app commit

app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+import matplotlib.pyplot as plt
+import time
+
+# Load the pre-trained numerical imputer, scaler, and model using joblib
+num_imputer = joblib.load('numerical_imputer.joblib')
+scaler = joblib.load('scaler.joblib')
+model = joblib.load('Final_model.joblib')
+
+# Define a function to preprocess the input data
+def preprocess_input_data(input_data):
+    input_data_df = pd.DataFrame(input_data, columns=['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age', 'Insurance'])
+    num_columns = input_data_df.select_dtypes(include='number').columns
+
+    input_data_imputed_num = num_imputer.transform(input_data_df[num_columns])
+    input_scaled_df = pd.DataFrame(scaler.transform(input_data_imputed_num), columns=num_columns)
+
+    return input_scaled_df
+
+# Define a function to make the sepsis prediction
+def predict_sepsis(input_data):
+    input_scaled_df = preprocess_input_data(input_data)
+    prediction = model.predict(input_scaled_df)[0]
+    probabilities = model.predict_proba(input_scaled_df)[0]
+    sepsis_status = "Positive" if prediction == 1 else "Negative"
+
+    output_df = pd.DataFrame(input_data, columns=['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age', 'Insurance'])
+    output_df['Prediction'] = sepsis_status
+    output_df['Negative Probability'] = probabilities[0]
+    output_df['Positive Probability'] = probabilities[1]
+
+    return output_df, probabilities
+
+# Create a Streamlit app
+def main():
+    st.title('Sepsis Prediction App')
+
+    st.image("Strealit_.jpg")
+
+    # How to use
+    st.sidebar.title('How to Use')
+    st.sidebar.markdown('1. Adjust the input parameters on the left sidebar.')
+    st.sidebar.markdown('2. Click the "Predict" button to initiate the prediction.')
+    st.sidebar.markdown('3. The app will simulate a prediction process with a progress bar.')
+    st.sidebar.markdown('4. Once the prediction is complete, the results will be displayed below.')
+
+
+    st.sidebar.title('Input Parameters')
+
+    # Input parameter explanations
+    st.sidebar.markdown('**PRG:** Plasma Glucose')
+    PRG = st.sidebar.number_input('PRG', value=0.0)
+
+    st.sidebar.markdown('**PL:** Blood Work Result 1')
+    PL = st.sidebar.number_input('PL', value=0.0)
+
+    st.sidebar.markdown('**PR:** Blood Pressure Measured')
+    PR = st.sidebar.number_input('PR', value=0.0)
+
+    st.sidebar.markdown('**SK:** Blood Work Result 2')
+    SK = st.sidebar.number_input('SK', value=0.0)
+
+    st.sidebar.markdown('**TS:** Blood Work Result 3')
+    TS = st.sidebar.number_input('TS', value=0.0)
+
+    st.sidebar.markdown('**M11:** BMI')
+    M11 = st.sidebar.number_input('M11', value=0.0)
+
+    st.sidebar.markdown('**BD2:** Blood Work Result 4')
+    BD2 = st.sidebar.number_input('BD2', value=0.0)
+
+    st.sidebar.markdown('**Age:** What is the Age of the Patient: ')
+    Age = st.sidebar.number_input('Age', value=0.0)
+
+    st.sidebar.markdown('**Insurance:** Does the patient have Insurance?')
+    insurance_options = {0: 'NO', 1: 'YES'}
+    Insurance = st.sidebar.radio('Insurance', list(insurance_options.keys()), format_func=lambda x: insurance_options[x])
+
+
+    input_data = [[PRG, PL, PR, SK, TS, M11, BD2, Age, Insurance]]
+
+    if st.sidebar.button('Predict'):
+        with st.spinner("Predicting..."):
+            # Simulate a long-running process
+            progress_bar = st.progress(0)
+            for i in range(100):
+                time.sleep(0.1)
+                progress_bar.progress(i + 1)
+
+            output_df, probabilities = predict_sepsis(input_data)
+
+            st.subheader('Prediction Result')
+            st.write(output_df)
+
+            # Plot the probabilities
+            fig, ax = plt.subplots()
+            ax.bar(['Negative', 'Positive'], probabilities)
+            ax.set_xlabel('Sepsis Status')
+            ax.set_ylabel('Probability')
+            ax.set_title('Sepsis Prediction Probabilities')
+            st.pyplot(fig)
+
+            # Print feature importance
+
+            if hasattr(model, 'coef_'):
+                feature_importances = model.coef_[0]
+                feature_names = ['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age', 'Insurance']
+
+                importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})
+                importance_df = importance_df.sort_values('Importance', ascending=False)
+
+                st.subheader('Feature Importance')
+                fig, ax = plt.subplots()
+                bars = ax.bar(importance_df['Feature'], importance_df['Importance'])
+                ax.set_xlabel('Feature')
+                ax.set_ylabel('Importance')
+                ax.set_title('Feature Importance')
+                ax.tick_params(axis='x', rotation=45)
+
+                # Add data labels to the bars
+                for bar in bars:
+                    height = bar.get_height()
+                    ax.annotate(f'{height:.2f}', xy=(bar.get_x() + bar.get_width() / 2, height),
+                    xytext=(0, 3),  # 3 points vertical offset
+                    textcoords="offset points",
+                    ha='center', va='bottom')
+                st.pyplot(fig)
+
+
+                #st.subheader('Feature Importance')
+                #st.write(importance_df)
+            else:
+                st.write('Feature importance is not available for this model.')
+
+if __name__ == '__main__':
+    main()