import streamlit as st
import requests
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import traceback
def show_liver_disease_prediction():
st.title("Liver Disease Prediction")
# Create sidebar for inputs
with st.sidebar:
st.header("Patient Details")
# Personal Information
st.subheader("Personal Information")
age = st.slider("Age", 10, 90, 45)
gender = st.radio("Gender", ["Male", "Female"])
# Liver Function Tests
st.subheader("Liver Function Tests")
total_bilirubin = st.number_input("Total Bilirubin", min_value=0.1, max_value=100.0, value=1.0, step=0.1,
help="Normal range: 0.3-1.2 mg/dL")
direct_bilirubin = st.number_input("Direct Bilirubin", min_value=0.0, max_value=50.0, value=0.3, step=0.1,
help="Normal range: 0.0-0.3 mg/dL")
# Enzyme Levels
st.subheader("Enzyme Levels")
alkaline_phosphotase = st.number_input("Alkaline Phosphotase", min_value=20, max_value=2000, value=290, step=10,
help="Normal range: 44-147 IU/L")
alamine_aminotransferase = st.number_input("Alamine Aminotransferase (ALT)", min_value=1, max_value=2000, value=40, step=1,
help="Normal range: 7-55 IU/L")
aspartate_aminotransferase = st.number_input("Aspartate Aminotransferase (AST)", min_value=1, max_value=2000, value=40, step=1,
help="Normal range: 8-48 IU/L")
# Protein Levels
st.subheader("Protein Levels")
total_protiens = st.number_input("Total Proteins", min_value=1.0, max_value=10.0, value=6.8, step=0.1,
help="Normal range: 6.0-8.3 g/dL")
albumin = st.number_input("Albumin", min_value=0.5, max_value=10.0, value=3.5, step=0.1,
help="Normal range: 3.5-5.0 g/dL")
albumin_globulin_ratio = st.number_input("Albumin/Globulin Ratio", min_value=0.1, max_value=5.0, value=1.0, step=0.1,
help="Normal range: 0.8-2.0")
# Submit button
predict_button = st.button("Predict Liver Disease Risk")
# Main content area
if predict_button:
# Prepare data for API
features = {
"Age": age,
"Gender": gender,
"Total_Bilirubin": total_bilirubin,
"Direct_Bilirubin": direct_bilirubin,
"Alkaline_Phosphotase": alkaline_phosphotase,
"Alamine_Aminotransferase": alamine_aminotransferase,
"Aspartate_Aminotransferase": aspartate_aminotransferase,
"Total_Protiens": total_protiens,
"Albumin": albumin,
"Albumin_and_Globulin_Ratio": albumin_globulin_ratio
}
# Call API
with st.spinner("Predicting..."):
try:
response = requests.post(
"http://localhost:8000/predict/liver",
json=features
)
if response.status_code == 200:
result = response.json()
# Display results
col1, col2 = st.columns([3, 2])
with col1:
# Prediction result
if result["prediction"]:
st.error("### High Risk of Liver Disease ⚠️")
st.markdown("The patient shows indicators consistent with potential liver disease.")
else:
st.success("### Low Risk of Liver Disease ✅")
st.markdown("The patient's indicators suggest normal liver function.")
# Enhanced explanation
st.info("### Key Indicators Analysis")
# Create a dataframe to show normal ranges vs patient values
indicators = {
"Indicator": [
"Total Bilirubin",
"Direct Bilirubin",
"Alkaline Phosphotase",
"ALT",
"AST",
"Total Proteins",
"Albumin",
"Albumin/Globulin Ratio"
],
"Patient Value": [
total_bilirubin,
direct_bilirubin,
alkaline_phosphotase,
alamine_aminotransferase,
aspartate_aminotransferase,
total_protiens,
albumin,
albumin_globulin_ratio
],
"Normal Range": [
"0.3-1.2 mg/dL",
"0.0-0.3 mg/dL",
"44-147 IU/L",
"7-55 IU/L",
"8-48 IU/L",
"6.0-8.3 g/dL",
"3.5-5.0 g/dL",
"0.8-2.0"
],
"Status": [
"Normal" if 0.3 <= total_bilirubin <= 1.2 else "Abnormal",
"Normal" if 0.0 <= direct_bilirubin <= 0.3 else "Abnormal",
"Normal" if 44 <= alkaline_phosphotase <= 147 else "Abnormal",
"Normal" if 7 <= alamine_aminotransferase <= 55 else "Abnormal",
"Normal" if 8 <= aspartate_aminotransferase <= 48 else "Abnormal",
"Normal" if 6.0 <= total_protiens <= 8.3 else "Abnormal",
"Normal" if 3.5 <= albumin <= 5.0 else "Abnormal",
"Normal" if 0.8 <= albumin_globulin_ratio <= 2.0 else "Abnormal"
]
}
indicators_df = pd.DataFrame(indicators)
# Style the dataframe
def highlight_abnormal(val):
if val == "Abnormal":
return 'background-color: #ffcccb'
else:
return 'background-color: #90ee90'
styled_df = indicators_df.style.applymap(highlight_abnormal, subset=['Status'])
st.dataframe(styled_df, use_container_width=True)
# Feature importance visualization if available
if "feature_importance" in result and result["feature_importance"]:
st.subheader("Factors Affecting Liver Disease Risk")
# Create feature importance dataframe
feature_names = list(features.keys())
importance_values = result["feature_importance"]
# If lengths don't match, use default values
if len(importance_values) != len(feature_names):
importance_values = [0.15, 0.05, 0.12, 0.08, 0.18, 0.14, 0.10, 0.08, 0.06, 0.04]
importance_df = pd.DataFrame({
"Feature": feature_names,
"Importance": importance_values
})
importance_df = importance_df.sort_values("Importance", ascending=False)
fig = px.bar(
importance_df,
x="Importance",
y="Feature",
orientation='h',
title="Feature Importance",
color="Importance",
color_continuous_scale=["#90ee90", "#ffcccb"]
)
st.plotly_chart(fig, use_container_width=True)
# Recommendations section
st.subheader("Recommendations")
if result["prediction"]:
st.markdown("""
* **Consult a Hepatologist:** Schedule an appointment with a liver specialist for further evaluation
* **Additional Testing:** Consider ultrasound, CT scan, or liver biopsy for definitive diagnosis
* **Lifestyle Changes:**
* Limit alcohol consumption
* Maintain a healthy weight
* Follow a liver-friendly diet low in processed foods and sugar
* Regular exercise
* **Medication Review:** Discuss current medications with your doctor as some may affect liver function
""")
else:
st.markdown("""
* **Regular Check-ups:** Continue routine health screenings
* **Healthy Lifestyle:**
* Maintain a balanced diet rich in fruits, vegetables, and whole grains
* Regular physical activity
* Limit alcohol consumption
* Stay hydrated
* **Liver Protection:** Avoid unnecessary medications that may strain the liver
""")
with col2:
# Risk probability gauge
st.subheader("Disease Risk Probability")
probability = result["probability"]
# Create gauge chart
fig = go.Figure(go.Indicator(
mode="gauge+number",
value=probability * 100,
domain={'x': [0, 1], 'y': [0, 1]},
title={'text': "Risk Level"},
gauge={
'axis': {'range': [0, 100], 'tickwidth': 1},
'bar': {'color': "darkred" if probability > 0.7 else "orange" if probability > 0.3 else "green"},
'steps': [
{'range': [0, 30], 'color': 'rgba(0, 128, 0, 0.3)'},
{'range': [30, 70], 'color': 'rgba(255, 165, 0, 0.3)'},
{'range': [70, 100], 'color': 'rgba(255, 0, 0, 0.3)'}
],
'threshold': {
'line': {'color': "red", 'width': 4},
'thickness': 0.75,
'value': probability * 100
}
}
))
fig.update_layout(
height=300,
margin=dict(l=20, r=20, t=50, b=20)
)
st.plotly_chart(fig, use_container_width=True)
# Risk level explanation
risk_level = "High" if probability > 0.7 else "Moderate" if probability > 0.3 else "Low"
risk_color = "red" if probability > 0.7 else "orange" if probability > 0.3 else "green"
st.markdown(f"{risk_level} Risk
", unsafe_allow_html=True)
# Liver health score
st.subheader("Liver Health Indicators")
# Calculate abnormal indicators
abnormal_count = sum(1 for status in indicators["Status"] if status == "Abnormal")
health_score = 100 - (abnormal_count / len(indicators["Status"])) * 100
# Display health score
st.metric(
label="Liver Health Score",
value=f"{health_score:.1f}%",
delta=None
)
# Create progress bar for health score
health_color = "green" if health_score > 70 else "orange" if health_score > 40 else "red"
st.markdown(f"""
""", unsafe_allow_html=True)
# Age and gender analysis
st.subheader("Demographic Analysis")
# Age risk factor
age_risk = "Higher" if age > 50 else "Moderate" if age > 35 else "Lower"
age_color = "red" if age > 50 else "orange" if age > 35 else "green"
st.markdown(f"""
Age Risk Factor:
{age_risk}
""", unsafe_allow_html=True)
# Gender risk factor
gender_risk = "Higher" if gender == "Male" else "Lower"
gender_color = "orange" if gender == "Male" else "green"
st.markdown(f"""
Gender Risk Factor:
{gender_risk}
Males typically have higher risk of liver disease than females.
""", unsafe_allow_html=True)
# Disclaimer
st.info("⚠️ Disclaimer: This prediction is for informational purposes only and should not replace professional medical advice.")
else:
st.error(f"Error: API returned status code {response.status_code}")
try:
error_detail = response.json()
st.error(f"Error details: {error_detail}")
except:
st.error(response.text)
except Exception as e:
st.error(f"Error connecting to API: {str(e)}")
st.error(traceback.format_exc())
st.info("Make sure the FastAPI backend is running with: python -m uvicorn src.api.main:app --reload")
if __name__ == "__main__":
show_liver_disease_prediction()