Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.svm import SVR
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.model_selection import train_test_split
+import time
+
+# Wide mode
+st.set_page_config(layout="wide")
+
+# Initialize parameters
+params = {
+    'Beds': 100,
+    'Doctors': 50,
+    'Nurses': 100,
+    'Ventilators': 20,
+    'ICU_Beds': 20,
+    'Surgical_Suites': 5,
+    'Emergency_Room_Beds': 10,
+    'Pharmacy_Staff': 5,
+    'Lab_Staff': 5,
+    'Radiology_Staff': 5,
+    'Patient_Admissions': 50,
+    'Patient_Discharges': 40,
+    'Average_Stay': 5,
+    'ICU_Admissions': 10,
+    'Surgical_Cases': 20,
+    'Emergency_Room_Visits': 30,
+    'Pharmacy_Requests': 50,
+    'Lab_Requests': 40,
+    'Radiology_Requests': 30
+}
+
+# Create a Streamlit app
+st.title("Hospital Resource Allocation Simulator")
+
+# Add a sidebar for input parameters
+st.sidebar.header("Input Parameters")
+for param, value in params.items():
+    params[param] = st.sidebar.slider(param, 0, 200, value)
+
+# Create a button to start the simulation
+if st.sidebar.button("Start Live Simulation"):
+    st.header("Live Simulation")
+    st.write("Simulation started. Graphs will update every second to represent hourly changes in resource allocation.")
+
+    # Initialize dataframes to store simulation data
+    beds_df = pd.DataFrame(index=pd.date_range(start='2022-01-01', periods=24, freq='H'), columns=['Available', 'Occupied'])
+    doctors_df = pd.DataFrame(index=pd.date_range(start='2022-01-01', periods=24, freq='H'), columns=['Available', 'Busy'])
+    nurses_df = pd.DataFrame(index=pd.date_range(start='2022-01-01', periods=24, freq='H'), columns=['Available', 'Busy'])
+    ventilators_df = pd.DataFrame(index=pd.date_range(start='2022-01-01', periods=24, freq='H'), columns=['Available', 'In_Use'])
+    icu_beds_df = pd.DataFrame(index=pd.date_range(start='2022-01-01', periods=24, freq='H'), columns=['Available', 'Occupied'])
+    surgical_suites_df = pd.DataFrame(index=pd.date_range(start='2022-01-01', periods=24, freq='H'), columns=['Available', 'In_Use'])
+    emergency_room_beds_df = pd.DataFrame(index=pd.date_range(start='2022-01-01', periods=24, freq='H'), columns=['Available', 'Occupied'])
+    pharmacy_staff_df = pd.DataFrame(index=pd.date_range(start='2022-01-01', periods=24, freq='H'), columns=['Available', 'Busy'])
+    lab_staff_df = pd.DataFrame(index=pd.date_range(start='2022-01-01', periods=24, freq='H'), columns=['Available', 'Busy'])
+    radiology_staff_df = pd.DataFrame(index=pd.date_range(start='2022-01-01', periods=24, freq='H'), columns=['Available', 'Busy'])
+
+    # Initialize log for resource usage
+    log = []
+
+    # Generate synthetic data for training
+    np.random.seed(42)
+    X = pd.DataFrame(np.random.randint(0, 100, size=(100, len(params))), columns=params.keys())
+    y = np.random.randint(0, 100, size=100)  # Dummy target for demonstration
+
+    # Split data for training and testing
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Initialize regression models
+    models = {
+        'Linear Regression': LinearRegression(),
+        'Random Forest Regression': RandomForestRegressor(),
+        'SVR': SVR(),
+        'Decision Tree Regression': DecisionTreeRegressor(),
+        'Gradient Boosting Regression': GradientBoostingRegressor()
+    }
+
+    # Predict values for each parameter using each model
+    predicted_values = {}
+    for name, model in models.items():
+        predictions = model.fit(X_train, y_train).predict(X_test)
+        predicted_values[name] = predictions
+
+    # Display predicted values
+    # st.subheader("Predicted Parameter Values")
+    # predicted_df = pd.DataFrame(predicted_values, index=X_test.index)
+    # st.write(predicted_df)
+
+    # Create a grid layout for the graphs
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        st.subheader("Beds Availability")
+        beds_chart = st.line_chart(beds_df)
+
+    with col2:
+        st.subheader("Doctors Availability")
+        doctors_chart = st.line_chart(doctors_df)
+
+    with col3:
+        st.subheader("Nurses Availability")
+        nurses_chart = st.line_chart(nurses_df)
+
+    col4, col5, col6 = st.columns(3)
+
+    with col4:
+        st.subheader("Ventilators Availability")
+        ventilators_chart = st.line_chart(ventilators_df)
+
+    with col5:
+        st.subheader("ICU Beds Availability")
+        icu_beds_chart = st.line_chart(icu_beds_df)
+
+    with col6:
+        st.subheader("Surgical Suites Availability")
+        surgical_suites_chart = st.line_chart(surgical_suites_df)
+
+    col7, col8, col9 = st.columns(3)
+
+    with col7:
+        st.subheader("Emergency Room Beds Availability")
+        emergency_room_beds_chart = st.line_chart(emergency_room_beds_df)
+
+    with col8:
+        st.subheader("Pharmacy Staff Availability")
+        pharmacy_staff_chart = st.line_chart(pharmacy_staff_df)
+
+    with col9:
+        st.subheader("Lab Staff Availability")
+        lab_staff_chart = st.line_chart(lab_staff_df)
+
+    col10, col11, col12 = st.columns(3)
+
+    with col10:
+        st.subheader("Radiology Staff Availability")
+        radiology_staff_chart = st.line_chart(radiology_staff_df)
+
+    # Start the simulation
+    for i in range(24):
+        # Simulate patient admissions and discharges
+        admissions = np.random.poisson(params['Patient_Admissions'])
+        discharges = np.random.poisson(params['Patient_Discharges'])
+
+        # Update bed availability
+        beds_df.iloc[i, 0] = params['Beds'] - admissions + discharges
+        beds_df.iloc[i, 1] = admissions
+
+        # Simulate doctor and nurse availability
+        doctors_available = params['Doctors'] - (admissions * 0.6 + params['ICU_Admissions'] * 0.4)
+        nurses_available = params['Nurses'] - (admissions * 1.4 + params['ICU_Admissions'] * 0.6)
+        doctors_df.iloc[i, 0] = doctors_available
+        doctors_df.iloc[i, 1] = admissions * 0.25
+        nurses_df.iloc[i, 0] = nurses_available
+        nurses_df.iloc[i, 1] = admissions * 0.8
+
+        # Simulate ventilator availability
+        ventilators_available = params['Ventilators'] - (admissions * 0.1 + params['ICU_Admissions'] * 0.2)
+        ventilators_df.iloc[i, 0] = ventilators_available
+        ventilators_df.iloc[i, 1] = admissions * 0.05
+
+        # Simulate ICU bed availability
+        icu_beds_available = params['ICU_Beds'] - (admissions * 0.1 + params['ICU_Admissions'] * 0.5)
+        icu_beds_df.iloc[i, 0] = icu_beds_available
+        icu_beds_df.iloc[i, 1] = admissions * 0.08
+
+        # Simulate surgical suite availability
+        surgical_suites_available = params['Surgical_Suites'] - (admissions * 0.1 + params['Surgical_Cases'] * 0.4)
+        surgical_suites_df.iloc[i, 0] = surgical_suites_available
+        surgical_suites_df.iloc[i, 1] = admissions * 0.05
+
+        # Simulate emergency room bed availability
+        emergency_room_beds_available = params['Emergency_Room_Beds'] - (admissions * 0.1 + params['Emergency_Room_Visits'] * 0.5)
+        emergency_room_beds_df.iloc[i, 0] = emergency_room_beds_available
+        emergency_room_beds_df.iloc[i, 1] = admissions * 0.1
+
+        # Simulate pharmacy, lab, and radiology staff availability
+        pharmacy_requests = params['Pharmacy_Requests'] + params['ICU_Admissions'] * 0.1 + admissions * 0.1
+        lab_requests = params['Lab_Requests'] + params['ICU_Admissions'] * 0.1 + admissions * 0.1
+        radiology_requests = params['Radiology_Requests'] + params['ICU_Admissions'] * 0.1 + admissions * 0.1
+        
+        pharmacy_staff_available = params['Pharmacy_Staff'] - pharmacy_requests * 0.1
+        lab_staff_available = params['Lab_Staff'] - lab_requests * 0.1
+        radiology_staff_available = params['Radiology_Staff'] - radiology_requests * 0.1
+        
+        pharmacy_staff_df.iloc[i, 0] = pharmacy_staff_available
+        pharmacy_staff_df.iloc[i, 1] = pharmacy_requests * 0.1
+        lab_staff_df.iloc[i, 0] = lab_staff_available
+        lab_staff_df.iloc[i, 1] = lab_requests * 0.1
+        radiology_staff_df.iloc[i, 0] = radiology_staff_available
+        radiology_staff_df.iloc[i, 1] = radiology_requests * 0.1
+
+        # Update graphs with new data
+        beds_chart.line_chart(beds_df)
+        doctors_chart.line_chart(doctors_df)
+        nurses_chart.line_chart(nurses_df)
+        ventilators_chart.line_chart(ventilators_df)
+        icu_beds_chart.line_chart(icu_beds_df)
+        surgical_suites_chart.line_chart(surgical_suites_df)
+        emergency_room_beds_chart.line_chart(emergency_room_beds_df)
+        pharmacy_staff_chart.line_chart(pharmacy_staff_df)
+        lab_staff_chart.line_chart(lab_staff_df)
+        radiology_staff_chart.line_chart(radiology_staff_df)
+
+        # Log resource usage for each patient
+        log.append(f"Hour {i}: Beds Available - {beds_df.iloc[i, 0]}, Beds Occupied - {beds_df.iloc[i, 1]}, Doctors Available - {doctors_df.iloc[i, 0]}, Doctors Busy - {doctors_df.iloc[i, 1]}, Nurses Available - {nurses_df.iloc[i, 0]}, Nurses Busy - {nurses_df.iloc[i, 1]}, Ventilators Available - {ventilators_df.iloc[i, 0]}, Ventilators In Use - {ventilators_df.iloc[i, 1]}, ICU Beds Available - {icu_beds_df.iloc[i, 0]}, ICU Beds Occupied - {icu_beds_df.iloc[i, 1]}, Surgical Suites Available - {surgical_suites_df.iloc[i, 0]}, Surgical Suites In Use - {surgical_suites_df.iloc[i, 1]}, Emergency Room Beds Available - {emergency_room_beds_df.iloc[i, 0]}, Emergency Room Beds Occupied - {emergency_room_beds_df.iloc[i, 1]}, Pharmacy Staff Available - {pharmacy_staff_df.iloc[i, 0]}, Pharmacy Staff Busy - {pharmacy_staff_df.iloc[i, 1]}, Lab Staff Available - {lab_staff_df.iloc[i, 0]}, Lab Staff Busy - {lab_staff_df.iloc[i, 1]}, Radiology Staff Available - {radiology_staff_df.iloc[i, 0]}, Radiology Staff Busy - {radiology_staff_df.iloc[i, 1]}")
+
+        # Wait for 1 second before updating the simulation
+        time.sleep(1)
+
+    # Display the resource usage log
+    st.subheader("Resource Usage Log")
+    for entry in log:
+        st.write(entry)
+