add gradio app

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+
+# Load the saved model
+joblib_file = "logistic_model_pipeline.pkl"
+loaded_pipeline = joblib.load(joblib_file)
+
+# Define a function to make predictions
+def predict_heart_disease(age, sex_male, cigs_per_day, tot_chol, sys_bp, glucose):
+    # Create a DataFrame for the input
+    input_data = pd.DataFrame({
+        'age': [age],
+        'Sex_male': [sex_male],
+        'cigsPerDay': [cigs_per_day],
+        'totChol': [tot_chol],
+        'sysBP': [sys_bp],
+        'glucose': [glucose]
+    })
+    
+    # Make prediction using the loaded model
+    prob = loaded_pipeline.predict_proba(input_data)[:, 1][0]  # Probability of having heart disease
+    return f"The probability of having heart disease is: {prob * 100:.2f}%"
+
+# Create the Gradio interface
+with gr.Blocks() as interface:
+    gr.Markdown("## Heart Disease Risk Prediction")
+    gr.Markdown("Enter your details below to check the probability of having heart disease.")
+
+    # Age input with helper text
+    age = gr.Slider(label="Age", minimum=20, maximum=100, value=50, step=1, info="Enter your age (20-100).")
+
+    # Sex input with helper text
+    sex_male = gr.Radio(label="Sex", choices=[1, 0], value=1, info="Select your sex: 1 for Male, 0 for Female.")
+
+    # Cigarettes per day input with helper text
+    cigs_per_day = gr.Slider(label="Cigarettes per Day", minimum=0, maximum=60, value=10, step=1, info="Enter the number of cigarettes you smoke per day (0-60).")
+
+    # Total cholesterol input with helper text
+    tot_chol = gr.Slider(label="Total Cholesterol (mg/dL)", minimum=100, maximum=400, value=200, step=1, info="Enter your total cholesterol level (100-400 mg/dL).")
+
+    # Systolic blood pressure input with helper text
+    sys_bp = gr.Slider(label="Systolic Blood Pressure (mmHg)", minimum=90, maximum=200, value=120, step=1, info="Enter your systolic blood pressure (90-200 mmHg).")
+
+    # Glucose input with helper text
+    glucose = gr.Slider(label="Glucose Level (mg/dL)", minimum=50, maximum=300, value=100, step=1, info="Enter your glucose level (50-300 mg/dL).")
+
+    # Predict button and output
+    predict_btn = gr.Button("Predict")
+    output = gr.Textbox(label="Prediction")
+
+    # Set up the prediction process
+    predict_btn.click(predict_heart_disease, inputs=[age, sex_male, cigs_per_day, tot_chol, sys_bp, glucose], outputs=output)
+
+# Launch the interface
+interface.launch(share=True)

logistic_model_pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:51eaff70710679311a8f950e382987874caa650264f9d05041a089d0a9852f02
+size 1886

requirement.txt

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+pandas
+numpy
+statsmodels
+scipy
+matplotlib
+scikit-learn
+imbalanced-learn
+gradio
+joblib