Update app.py

app.py

@@ -1,101 +1,100 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-from sklearn.ensemble import GradientBoostingClassifier
-from sklearn.preprocessing import StandardScaler
-
-st.set_page_config(page_title="Liver Disease Prediction App", layout="wide")
-
-# Load the data and preprocess
-@st.cache_data
-def load_data():
-    data = pd.read_csv('Liver_disease_data.csv')
-    X = data.drop('Diagnosis', axis=1)
-    y = data['Diagnosis']
-    # One-hot encode the Gender column
-    X = pd.get_dummies(X, columns=['Gender'], prefix='Gender', drop_first=True)
-    return X, y
-
-X, y = load_data()
-
-# Train the model
-@st.cache_resource
-def train_model():
-    scaler = StandardScaler()
-    X_scaled = scaler.fit_transform(X)
-    model = GradientBoostingClassifier(random_state=42)
-    model.fit(X_scaled, y)
-    return scaler, model
-
-scaler, model = train_model()
-
-st.title('Liver Disease Prediction App')
-
-st.write('Enter the following information to predict liver disease:')
-
-# Create two columns for input fields
-col1, col2 = st.columns(2)
-
-with col1:
-    age = st.number_input('Age', min_value=0, max_value=120, value=30)
-    gender = st.selectbox('Gender', [0, 1], format_func=lambda x: 'Female' if x == 0 else 'Male')
-    bmi = st.number_input('BMI', min_value=0.0, max_value=50.0, value=25.0, format="%.1f")
-    alcohol_consumption = st.number_input('Alcohol Consumption', min_value=0.0, value=5.0, format="%.1f")
-    smoking = st.selectbox('Smoking', [0, 1], format_func=lambda x: 'No' if x == 0 else 'Yes')
-
-with col2:
-    genetic_risk = st.selectbox('Genetic Risk', [0, 1], format_func=lambda x: 'No' if x == 0 else 'Yes')
-    physical_activity = st.number_input('Physical Activity', min_value=0.0, value=1.0, format="%.1f")
-    diabetes = st.selectbox('Diabetes', [0, 1], format_func=lambda x: 'No' if x == 0 else 'Yes')
-    hypertension = st.selectbox('Hypertension', [0, 1], format_func=lambda x: 'No' if x == 0 else 'Yes')
-    liver_function_test = st.number_input('Liver Function Test', min_value=0.0, value=50.0, format="%.1f")
-
-# Center the predict button
-col1, col2, col3 = st.columns([1, 1, 1])
-with col2:
-    predict_button = st.button('Predict', use_container_width=True)
-
-if predict_button:
-    # Prepare input data
-    input_data = pd.DataFrame({
-        'Age': [age],
-        'BMI': [bmi],
-        'AlcoholConsumption': [alcohol_consumption],
-        'Smoking': [smoking],
-        'GeneticRisk': [genetic_risk],
-        'PhysicalActivity': [physical_activity],
-        'Diabetes': [diabetes],
-        'Hypertension': [hypertension],
-        'LiverFunctionTest': [liver_function_test],
-        'Gender': [gender]
-    })
-
-    # One-hot encode the Gender column to match the training data
-    input_data = pd.get_dummies(input_data, columns=['Gender'], prefix='Gender', drop_first=True)
-
-    # Ensure all columns from training data are present
-    for col in X.columns:
-        if col not in input_data.columns:
-            input_data[col] = 0
-
-    # Reorder columns to match training data
-    input_data = input_data[X.columns]
-
-    # Scale the input data
-    input_scaled = scaler.transform(input_data)
-
-    # Make prediction
-    prediction = model.predict(input_scaled)
-    probability = model.predict_proba(input_scaled)[0][1]
-
-    st.markdown("---")
-    st.subheader('Prediction Results:')
-    if prediction[0] == 1:
-        st.warning('The Patient has High risk of liver disease')
-    else:
-        st.success('The Patient has Low risk of liver disease')
-    
-    st.write(f'Probability of liver disease: {probability:.2f}')
-
-st.markdown("---")
+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.preprocessing import StandardScaler
+
+st.set_page_config(page_title="Liver Disease Prediction App", layout="wide")
+
+# Loading the data and preprocessing steps
+@st.cache_data
+def load_data():
+    data = pd.read_csv('Liver_disease_data.csv')
+    X = data.drop('Diagnosis', axis=1)
+    y = data['Diagnosis']
+    # One-hot encode the Gender column
+    X = pd.get_dummies(X, columns=['Gender'], prefix='Gender', drop_first=True)
+    return X, y
+
+X, y = load_data()
+
+# Training the model
+@st.cache_resource
+def train_model():
+    scaler = StandardScaler()
+    X_scaled = scaler.fit_transform(X)
+    model = GradientBoostingClassifier(random_state=42)
+    model.fit(X_scaled, y)
+    return scaler, model
+
+scaler, model = train_model()
+
+st.title('Liver Disease Prediction App')
+
+st.write('Enter the following information to predict liver disease:')
+
+# Creating two columns for input fields
+col1, col2 = st.columns(2)
+
+with col1:
+    age = st.number_input('Age', min_value=0, max_value=120, value=30)
+    gender = st.selectbox('Gender', [0, 1], format_func=lambda x: 'Female' if x == 0 else 'Male')
+    bmi = st.number_input('BMI', min_value=0.0, max_value=50.0, value=25.0, format="%.1f")
+    alcohol_consumption = st.number_input('Alcohol Consumption', min_value=0.0, value=5.0, format="%.1f")
+    smoking = st.selectbox('Smoking', [0, 1], format_func=lambda x: 'No' if x == 0 else 'Yes')
+
+with col2:
+    genetic_risk = st.selectbox('Genetic Risk', [0, 1], format_func=lambda x: 'No' if x == 0 else 'Yes')
+    physical_activity = st.number_input('Physical Activity', min_value=0.0, value=1.0, format="%.1f")
+    diabetes = st.selectbox('Diabetes', [0, 1], format_func=lambda x: 'No' if x == 0 else 'Yes')
+    hypertension = st.selectbox('Hypertension', [0, 1], format_func=lambda x: 'No' if x == 0 else 'Yes')
+    liver_function_test = st.number_input('Liver Function Test', min_value=0.0, value=50.0, format="%.1f")
+
+col1, col2, col3 = st.columns([1, 1, 1])
+with col2:
+    predict_button = st.button('Predict', use_container_width=True)
+
+if predict_button:
+    # Preparing the input data
+    input_data = pd.DataFrame({
+        'Age': [age],
+        'BMI': [bmi],
+        'AlcoholConsumption': [alcohol_consumption],
+        'Smoking': [smoking],
+        'GeneticRisk': [genetic_risk],
+        'PhysicalActivity': [physical_activity],
+        'Diabetes': [diabetes],
+        'Hypertension': [hypertension],
+        'LiverFunctionTest': [liver_function_test],
+        'Gender': [gender]
+    })
+
+    # One-hot encode the Gender column to match the training data
+    input_data = pd.get_dummies(input_data, columns=['Gender'], prefix='Gender', drop_first=True)
+
+    # Ensuring all the columns from training data are present
+    for col in X.columns:
+        if col not in input_data.columns:
+            input_data[col] = 0
+
+    # Reordering the columns to match training data
+    input_data = input_data[X.columns]
+
+    # Scaling the input data
+    input_scaled = scaler.transform(input_data)
+
+    # Making prediction
+    prediction = model.predict(input_scaled)
+    probability = model.predict_proba(input_scaled)[0][1]
+
+    st.markdown("---")
+    st.subheader('Prediction Results:')
+    if prediction[0] == 1:
+        st.warning('The Patient has High risk of having liver disease')
+    else:
+        st.success('The Patient has Low risk of having liver disease')
+    
+    st.write(f'Probability of liver disease: {probability:.2f}')
+
+st.markdown("---")
 st.write('Note: This app is for educational purposes only. Always consult a medical professional for accurate diagnosis and advice.')