Delete .ipynb_checkpoints

.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 5
-}

.ipynb_checkpoints/gradio_hearttack_app-checkpoint.py

@@ -1,83 +0,0 @@
-import gradio as gr
-import xgboost as xgb
-import joblib
-import numpy as np
-from sklearn.preprocessing import StandardScaler
-import pandas as pd
-import shap
-import matplotlib.pyplot as plt
-
-# Load the model and the scaler
-model = joblib.load('best_XGB.pkl')
-scaler = joblib.load('scaler.pkl')  # Ensure the scaler is saved and loaded with the same scikit-learn version
-cutoff = 0.42  # Custom cutoff probability
-
-# Use TreeExplainer for XGBoost models
-explainer = shap.TreeExplainer(model)
-
-# Define the prediction function with preprocessing, scaling, and SHAP analysis
-def predict_heart_attack(Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose):
-    # Define feature names in the same order as the training data
-    feature_names = ['Gender', 'age', 'cigsPerDay', 'BPMeds', 'prevalentHyp', 'diabetes', 'totChol', 'sysBP', 'diaBP', 'BMI', 'heartRate', 'glucose']
-    
-    # Create a DataFrame with the correct feature names for prediction
-    features = pd.DataFrame([[Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose]], columns=feature_names)
-    
-    # Standardize the features (scaling)
-    scaled_features = scaler.transform(features)
-    
-    # Predict probabilities
-    proba = model.predict_proba(scaled_features)[:, 1]  # Probability of class 1 (heart attack)
-    
-    # Apply custom cutoff
-    if proba[0] >= cutoff:
-        prediction_class = 1
-    else:
-        prediction_class = 0
-    
-    # Generate SHAP values for the prediction using the explainer
-    shap_values = explainer(features)
-
-    # Plot SHAP values
-    plt.figure(figsize=(8, 6))
-    shap.waterfall_plot(shap_values[0])  # Using the SHAP Explanation object
-    plt.savefig('shap_plot.png')  # Save SHAP plot to a file
-    
-    result = f"Predicted Probability: {proba[0]*100:.2f}%. Predicted Class with cutoff {cutoff}: {prediction_class}"
-    
-    return result, 'shap_plot.png'  # Return the prediction and SHAP plot
-
-# Create the Gradio interface with preprocessing, prediction, and SHAP visualization
-with gr.Blocks() as app:
-    with gr.Row():
-        with gr.Column():
-            Gender = gr.Radio([0, 1], label="Gender (0=Female, 1=Male)")
-            cigsPerDay = gr.Slider(0, 40, step=1, label="Cigarettes per Day")
-            prevalentHyp = gr.Radio([0, 1], label="Prevalent Hypertension (0=No, 1=Yes)")
-            totChol = gr.Slider(100, 400, step=1, label="Total Cholesterol in mg/dl")
-            diaBP = gr.Slider(60, 120, step=1, label="Diastolic/Lower BP")
-            heartRate = gr.Slider(50, 120, step=1, label="Heart Rate")
-        
-        with gr.Column():
-            age = gr.Slider(20, 80, step=1, label="Age (years)")
-            BPMeds = gr.Radio([0, 1], label="On BP Medications (0=No, 1=Yes)")
-            diabetes = gr.Radio([0, 1], label="Diabetes (0=No, 1=Yes)")
-            sysBP = gr.Slider(90, 200, step=1, label="Systolic BP/Higher BP")
-            BMI = gr.Slider(15, 40, step=0.1, label="Body Mass Index (BMI) in kg/m2")
-            glucose = gr.Slider(50, 250, step=1, label="Fasting Glucose Level")
-    
-    # Center-aligned prediction output
-    with gr.Row():
-        gr.HTML("<div style='text-align: center; width: 100%'>Heart Attack Prediction</div>")
-    
-    with gr.Row():
-        prediction_output = gr.Textbox(label="", interactive=False, elem_id="prediction_output")
-    
-    with gr.Row():
-        shap_plot_output = gr.Image(label="SHAP Analysis")
-
-    # Link inputs and prediction output
-    submit_btn = gr.Button("Submit")
-    submit_btn.click(fn=predict_heart_attack, inputs=[Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose], outputs=[prediction_output, shap_plot_output])
-
-app.launch(share = True)

.ipynb_checkpoints/gradio_hearttack_app_old-checkpoint.py

@@ -1,83 +0,0 @@
-import gradio as gr
-import xgboost as xgb
-import joblib
-import numpy as np
-from sklearn.preprocessing import StandardScaler
-import pandas as pd
-import shap
-import matplotlib.pyplot as plt
-
-# Load the model and the scaler
-model = joblib.load('best_XGB.pkl')
-scaler = joblib.load('scaler.pkl')  # Load the scaler that was saved during training
-cutoff = 0.42  # Custom cutoff probability
-
-# Load SHAP explainer based on your XGBoost model
-explainer = shap.Explainer(model)
-
-# Define the prediction function with preprocessing, scaling, and SHAP analysis
-def predict_heart_attack(Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose):
-    # Define feature names in the same order as the training data
-    feature_names = ['Gender', 'age', 'cigsPerDay', 'BPMeds', 'prevalentHyp', 'diabetes', 'totChol', 'sysBP', 'diaBP', 'BMI', 'heartRate', 'glucose']
-    
-    # Create a DataFrame with the correct feature names for prediction
-    features = pd.DataFrame([[Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose]], columns=feature_names)
-    
-    # Standardize the features (scaling)
-    scaled_features = scaler.transform(features)
-    
-    # Predict probabilities
-    proba = model.predict_proba(scaled_features)[:, 1]  # Probability of class 1 (heart attack)
-    
-    # Apply custom cutoff
-    if proba[0] >= cutoff:
-        prediction_class = 1
-    else:
-        prediction_class = 0
-    
-    # Generate SHAP values for the prediction
-    shap_values = explainer(scaled_features)
-    
-    # Plot SHAP values
-    plt.figure(figsize=(8, 6))
-    shap.waterfall_plot(shap_values[0])
-    plt.savefig('shap_plot.png')  # Save SHAP plot to a file
-    
-    result = f"Predicted Probability: {proba[0]*100:.2f}%. Predicted Class with cutoff {cutoff}: {prediction_class}"
-    
-    return result, 'shap_plot.png'  # Return the prediction and SHAP plot
-
-# Create the Gradio interface with preprocessing, prediction, and SHAP visualization
-with gr.Blocks() as app:
-    with gr.Row():
-        with gr.Column():
-            Gender = gr.Radio([0, 1], label="Gender (0=Female, 1=Male)")
-            cigsPerDay = gr.Slider(0, 40, step=1, label="Cigarettes per Day")
-            prevalentHyp = gr.Radio([0, 1], label="Prevalent Hypertension (0=No, 1=Yes)")
-            totChol = gr.Slider(100, 400, step=1, label="Total Cholesterol in mg/dl")
-            diaBP = gr.Slider(60, 120, step=1, label="Diastolic/Lower BP")
-            heartRate = gr.Slider(50, 120, step=1, label="Heart Rate")
-        
-        with gr.Column():
-            age = gr.Slider(20, 80, step=1, label="Age (years)")
-            BPMeds = gr.Radio([0, 1], label="On BP Medications (0=No, 1=Yes)")
-            diabetes = gr.Radio([0, 1], label="Diabetes (0=No, 1=Yes)")
-            sysBP = gr.Slider(90, 200, step=1, label="Systolic BP/Higher BP")
-            BMI = gr.Slider(15, 40, step=0.1, label="Body Mass Index  (weight in kg/ height in meter squared)(BMI) in kg/m2")
-            glucose = gr.Slider(50, 250, step=1, label="Fasting Glucose Level")
-    
-    # Center-aligned prediction output
-    with gr.Row():
-        gr.HTML("<div style='text-align: center; width: 100%'>Heart Attack Prediction</div>")
-    
-    with gr.Row():
-        prediction_output = gr.Textbox(label="", interactive=False, elem_id="prediction_output")
-    
-    with gr.Row():
-        shap_plot_output = gr.Image(label="SHAP Analysis")
-
-    # Link inputs and prediction output
-    submit_btn = gr.Button("Submit")
-    submit_btn.click(fn=predict_heart_attack, inputs=[Gender, age, cigsPerDay, BPMeds, prevalentHyp, diabetes, totChol, sysBP, diaBP, BMI, heartRate, glucose], outputs=[prediction_output, shap_plot_output])
-
-app.launch()

.ipynb_checkpoints/requirements-checkpoint.txt

@@ -1,7 +0,0 @@
-gradio==5.1.0
-gradio_client==1.4.0
-joblib==1.4.2
-numpy==1.26.4
-pandas==2.2.2
-scikit-learn==1.4.2
-xgboost==2.0.3