| import streamlit as st
|
| import pandas as pd
|
| import numpy as np
|
| import joblib
|
| import base64
|
|
|
| def get_image_base64(file_path):
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| with open(file_path, "rb") as f:
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| data = f.read()
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| return base64.b64encode(data).decode()
|
|
|
| def streamlit_layout():
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| st.set_page_config(
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| page_title="Diabetes Risk Analyzer",
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| page_icon="🩺",
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| layout="wide"
|
| )
|
|
|
| image_base64 = get_image_base64("Cover_pic.png")
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| st.markdown(
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| f"""
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| <div style="text-align: center;">
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| <img src="data:image/png;base64,{image_base64}" style="width: 1600px; max-width: 100%; height: auto;"/>
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| </div>
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| """,
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| unsafe_allow_html=True
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| )
|
|
|
| def css_markdown():
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| st.markdown("""
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| <style>
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| .main-title {
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| font-size: 36px !important;
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| color: #0077b6;
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| text-align: center;
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| }
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| .stButton > button {
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| background-color: #0077b6;
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| color: white;
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| border-radius: 8px;
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| height: 3em;
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| width: 100%;
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| }
|
|
|
| div[class*="stRadio"] > label > div[data-testid="stMarkdownContainer"] > p {
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| font-size: 18px;
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| }
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|
|
| div[class*="stSlider"] > label > div[data-testid="stMarkdownContainer"] > p {
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| font-size: 18px;
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| }
|
|
|
| .stTabs [data-baseweb="tab-list"] button [data-testid="stMarkdownContainer"] p {
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| font-size:18px;
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| font-weight: bold;
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| color: #004085;
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| }
|
|
|
| </style>
|
| <br>
|
| """, unsafe_allow_html=True)
|
|
|
| def get_user_input(feature_names, col_map, col_list):
|
|
|
| model_details = joblib.load("model_details.pkl")
|
|
|
| binary_cols = ['HighBP', 'HighChol', 'CholCheck', 'Smoker', 'Stroke',
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| 'HeartDiseaseorAttack', 'PhysActivity', 'Fruits', 'Veggies',
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| 'HvyAlcoholConsump', 'AnyHealthcare', 'NoDocbcCost', 'DiffWalk', 'Sex']
|
| numeric_cols = ['GenHlth', 'MentHlth', 'PhysHlth', 'Age', 'Education', 'Income','BMI_box']
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|
|
| user_input = pd.DataFrame(columns=feature_names)
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|
|
| for i, feature in enumerate(feature_names):
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| label = col_map[feature]
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| if feature in binary_cols:
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| with col_list[i % 3]:
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| if feature == 'Sex':
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| val = st.radio(label, ["Male", "Female"], horizontal=True)
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| user_input.loc[0, feature] = 1 if val == "Male" else 0
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| else:
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| val = st.radio(label, ["No", "Yes"], horizontal=True)
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| user_input.loc[0, feature] = 1 if val == "Yes" else 0
|
| elif feature in numeric_cols:
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| step = 1
|
| with col_list[i % 3]:
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| if feature == 'BMI_box':
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| val = st.slider(label, min_value=0, max_value=100, step=step,
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| help="BMI = weight (lb) / height² (inches) * 703")
|
| user_input.loc[0, feature] = apply_boxcox(val, model_details["Fitted Lambda"]["BMI"])
|
| elif feature == 'GenHlth':
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| val = st.slider(label, min_value=0, max_value=5, step=step,
|
| help="Would you say that in general your health is: scale " \
|
| "1-5 1 = excellent 2 = very good 3 = good 4 = fair 5 = poor")
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| user_input.loc[0, feature] = val
|
| elif feature == 'MentHlth':
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| val = st.slider(label, min_value=0, max_value=30, step=step,
|
| help="Thinking about your mental health, which includes stress, depression, " \
|
| "and problems with emotions, for how many days during the past 30 days was " \
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| "your mental health not good? scale 1-30 days")
|
| user_input.loc[0, feature] = val
|
| elif feature == 'PhysHlth':
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| val = st.slider(label, min_value=0, max_value=30, step=step,
|
| help="thinking about your physical health, which includes physical illness "
|
| "and injury, for how many days during the past 30 days was your physical " \
|
| "health not good? scale 1-30 days")
|
| user_input.loc[0, feature] = val
|
| elif feature == 'Age':
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| val = st.slider(label, min_value=0, max_value=13, step=step,
|
| help="13-level age category (_AGEG5YR see codebook) 1 = 18-24 9 = 60-64 13 = 80 or older")
|
| user_input.loc[0, feature] = val
|
| elif feature == 'Education':
|
| val = st.slider(label, min_value=0, max_value=6, step=step,
|
| help="Education level (EDUCA see codebook) scale 1-6 1 = Never attended school or only " \
|
| "kindergarten , 2 = Grades 1 through 8 (Elementary) ,3 = Grades 9 throug 11 (Some high school), " \
|
| "4 = Grade 12 or GED (High school graduate) , 5 = College 1 year to 3 years (Some college or technical school) " \
|
| ", 6 = College 4 years or more (College graduate).")
|
| user_input.loc[0, feature] = val
|
| elif feature == 'Income':
|
| val = st.slider(label, min_value=0, max_value=13, step=step,
|
| help="1 = less than $10,000 5 = less than $35,000 8 = $75,000 or more")
|
| user_input.loc[0, feature] = val
|
| else:
|
| val = st.slider(label, min_value=0, max_value=100, step=step)
|
| user_input.loc[0, feature] = val
|
|
|
| return user_input
|
|
|
|
|
| def apply_boxcox(x, lmbda):
|
| if lmbda == 0:
|
| return np.log(x + 0.001)
|
| else:
|
| return ((x + 0.001) ** lmbda - 1) / lmbda
|
| |
