Docker封装临床预测模型
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt \
&& apt-get update && apt-get install -y libgomp1
COPY . .
EXPOSE 8501
CMD ["streamlit", "run", "app.py"]
将生存分析模型封装为Web应用
import streamlit as st
import joblib
@st.cache_data
def load_model():
return joblib.load('rsf_model.pkl')
st.title('肺癌生存预测系统')
age = st.slider('患者年龄', 30, 90, 65)
mutation = st.selectbox('EGFR突变状态', ['野生型', 'L858R', '19del'])
if st.button('预测'):
risk = model.predict([[age, mutation_map[mutation]]])
st.plotly_chart(plot_survival_curve(risk))