sleep disorder analysis added

Prediction Models/sleep_disorder_predictor/model.py

@@ -0,0 +1,43 @@
+import streamlit as st
+import pickle
+import pandas as pd  # Import pandas to handle DataFrames
+import numpy as np
+import warnings
+warnings.filterwarnings("ignore")
+
+# Load the model and the scaler
+model_path = 'Prediction Models/sleep_disorder_predictor/saved_models/Model_Prediction.sav'
+preprocessor_path = 'Prediction Models/sleep_disorder_predictor/saved_models/preprocessor.sav'
+
+# Load the pre-trained model and scaler using pickle
+loaded_model = pickle.load(open(model_path, 'rb'))
+preprocessor = pickle.load(open(preprocessor_path, 'rb'))
+
+# Define the prediction function
+def disease_get_prediction(Age, Sleep_Duration,  
+                            Heart_Rate, Daily_Steps, 
+                            Systolic, Diastolic, Occupation, Quality_of_Sleep, Gender, 
+                            Physical_Activity_Level, Stress_Level, BMI_Category):
+    # Create a DataFrame with the features using correct column names
+    features = pd.DataFrame({
+        'Age': [int(Age)],
+        'Sleep Duration': [float(Sleep_Duration)],  # Changed to match expected name
+        'Heart Rate': [int(Heart_Rate)],            # Changed to match expected name
+        'Daily Steps': [int(Daily_Steps)],          # Changed to match expected name
+        'Systolic': [float(Systolic)],
+        'Diastolic': [float(Diastolic)],
+        'Occupation': [Occupation],
+        'Quality of Sleep': [int(Quality_of_Sleep)], # Changed to match expected name
+        'Gender': [Gender],
+        'Physical Activity Level': [int(Physical_Activity_Level)], # Changed to match expected name
+        'Stress Level': [int(Stress_Level)],       # Changed to match expected name
+        'BMI Category': [BMI_Category]               # Changed to match expected name
+    })
+
+    # Apply the preprocessor (make sure it expects a DataFrame)
+    preprocessed_data = preprocessor.transform(features)
+
+    # Make prediction
+    prediction = loaded_model.predict(preprocessed_data)
+
+    return prediction