Spaces:
Sleeping
Sleeping
hackerbyhobby
commited on
app
Browse files
app.py
CHANGED
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import gradio as gr
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import joblib
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import numpy as np
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import pandas as pd
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model_path = "trained_model.pkl"
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# Define the prediction function
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def
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PhysicalHealthDays: float,
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MentalHealthDays: float,
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SleepHours: float,
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BMI: float,
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PhysicalActivities: str,
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AlcoholDrinkers: str,
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HIVTesting: str,
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RemovedTeeth: str,
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HighRiskLastYear: str,
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CovidPos: str,
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):
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try:
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alcohol_drinkers = 1 if AlcoholDrinkers.lower() == "yes" else 0
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hiv_testing = 1 if HIVTesting.lower() == "yes" else 0
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removed_teeth = 1 if RemovedTeeth.lower() == "yes" else 0
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high_risk_last_year = 1 if HighRiskLastYear.lower() == "yes" else 0
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covid_pos = 1 if CovidPos.lower() == "yes" else 0
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# Combine inputs into a numpy array for prediction
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features = np.array([
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PhysicalHealthDays, MentalHealthDays, SleepHours, BMI,
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physical_activities, alcohol_drinkers, hiv_testing,
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removed_teeth, high_risk_last_year, covid_pos
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]).reshape(1, -1)
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# Predict with the model
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prediction = model.predict(features)
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return "Heart Disease Risk" if prediction[0] == 1 else "No Risk"
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except Exception as e:
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return f"Error during prediction: {e}"
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# Define the Gradio interface
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with gr.Blocks() as app:
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gr.Markdown("# Heart Disease Prediction App")
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gr.Markdown("### Provide input values and
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SleepHours = gr.Slider(0, 24, label="Average Sleep Hours")
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BMI = gr.Slider(10, 50, label="Body Mass Index (BMI)")
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with gr.Row():
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PhysicalActivities = gr.Radio(["Yes", "No"], label="Engaged in Physical Activities?")
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AlcoholDrinkers = gr.Radio(["Yes", "No"], label="Consumes Alcohol?")
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HIVTesting = gr.Radio(["Yes", "No"], label="Tested for HIV?")
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RemovedTeeth = gr.Radio(["Yes", "No"], label="Has Removed Teeth?")
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HighRiskLastYear = gr.Radio(["Yes", "No"], label="High Risk Last Year?")
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CovidPos = gr.Radio(["Yes", "No"], label="Tested Positive for COVID-19?")
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predict_button = gr.Button("Predict")
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output = gr.Textbox(label="Prediction Result")
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# Connect prediction logic
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predict_button.click(
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fn=
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inputs=
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PhysicalHealthDays, MentalHealthDays, SleepHours, BMI,
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PhysicalActivities, AlcoholDrinkers, HIVTesting,
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RemovedTeeth, HighRiskLastYear, CovidPos
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],
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outputs=output,
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)
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"""
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Webapp Front End
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"""
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import gradio as gr
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import joblib
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import pandas as pd
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MODEL_PATH = "Random_Foresttest_model.pkl"
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try:
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rf_model = joblib.load(MODEL_PATH)
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except FileNotFoundError as e:
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raise FileNotFoundError(
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f"Model file not found at {MODEL_PATH}. Please check the path."
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) from e
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# Define the prediction function
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def predict_with_model(State: float, Sex: float, GeneralHealth: float, PhysicalHealthDays: float, MentalHealthDays: float, LastCheckupTime: float, PhysicalActivities: float, SleepHours: float, HadStroke: float, HadArthritis: float, HadDiabetes: float, SmokerStatus: float, ECigaretteUsage: float, RaceEthnicityCategory: float, AgeCategory: float, HeightInMeters: float, WeightInKilograms: float, BMI: float, AlcoholDrinkers: float, HighRiskLastYear: float):
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try:
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# Prepare input as a DataFrame
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input_data = pd.DataFrame([[State, Sex, GeneralHealth, PhysicalHealthDays, MentalHealthDays, LastCheckupTime, PhysicalActivities, SleepHours, HadStroke, HadArthritis, HadDiabetes, SmokerStatus, ECigaretteUsage, RaceEthnicityCategory, AgeCategory, HeightInMeters, WeightInKilograms, BMI, AlcoholDrinkers, HighRiskLastYear]], columns=['State', 'Sex', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays', 'LastCheckupTime', 'PhysicalActivities', 'SleepHours', 'HadStroke', 'HadArthritis', 'HadDiabetes', 'SmokerStatus', 'ECigaretteUsage', 'RaceEthnicityCategory', 'AgeCategory', 'HeightInMeters', 'WeightInKilograms', 'BMI', 'AlcoholDrinkers', 'HighRiskLastYear'])
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prediction = rf_model.predict(input_data)
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return "Heart Disease Risk" if prediction[0] == 1 else "No Risk"
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except Exception as e:
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return f"Error during prediction: {e}"
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# Define the Gradio interface
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with gr.Blocks() as app:
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gr.Markdown("# Heart Disease Prediction App")
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gr.Markdown("### Provide input values for the features below and get a prediction.")
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input_components = []
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for feature in ['State', 'Sex', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays', 'LastCheckupTime', 'PhysicalActivities', 'SleepHours', 'HadStroke', 'HadArthritis', 'HadDiabetes', 'SmokerStatus', 'ECigaretteUsage', 'RaceEthnicityCategory', 'AgeCategory', 'HeightInMeters', 'WeightInKilograms', 'BMI', 'AlcoholDrinkers', 'HighRiskLastYear']:
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input_components.append(gr.Slider(0, 100, step=1, label=feature))
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predict_button = gr.Button("Predict")
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output = gr.Textbox(label="Prediction Result")
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# Connect prediction logic
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predict_button.click(
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fn=predict_with_model,
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inputs=input_components,
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outputs=output,
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)
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