revamed a lot

app.py

@@ -1,86 +1,49 @@
+
 import gradio as gr
 import joblib
-import pandas as pd
 import json
+import numpy as np
 
 # Load the trained model
-model = joblib.load("optimized_heart_failure_model.pkl")
+model = joblib.load('trained_model_selected.pkl')
 
-# Load feature names used during training
-with open("feature_names.json", "r") as f:
+# Load feature names
+with open('selected_features.json', 'r') as f:
     feature_names = json.load(f)
 
-# Define the prediction function
-def predict_heart_failure(age_category, alcohol_drinkers, chest_scan, covid_positive, physical_health_days, mental_health_days, sleep_hours, bmi):
-    # Initialize all features to 0
-    input_data = {feature: 0 for feature in feature_names}
-
-    # Handle Age Category
-    input_data[f"AgeCategory_{age_category}"] = 1  # Set the selected age category to 1
-
-    # Handle categorical inputs
-    input_data[f"AlcoholDrinkers_{alcohol_drinkers}"] = 1  # Alcohol Drinkers
-    input_data[f"ChestScan_{chest_scan}"] = 1  # Chest Scan
-    input_data[f"CovidPos_{covid_positive}"] = 1  # Covid Positive
-
-    # Handle numeric inputs
-    input_data["PhysicalHealthDays"] = physical_health_days
-    input_data["MentalHealthDays"] = mental_health_days
-    input_data["SleepHours"] = sleep_hours
-    input_data["BMI"] = bmi
-
-    # Create a DataFrame with the required feature names
-    input_df = pd.DataFrame([input_data])
-
-    # Configure pandas to display all columns
-    pd.set_option('display.max_columns', None)  # Display all columns
-    pd.set_option('display.width', None)       # Disable line-wrapping for wide DataFrames
-
-
-    # Ensure all required features are present
-    for feature in feature_names:
-        if feature not in input_df.columns:
-            input_df[feature] = 0  # Fill missing features with default value (e.g., 0)
-
-    print("Inputs received in the app:")
-    print(input_df)
-    
-    # Ensure no extra features are included
-    input_df = input_df[feature_names]
-
-    # Make a prediction
-    prediction = model.predict(input_df)[0]
-    print(f"Prediction: {prediction}")
-    return "High likelihood of heart failure" if prediction == 1 else "Low likelihood of heart failure"
-
-
-
-
-
-# Define Gradio inputs
-gradio_inputs = [
-    gr.Radio(
-        ["18-24", "25-34", "35-44", "45-54", "55-64", "65-74", "75-79", "80 or older"],
-        label="Age Category"
-    ),
-    gr.Radio(["Yes", "No"], label="Alcohol Drinkers"),
-    gr.Radio(["Yes", "No"], label="Chest Scan"),
-    gr.Radio(["Yes", "No"], label="COVID Positive"),
-    gr.Slider(0, 30, step=1, label="Physical Health Days"),
-    gr.Slider(0, 30, step=1, label="Mental Health Days"),
-    gr.Slider(0, 12, step=0.5, label="Sleep Hours"),
-    gr.Slider(10, 50, step=0.1, label="BMI")
+# Define prediction function
+def predict_heart_failure(*args):
+    """
+    Predict heart failure likelihood based on input features.
+
+    Args:
+        *args: Input values for each feature.
+
+    Returns:
+        str: Prediction result.
+        str: Prediction probability as a percentage.
+    """
+    input_data = np.array(args).reshape(1, -1)
+    prediction = model.predict(input_data)
+    probability = model.predict_proba(input_data)
+    result = "Likely" if prediction[0] == 1 else "Unlikely"
+    return result, f"{probability[0][1] * 100:.2f}%"
+
+# Create Gradio interface
+inputs = [gr.inputs.Number(label=feature) for feature in feature_names]
+outputs = [
+    gr.outputs.Textbox(label="Prediction"),
+    gr.outputs.Textbox(label="Probability (%)")
 ]
 
-# Set up the Gradio interface
-interface = gr.Interface(
+app = gr.Interface(
     fn=predict_heart_failure,
-    inputs=gradio_inputs,
-    outputs="text",
-    title="Heart Failure Risk Prediction",
-    description="This app predicts the likelihood of heart failure based on health and lifestyle inputs."
+    inputs=inputs,
+    outputs=outputs,
+    title="Heart Failure Prediction",
+    description="Enter the values for the features to predict the likelihood of heart failure."
 )
 
 # Launch the app
 if __name__ == "__main__":
-    interface.launch()
+    app.launch()

feature_names.json

@@ -1 +0,0 @@
-["PhysicalHealthDays", "MentalHealthDays", "SleepHours", "HeightInMeters", "WeightInKilograms", "BMI", "State_Alaska", "State_Arizona", "State_Arkansas", "State_California", "State_Colorado", "State_Connecticut", "State_Delaware", "State_District of Columbia", "State_Florida", "State_Georgia", "State_Guam", "State_Hawaii", "State_Idaho", "State_Illinois", "State_Indiana", "State_Iowa", "State_Kansas", "State_Kentucky", "State_Louisiana", "State_Maine", "State_Maryland", "State_Massachusetts", "State_Michigan", "State_Minnesota", "State_Mississippi", "State_Missouri", "State_Montana", "State_Nebraska", "State_Nevada", "State_New Hampshire", "State_New Jersey", "State_New Mexico", "State_New York", "State_North Carolina", "State_North Dakota", "State_Ohio", "State_Oklahoma", "State_Oregon", "State_Pennsylvania", "State_Puerto Rico", "State_Rhode Island", "State_South Carolina", "State_South Dakota", "State_Tennessee", "State_Texas", "State_Utah", "State_Vermont", "State_Virgin Islands", "State_Virginia", "State_Washington", "State_West Virginia", "State_Wisconsin", "State_Wyoming", "Sex_Male", "GeneralHealth_Fair", "GeneralHealth_Good", "GeneralHealth_Poor", "GeneralHealth_Very good", "LastCheckupTime_Within past 2 years (1 year but less than 2 years ago)", "LastCheckupTime_Within past 5 years (2 years but less than 5 years ago)", "LastCheckupTime_Within past year (anytime less than 12 months ago)", "PhysicalActivities_Yes", "RemovedTeeth_6 or more, but not all", "RemovedTeeth_All", "RemovedTeeth_None of them", "HadStroke_Yes", "HadAsthma_Yes", "HadSkinCancer_Yes", "HadCOPD_Yes", "HadDepressiveDisorder_Yes", "HadKidneyDisease_Yes", "HadArthritis_Yes", "HadDiabetes_No, pre-diabetes or borderline diabetes", "HadDiabetes_Yes", "HadDiabetes_Yes, but only during pregnancy (female)", "DeafOrHardOfHearing_Yes", "BlindOrVisionDifficulty_Yes", "DifficultyConcentrating_Yes", "DifficultyWalking_Yes", "DifficultyDressingBathing_Yes", "DifficultyErrands_Yes", "SmokerStatus_Current smoker - now smokes some days", "SmokerStatus_Former smoker", "SmokerStatus_Never smoked", "ECigaretteUsage_Not at all (right now)", "ECigaretteUsage_Use them every day", "ECigaretteUsage_Use them some days", "ChestScan_Yes", "RaceEthnicityCategory_Hispanic", "RaceEthnicityCategory_Multiracial, Non-Hispanic", "RaceEthnicityCategory_Other race only, Non-Hispanic", "RaceEthnicityCategory_White only, Non-Hispanic", "AgeCategory_Age 25 to 29", "AgeCategory_Age 30 to 34", "AgeCategory_Age 35 to 39", "AgeCategory_Age 40 to 44", "AgeCategory_Age 45 to 49", "AgeCategory_Age 50 to 54", "AgeCategory_Age 55 to 59", "AgeCategory_Age 60 to 64", "AgeCategory_Age 65 to 69", "AgeCategory_Age 70 to 74", "AgeCategory_Age 75 to 79", "AgeCategory_Age 80 or older", "AlcoholDrinkers_Yes", "HIVTesting_Yes", "FluVaxLast12_Yes", "PneumoVaxEver_Yes", "TetanusLast10Tdap_Yes, received Tdap", "TetanusLast10Tdap_Yes, received tetanus shot but not sure what type", "TetanusLast10Tdap_Yes, received tetanus shot, but not Tdap", "HighRiskLastYear_Yes", "CovidPos_Tested positive using home test without a health professional", "CovidPos_Yes"]

selected_features.json

@@ -0,0 +1 @@
+["HadAngina_Yes", "HadHeartAttack_Yes", "ChestScan_Yes", "BMI", "WeightInKilograms", "HadStroke_Yes", "DifficultyWalking_Yes", "HeightInMeters", "HadDiabetes_Yes", "PhysicalHealthDays", "SleepHours", "PneumoVaxEver_Yes", "HadArthritis_Yes"]

optimized_heart_failure_model.pkl → trained_model.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b38947d94cf77ec9b86bc963fcb91cb396c025cecfa499e7f576addf67b411a1
-size 665007513
+oid sha256:671e17b4726b0253e73d9a3b6af013302cdd2c32086e9b67357d44f145af01cd
+size 7804185