|
th {{ text-align: center !important; }} |
|
|
td {{ text-align: center; }} |
|
|
</style> |
|
|
{table_html} |
|
|
""" |
|
|
content_html += f"<div style='overflow-x:auto'>{table_html}</div>" |
|
|
|
|
|
st.markdown(f""" |
|
|
<div style="background-color:{bg_color}; padding:14px 18px; border-radius:10px; margin-bottom:18px; display:flex;"> |
|
|
<div style="font-size:22px; margin-right:12px;">{avatar}</div> |
|
|
<div style="font-size:15px; line-height:1.6; width:100%;">{content_html}</div> |
|
|
</div> |
|
|
""", unsafe_allow_html=True) |
|
|
|
|
|
|
|
|
def render_centered_table(df): |
|
|
html_table = df.to_html(index=False) |
|
|
centered_html = f""" |
|
|
<div style="display: flex; justify-content: center;"> |
|
|
<div style="width: 80%;"> |
|
|
{html_table} |
|
|
</div> |
|
|
</div> |
|
|
""" |
|
|
st.markdown(centered_html, unsafe_allow_html=True) |
|
|
|
|
|
|
|
|
def render_prediction_report(params): |
|
|
st.markdown("<h4>📝 Comprehensive Prediction Report</h4>", unsafe_allow_html=True) |
|
|
|
|
|
render_chat("AI", """ |
|
|
Excellent! |
|
|
I’ve received your case report—thanks for submitting it! |
|
|
With this complete dataset, I can now provide you with a comprehensive forecast of how your knee condition may evolve over time. Here’s what the model predicts: |
|
|
""") |
|
|
|
|
|
st.markdown("<h5>📊 Symptom Trajectory Forecast (KOOS, 0–100)</h5>", unsafe_allow_html=True) |
|
|
symptom_table = { |
|
|
"Metric": ["Right Knee Pain", "Right Knee Symptoms", "Sport/Recreation Function", "Quality of Life", |
|
|
"Left Knee Pain"], |
|
|
"Current (V00)": [ |
|
|
params["symptom_trajectory.right_knee.pain.v00"], |
|
|
params["symptom_trajectory.right_knee.symptoms.v00"], |
|
|
params["symptom_trajectory.right_knee.sport_recreation_function.v00"], |
|
|
params["symptom_trajectory.right_knee.quality_of_life.v00"], |
|
|
params["symptom_trajectory.left_knee.pain.v00"] |
|
|
], |
|
|
"Year 2 (V01)": [ |
|
|
params["symptom_trajectory.right_knee.pain.v01"], |
|
|
params["symptom_trajectory.right_knee.symptoms.v01"], |
|
|
params["symptom_trajectory.right_knee.sport_recreation_function.v01"], |
|
|
params["symptom_trajectory.right_knee.quality_of_life.v01"], |
|
|
params["symptom_trajectory.left_knee.pain.v01"] |
|
|
], |
|
|
"Year 4 (V04)": [ |
|
|
params["symptom_trajectory.right_knee.pain.v04"], |
|
|
params["symptom_trajectory.right_knee.symptoms.v04"], |
|
|
params["symptom_trajectory.right_knee.sport_recreation_function.v04"], |
|
|
params["symptom_trajectory.right_knee.quality_of_life.v04"], |
|
|
params["symptom_trajectory.left_knee.pain.v04"] |
|
|
] |
|
|
} |
|
|
render_chat("AI", "Here is the forecast of your knee-related symptoms over the coming years:", |
|
|
pd.DataFrame(symptom_table)) |
|
|
|
|
|
|
|
|
st.markdown("<h5>🦴 Imaging Trajectory (KL grade, 0–4)</h5>", unsafe_allow_html=True) |
|
|
imaging_table = { |
|
|
"Knee": ["Right", "Left"], |
|
|
"Current": [ |
|
|
params["imaging_trajectory.right_knee.pain.v00"], |
|
|
params["imaging_trajectory.left_knee.pain.v00"] |
|
|
], |
|
|
"Year 2": [ |
|
|
params["imaging_trajectory.right_knee.pain.v01"], |
|
|
params["imaging_trajectory.left_knee.pain.v01"] |
|
|
], |
|
|
"Year 4": [ |
|
|
params["imaging_trajectory.right_knee.pain.v04"], |
|
|
params["imaging_trajectory.left_knee.pain.v04"] |
|
|
] |
|
|
} |
|
|
render_chat("AI", "Here’s how your knee structure may change over time, based on imaging predictions:", |
|
|
pd.DataFrame(imaging_table)) |
|
|
|
|
|
|
|
|
st.markdown("<h5>💡 Key Contributing Factors (SHAP)</h5>", unsafe_allow_html=True) |
|
|
shap_data = params["key_factors.right_knee_symptoms_year2"] |
|
|
shap_table = { |
|
|
"Feature": [item["feature"] for item in shap_data], |
|
|
"Impact on KOOS Symptoms": [f"{item['impact']} ({item['effect']})" for item in shap_data] |
|
|
} |
|
|
render_chat("AI", "These are the most impactful factors influencing your right knee symptoms at Year 2:", |
|
|
pd.DataFrame(shap_table)) |
|
|
|
|
|
|
|
|
def load_default_params(): |
|
|
with open(PARAMS_FILE, "r") as f: |
|
|
return json.load(f) |
|
|
|
|
|
|
|
|
def render_prediction_page(): |
|
|
"""渲染预测页面""" |
|
|
|
|
|
col1, col2 = st.columns([1.2, 0.8]) |
|
|
|
|
|
with col1: |
|
|
params = load_default_params() |
|
|
|
|
|
|
|
|
param_display_list = [] |
|
|
display_to_key = {} |
|
|
for k, v in params.items(): |
|
|
if isinstance(v, (int, float, str)): |
|
|
label = f"{k} ({v})" |
|
|
else: |
|
|
label = f"{k} (complex)" |
|
|
param_display_list.append(label) |
|
|
display_to_key[label] = k |
|
|
|
|
|
st.markdown("**Parameter Mode: `fixed parameter from Assess Agent`**") |
|
|
|
|
|
with st.expander("Click to view parameters(Extracted from Assess Agent)"): |
|
|
selected_display = st.radio(" ", param_display_list, index=0) |
|
|
|
|
|
model = display_to_key[selected_display] |
|
|
threshold = params[model] |
|
|
|
|
|
st.markdown("**Selected value:**") |
|
|
if isinstance(threshold, dict): |
|
|
st.json(threshold) |
|
|
elif isinstance(threshold, list): |
|
|
for i, item in enumerate(threshold): |
|
|
st.markdown(f"**Item {i + 1}:**") |
|
|
st.json(item) |
|
|
else: |
|
|
st.write(threshold) |
|
|
|
|
|
|
|
|
if "prediction_done" not in st.session_state: |
|
|
st.session_state["prediction_done"] = False |
|
|
|
|
|
|
|
|
if st.button("Starting prediction", type="primary"): |
|
|
with st.spinner("Analysing"): |
|
|
time.sleep(3) |
|
|
st.success("Prediction completed!") |
|
|
st.session_state["prediction_done"] = True |
|
|
|
|
|
|
|
|
if st.session_state["prediction_done"]: |
|
|
render_prediction_report(params) |
|
|
spacer(16) |
|
|
|
|
|
export_col1, export_col2 = st.columns([1, 1]) |
|
|
|
|
|
with export_col1: |
|
|
pdf_text = generate_report_text_from_prediction(params) |
|
|
pdf_bytes = generate_pdf(pdf_text) |
|
|
|
|
|
st.download_button( |
|
|
label="📄 Download Prediction Report as PDF", |
|
|
data=pdf_bytes, |
|
|
file_name="prediction_report.pdf", |
|
|
mime="application/pdf", |
|
|
use_container_width=True |
|
|
) |
|
|
|
|
|
with export_col2: |
|
|
with open(PARAMS_FILE, "rb") as f: |
|
|
json_bytes = f.read() |
|
|
|
|
|
st.download_button( |
|
|
label="Download Prediction Report JSON", |
|
|
data=json_bytes, |
|
|
file_name="predict_params.json", |
|
|
mime="application/json", |
|
|
use_container_width=True |
|
|
) |
|
|
|
|
|
with col2: |
|
|
safe_image_display(IMAGE_PATHS["predicting_framework"], "Framework for predicting progress risks", |
|
|
use_container_width=True) |
|
|
|
|
|
|
|
|
def render_agent_message_return_html(role: str, action_html: str, style_class: str) -> str: |
|
|
return f""" |
|
|
<div class="chat-bubble {style_class}"> |
|
|
<div class="chat-icon"><strong>{role}</strong></div> |
|
|
<div class="chat-content" style="margin-top: 8px; text-align: left;"> |
|
|
{action_html} |
|
|
</div> |
|
|
</div> |
|
|
""" |
|
|
|
|
|
|
|
|
def render_agent_message(role: str, action: str, style_class: str) -> str: |
|
|
action_html = markdown.markdown(action, extensions=["extra", "nl2br"]) |
|
|
return f""" |
|
|
<div class="chat-bubble {style_class}"> |
|
|
<div class="chat-icon"><strong>{role}</strong></div> |
|
|
<div style="margin-top: 8px; text-align: left;">{action_html}</div> |
|
|
</div> |
|
|
""" |
|
|
|
|
|
|
|
|
def extract_week_number(phase_name: str) -> int: |
|
|
"""从 'Week 1–4'（含 en dash）中提取排序基准数字""" |
|
|
|
|
|
normalized = phase_name.replace("–", "-").replace("—", "-") |
|
|
match = re.search(r"Week (\d+)", normalized) |
|
|
return int(match.group(1)) if match else 0 |
|
|
|
|
|
|
|
|
def render_exercise_plan_return_html(plan: Dict) -> str: |
|
|
sorted_phases = sorted(plan.items(), key=lambda x: extract_week_number(x[0])) |
|
|
html_blocks = [] |
|
|
|
|
|
for i, (phase, content) in enumerate(sorted_phases, start=1): |
|
|
goal = content.get("Goal", "") |
|
|
prescriptions = content.get("Prescription", []) |
|
|
|
|
|
markdown_text = f"<h4>Phase {i}: {phase}</h4>" |
|
|
markdown_text += f"<b>GOAL:</b> {goal}<br><br>" |
|
|
|
|
|
for item in prescriptions: |
|
|
category = item.get("Category", "Training") |
|
|
description = item.get("Description", "") |
|
|
markdown_text += f"<b>{category} Training:</b><br>" |
|
|
for part in description.split(", "): |
|
|
markdown_text += f"- {part.strip()}<br>" |
|
|
markdown_text += "<br>" |
|
|
|
|
|
html = render_agent_message_return_html( |
|
|
role="A. Exercise Prescriptionist Agent", |
|
|
action_html=markdown_text, |
|
|
style_class="exercise" |
|
|
) |
|
|
html_blocks.append(html) |
|
|
|
|
|
return "\n".join(html_blocks) |
|
|
|
|
|
|
|
|
def render_surgical_pharma_plan_return_html(plan_data: Dict) -> List[str]: |
|
|
""" |
|
|
返回 Surgical & Pharmacological Specialist Agent 的多个 HTML 块列表， |
|
|
每个块可被单独传入 st.markdown(..., unsafe_allow_html=True) 渲染。 |
|
|
""" |
|
|
html_blocks = [] |
|
|
|
|
|
|
|
|
guideline_markdown = "#### Clinical Guideline Analysis\n\n### Matched Guidelines Summary\n\n" |
|
|
title_map = { |
|
|
"564": "Severe Functional Limitation", |
|
|
"225": "Moderate Functional Limitation with Mechanical Symptoms", |
|
|
"482": "Younger Patient with Single-Compartment Disease" |
|
|
} |
|
|
|
|
|
for item in plan_data.get("matched_guidelines", []): |
|
|
guideline_text = item.get("guideline", "") |
|
|
match = re.search(r"Scenario (\d+):", guideline_text) |
|
|
gid = match.group(1) if match else "Unknown" |
|
|
title = title_map.get(gid, "Clinical Scenario") |
|
|
|
|
|
guideline_markdown += f"**Guideline {gid}: {title}**\n" |
|
|
|
|
|
def extract_section(text, start_kw, end_kw=None): |
|
|
try: |
|
|
start = text.index(start_kw) |
|
|
end = text.index(end_kw, start) if end_kw else None |
|
|
return text[start + len(start_kw):end].strip() |
|
|
except ValueError: |
|
|
return "" |
|
|
|
|
|
clinical = extract_section(guideline_text, 'The patient reports', 'Demonstrates') or extract_section( |
|
|
guideline_text, 'Experiences', 'has limited') or "" |
|
|
physical = extract_section(guideline_text, 'Demonstrates', 'Shows') or extract_section(guideline_text, |
|
|
'has limited', |
|
|
'shows') or "" |
|
|
radio = extract_section(guideline_text, 'Shows', 'Total') or extract_section(guideline_text, 'exhibits', |
|
|
'Total') or "" |
|
|
|
|
|
guideline_markdown += f"- **Clinical Presentation:** {clinical}\n" |
|
|
guideline_markdown += f"- **Physical Findings:** {physical}\n" |
|
|
guideline_markdown += f"- **Radiographic Features:** {radio}\n" |
|
|
guideline_markdown += f"**Recommendations:**\n" |
|
|
|
|
|
recos = re.findall( |
|
|
r"(Total knee arthroplasty|Unicompartmental knee arthroplasty.*?|Realignment Osteotomy.*?)\s*(Appropriate|May Be Appropriate|Rarely Appropriate)\s*(\d)", |
|
|
guideline_text) |
|
|
for rec in recos: |
|
|
guideline_markdown += f"- {rec[0]}: {rec[1]} ({rec[2]}/9)\n" |
|
|
guideline_markdown += "\n" |
|
|
|
|
|
guideline_html = render_agent_message_return_html( |
|
|
role="B. Surgical & Pharmacological Specialist Agent", |
|
|
action_html=markdown.markdown(guideline_markdown, extensions=["extra", "nl2br"]), |
|
|
style_class="surgical" |
|
|
) |
|
|
html_blocks.append(guideline_html) |
|
|
|
|
|
|
|
|
meds = plan_data.get("medication_plan", []) |
|
|
med_table_md = "#### Pharmacological Management Plan\n\n" |
|
|
med_table_md += "| Medication | Dosage | Administration Schedule | Notes |\n" |
|
|
med_table_md += "|------------|--------|-------------------------|-------|\n" |
|
|
|
|
|
for med in meds: |
|
|
name = med.get("name", "") |
|
|
dosage = med.get("dosage", "") |
|
|
freq = med.get("frequency", "") |
|
|
notes = "" |
|
|
if "Ibuprofen" in name: |
|
|
notes = "Monitor for GI effects; take with food" |
|
|
elif "Acetaminophen" in name: |
|
|
notes = "Not to exceed 3000 mg daily" |
|
|
elif "Corticosteroids" in name: |
|
|
notes = "Consider after failed oral analgesics" |
|
|
med_table_md += f"| {name} | {dosage} | {freq} | {notes} |\n" |
|
|
|
|
|
med_table_md += "\nNote: Medication regimen should be tailored based on patient comorbidities, concomitant medications, and individual response to therapy.\n" |
|
|
|
|
|
med_table_html = markdown.markdown(med_table_md, extensions=["extra", "nl2br"]) |
|
|
wrapped_html = f"<div class='markdown-wrapper'>{med_table_html}</div>" |
|
|
|
|
|
pharma_html = render_agent_message_return_html( |
|
|
role="B. Surgical & Pharmacological Specialist Agent", |
|
|
action_html=wrapped_html, |
|
|
style_class="pharma" |
|
|
) |
|
|
html_blocks.append(pharma_html) |
|
|
|
|
|
return html_blocks |
|
|
|
|
|
|
|
|
def render_nutrition_psychology_plan_return_html(plan_data: Dict) -> List[str]: |
|
|
"""返回 Nutritional & Psychological Specialist Agent 的多个 HTML 气泡块""" |
|
|
html_blocks = [] |
|
|
|
|
|
|
|
|
nutrition = plan_data.get("nutrition", {}) |
|
|
n_goal = nutrition.get("goal", "") |
|
|
n_duration = nutrition.get("duration", "") |
|
|
n_content = nutrition.get("content", []) |
|
|
|
|
|
nutrition_md = "#### Nutritional Intervention Plan\n" |
|
|
nutrition_md += f"**Goal:** {n_goal}\n\n" |
|
|
nutrition_md += f"**Delivery Method:** Personalized one-on-one counseling supplemented with mobile application reminders\n" |
|
|
nutrition_md += f"**Program Structure:**\n" |
|
|
nutrition_md += f"- **Initial Phase:** Weekly consultations (first 6 weeks)\n" |
|
|
nutrition_md += f"- **Maintenance Phase:** Bi-weekly check-ins\n" |
|
|
nutrition_md += f"- **Total Duration:** {n_duration} comprehensive program\n" |
|
|
|
|
|
|
|
|
strategies = { |
|
|
"Anti-inflammatory": [], |
|
|
"Macronutrient": [], |
|
|
"Weight": [] |
|
|
} |
|
|
|
|
|
for item in n_content: |
|
|
if "Anti-inflammatory" in item or "Adequacy" in item: |
|
|
strategies["Anti-inflammatory"].append(item) |
|
|
elif "macronutrient" in item or "Balance" in item: |
|
|
strategies["Macronutrient"].append(item) |
|
|
elif "calorie" in item.lower() or "Calorie control" in item: |
|
|
strategies["Weight"].append(item) |
|
|
|
|
|
nutrition_md += "**Key Nutritional Strategies:**\n" |
|
|
if strategies["Anti-inflammatory"]: |
|
|
nutrition_md += "1. **Anti-inflammatory Focus**\n" |
|
|
nutrition_md += " - Incorporate omega-3 rich foods (fatty fish, walnuts, flaxseeds)\n" |
|
|
nutrition_md += " - Increase consumption of antioxidant-rich leafy greens\n" |
|
|
nutrition_md += " - Integrate nuts and seeds for micronutrient support\n" |
|
|
nutrition_md += " - Purpose: Reduce joint inflammation and support tissue repair\n" |
|
|
if strategies["Macronutrient"]: |
|
|
nutrition_md += "2. **Macronutrient Optimization**\n" |
|
|
nutrition_md += " - Ensure adequate protein intake to support muscle maintenance\n" |
|
|
nutrition_md += " - Balance complex carbohydrates for sustained energy\n" |
|
|
nutrition_md += " - Include healthy fats to support joint lubrication\n" |
|
|
nutrition_md += " - Purpose: Enhance musculoskeletal strength and joint function\n" |
|
|
if strategies["Weight"]: |
|
|
nutrition_md += "3. **Weight Management**\n" |
|
|
nutrition_md += " - Implement portion awareness techniques\n" |
|
|
nutrition_md += " - Monitor caloric balance through guided food journaling\n" |
|
|
nutrition_md += " - Adjust intake based on activity levels and rehabilitation phases\n" |
|
|
nutrition_md += " - Purpose: Reduce mechanical stress on knee joints\n" |
|
|
|
|
|
nutrition_html = render_agent_message_return_html( |
|
|
role="C. Nutritional & Psychological Specialist Agent", |
|
|
action_html=markdown.markdown(nutrition_md, extensions=["extra", "nl2br"]), |
|
|
style_class="nutrition" |
|
|
) |
|
|
html_blocks.append(nutrition_html) |
|
|
|
|
|
|
|
|
psych = plan_data.get("psychology", {}) |
|
|
p_goal = psych.get("goal", "") |
|
|
p_duration = psych.get("duration", "") |
|
|
p_content = psych.get("content", []) |
|
|
|
|
|
psychology_md = "#### Psychological Support\n" |
|
|
psychology_md += f"**Goal:** {p_goal}\n\n" |
|
|
psychology_md += f"**Delivery Method:** Tele-health Cognitive Behavioral Therapy with structured daily practice components\n" |
|
|
psychology_md += f"**Program Structure:**\n" |
|
|
psychology_md += f"- **Intensive Phase:** Weekly sessions (first 8 weeks)\n" |
|
|
psychology_md += f"- **Consolidation Phase:** Bi-weekly sessions\n" |
|
|
psychology_md += f"- **Total Duration:** {p_duration} comprehensive program\n" |
|
|
|
|
|
psychology_md += "**Evidence-Based Psychological Approaches:**\n" |
|
|
for idx, item in enumerate(p_content, start=1): |
|
|
if "Motivational" in item: |
|
|
psychology_md += f"{idx}. **Motivational Interviewing**\n" |
|
|
psychology_md += " - Explore personal values related to mobility and function\n" |
|
|
psychology_md += " - Resolve ambivalence about rehabilitation commitment\n" |
|
|
psychology_md += " - Develop intrinsic motivation for consistent exercise adherence\n" |
|
|
psychology_md += " - Purpose: Strengthen commitment to rehabilitation protocols\n" |
|
|
elif "CBT" in item: |
|
|
psychology_md += f"{idx}. **Cognitive Restructuring**\n" |
|
|
psychology_md += " - Identify and challenge maladaptive thoughts about pain and recovery\n" |
|
|
psychology_md += " - Transform catastrophizing patterns into realistic perspectives\n" |
|
|
psychology_md += " - Develop confidence in functional improvement\n" |
|
|
psychology_md += " - Purpose: Reduce pain-related fear and enhance rehabilitation engagement\n" |
|
|
elif "mindfulness" in item.lower(): |
|
|
psychology_md += f"{idx}. **Digital Mindfulness Integration**\n" |
|
|
psychology_md += " - Implement scheduled mindfulness practice through mobile notifications\n" |
|
|
psychology_md += " - Provide guided pain-specific meditation recordings\n" |
|
|
psychology_md += " - Track stress levels in relation to symptom fluctuations\n" |
|
|
psychology_md += " - Purpose: Enhance stress management and improve pain tolerance\n" |
|
|
|
|
|
psychology_md += "*Note: Both nutritional and psychological interventions will be coordinated with physical rehabilitation to ensure comprehensive care integration.*" |
|
|
|
|
|
psychology_html = render_agent_message_return_html( |
|
|
role="C. Nutritional & Psychological Specialist Agent", |
|
|
action_html=markdown.markdown(psychology_md, extensions=["extra", "nl2br"]), |
|
|
style_class="psychology" |
|
|
) |
|
|
html_blocks.append(psychology_html) |
|
|
|
|
|
return html_blocks |
|
|
|
|
|
|
|
|
def render_clinical_decision_agent_return_html(plan_data: Dict) -> str: |
|
|
primary_goal = plan_data.get("Goals", {}).get("Primary", "") |
|
|
secondary_goal = plan_data.get("Goals", {}).get("Secondary", "") |
|
|
|
|
|
plan = plan_data.get("InterventionPlan", {}) |
|
|
action_md = "#### Integrated Multimodal Intervention Plan\n\n" |
|
|
|
|
|
|
|
|
med_summary = plan.get("Medication", {}).get("Summary", "") |
|
|
action_md += "**🩺 Medication Strategy**\n" |
|
|
action_md += f"- {med_summary}\n\n" |
|
|
|
|
|
|
|
|
nutrition_desc = plan.get("NutritionPlan", {}).get("Description", "") |
|
|
framework = plan.get("NutritionPlan", {}).get("Framework", "") |
|
|
action_md += "**🥗 Nutrition Plan**\n" |
|
|
action_md += f"- **Framework:** {framework}\n" |
|
|
action_md += f"- {nutrition_desc}\n\n" |
|
|
|
|
|
|
|
|
exercise = plan.get("ExercisePlan", {}) |
|
|
framework = exercise.get("Framework", "") |
|
|
phases = exercise.get("Phases", {}) |
|
|
action_md += "**🏃 Exercise Plan**\n" |
|
|
action_md += f"- **Framework:** {framework}\n" |
|
|
for week_range, content in phases.items(): |
|
|
goal = content.get("Goal", "") |
|
|
prescription = content.get("Prescription", "") |
|
|
action_md += f" - **{week_range}:** {goal}\n" |
|
|
action_md += f" - {prescription}\n" |
|
|
action_md += "\n" |
|
|
|
|
|
|
|
|
psych_summary = plan.get("PsychologicalSupport", {}).get("Summary", "") |
|
|
action_md += "**🧠 Psychological Support**\n" |
|
|
action_md += f"- {psych_summary}\n\n" |
|
|
|
|
|
|
|
|
surgical_summary = plan.get("SurgicalOrInjectionConsiderations", {}).get("Summary", "") |
|
|
action_md += "**🛠️ Surgical or Injection Considerations**\n" |
|
|
action_md += f"- {surgical_summary}\n\n" |
|
|
|
|
|
|
|
|
safety_summary = plan.get("SafetyMonitoring", {}).get("Summary", "") |
|
|
action_md += "**🔍 Safety Monitoring Plan**\n" |
|
|
action_md += f"- {safety_summary}\n\n" |
|
|
|
|
|
|
|
|
accessibility = plan_data.get("AccessibilityFeasibility", "") |
|
|
rationale = plan_data.get("PersonalizationRationale", "") |
|
|
evidence = plan_data.get("EvidenceCompliance", "") |
|
|
action_md += "#### Personalized Treatment Context\n" |
|
|
action_md += f"- **Accessibility & Feasibility:** {accessibility}\n" |
|
|
action_md += f"- **Personalization Rationale:** {rationale}\n" |
|
|
action_md += f"- **Evidence Compliance:** {evidence}\n" |
|
|
|
|
|
html = render_agent_message( |
|
|
role="🧩 Clinical Decision-Making Agent", |
|
|
action=action_md, |
|
|
style_class="decision" |
|
|
) |
|
|
|
|
|
return html |
|
|
|
|
|
|
|
|
def render_progress_bar(step: int, total: int): |
|
|
'''进度条函数''' |
|
|
progress = step / total |
|
|
st.markdown(f""" |
|
|
<div style="background-color: #eee; height: 8px; width: 100%; border-radius: 4px; margin-bottom: 12px;"> |
|
|
<div style="height: 100%; width: {progress * 100:.1f}%; background-color: #4CAF50; border-radius: 4px;"></div> |
|
|
</div> |
|
|
""", unsafe_allow_html=True) |
|
|
st.markdown(f"<small style='color: grey;'>Progress: {int(progress * 100)}%</small>", unsafe_allow_html=True) |
|
|
|
|
|
|
|
|
def render_all_agents_auto(): |
|
|
total_agents = 4 |
|
|
progress_placeholder = st.empty() |
|
|
|
|
|
|
|
|
progress_placeholder.markdown(render_progress_bar_html(1, total_agents), unsafe_allow_html=True) |
|
|
exercise_plan = load_plan("exercise") |
|
|
with st.expander("A. Exercise Prescriptionist Agent", expanded=False): |
|
|
html = render_exercise_plan_return_html(exercise_plan) |
|
|
st.markdown(html, unsafe_allow_html=True) |
|
|
|
|
|
|
|
|
progress_placeholder.markdown(render_progress_bar_html(2, total_agents), unsafe_allow_html=True) |
|
|
surgical_plan = load_plan("surgical_pharma") |
|
|
with st.expander("B. Surgical & Pharmacological Specialist Agent", expanded=False): |
|
|
|
|
|
|
|
|
html_blocks = render_surgical_pharma_plan_return_html(surgical_plan) |
|
|
for html in html_blocks: |
|
|
st.markdown(html, unsafe_allow_html=True) |
|
|
|
|
|
|
|
|
progress_placeholder.markdown(render_progress_bar_html(3, total_agents), unsafe_allow_html=True) |
|
|
nutrition_plan = load_plan("nutrition_psychology") |
|
|
with st.expander("C. Nutritional & Psychological Specialist Agent", expanded=False): |
|
|
|
|
|
|
|
|
html_blocks = render_nutrition_psychology_plan_return_html(nutrition_plan) |
|
|
for html in html_blocks: |
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st.markdown(html, unsafe_allow_html=True) |
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|
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with st.spinner("Clinical Decision-Making Agent reasoning..."): |
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time.sleep(3) |
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|
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progress_placeholder.markdown(render_progress_bar_html(4, total_agents), unsafe_allow_html=True) |
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decision_plan = load_plan("clinical_integration") |
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with st.expander("D. Clinical Decision-Making Agent", expanded=False): |
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html = render_clinical_decision_agent_return_html(decision_plan) |
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st.markdown(html, unsafe_allow_html=True) |
|
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|
|
|
|
|
|
|
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def render_progress_bar_html(step: int, total: int) -> str: |
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progress = step / total |
|
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return f""" |
|
|
<div style="background-color: #eee; height: 8px; width: 100%; border-radius: 4px; margin-bottom: 12px;"> |
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|
<div style="height: 100%; width: {progress * 100:.1f}%; background-color: #4CAF50; border-radius: 4px;"></div> |
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</div> |
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<small style='color: grey;'>Progress: {int(progress * 100)}%</small> |
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""" |
|
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|
|
|
|
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def render_therapy_page(): |
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|
"""渲染治疗推荐页面""" |
|
|
inject_agent_styles() |
|
|
col1, col2 = st.columns([1.5, 1]) |
|
|
|
|
|
with col2: |
|
|
safe_image_display(IMAGE_PATHS["recommendation_framework"], "Framework for personalizing treatment", |
|
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use_container_width=True) |
|
|
|
|
|
with col1: |
|
|
st.subheader("🩺 Quick Therapy Demo") |
|
|
|
|
|
|
|
|
case_data = load_case_data() |
|
|
if not case_data: |
|
|
st.warning("Case data cannot be loaded.") |
|
|
return |
|
|
|
|
|
case_names = list(case_data.keys()) |
|
|
case_options = [""] + case_names |
|
|
|
|
|
selected_case = st.selectbox("Select a sample case：", case_options) |
|
|
|
|
|
st.markdown(get_chat_styles(), unsafe_allow_html=True) |
|
|
|
|
|
if selected_case != "": |
|
|
case = case_data[selected_case] |
|
|
|
|
|
if "start_clicked" not in st.session_state: |
|
|
st.session_state.start_clicked = False |
|
|
|
|
|
if not st.session_state.get("start_clicked", False): |
|
|
st.success(f"Selected：{selected_case}") |
|
|
|
|
|
st.markdown("### 📑 Case Reports") |
|
|
for report_name in case.get("reports", []): |
|
|
st.markdown(f"📄 {report_name}") |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
button_placeholder = st.empty() |
|
|
spacer_placeholder = st.empty() |
|
|
|
|
|
if "start_clicked" not in st.session_state: |
|
|
st.session_state.start_clicked = False |
|
|
|
|
|
if not st.session_state.start_clicked: |
|
|
if button_placeholder.button("▶️ Start Multi-Agent Reasoning"): |
|
|
st.session_state.start_clicked = True |
|
|
st.query_params.update({"start": "1"}) |
|
|
|
|
|
button_placeholder.empty() |
|
|
spacer_placeholder.markdown("<div style='height: 48px;'></div>", unsafe_allow_html=True) |
|
|
else: |
|
|
button_placeholder.empty() |
|
|
spacer_placeholder.empty() |
|
|
render_all_agents_auto() |
|
|
|
|
|
|
|
|
else: |
|
|
|
|
|
render_all_agents_auto() |
