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import numpy as np |
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import pickle |
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import streamlit as st |
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models = { |
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"Logistic Regression": "LogisticRegression_model.pkl", |
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"Decision Tree Classifier": "DecisionTreeClassifier_model.pkl", |
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"Random Forest Classifier": "RandomForestClassifier_model.pkl", |
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"SVC": "SVC_model.pkl" |
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} |
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def load_model(model_name): |
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model_path = models.get(model_name) |
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if model_path: |
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return pickle.load(open(model_path, 'rb')) |
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return None |
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def diabetes_prediction(input_data, model): |
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prediction = model.predict([input_data]) |
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return 'The person is diabetic' if prediction[0] else 'The person is not diabetic' |
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def main(): |
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st.title('MultiModel Diabetes Predictor') |
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selected_model_name = st.selectbox("Choose Prediction Method (Default: Logistic Regression)", list(models.keys())) |
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loaded_model = load_model(selected_model_name) |
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if loaded_model is None: |
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st.error("Model not found!") |
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return |
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Pregnancies = st.text_input('Number of Pregnancies') |
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Glucose = st.text_input('Glucose Level') |
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BloodPressure = st.text_input('Blood Pressure Value') |
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SkinThickness = st.text_input('Skin Thickness Value') |
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Insulin = st.text_input('Insulin Level') |
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BMI = st.text_input('BMI Value') |
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DiabetesPedigreeFunction = st.text_input('Diabetes Pedigree Function Value') |
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Age = st.text_input('Age of the Person') |
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diagnosis = '' |
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if st.button('Diabetes Test Result'): |
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try: |
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input_data = np.array([ |
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float(Pregnancies), float(Glucose), float(BloodPressure), |
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float(SkinThickness), float(Insulin), float(BMI), |
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float(DiabetesPedigreeFunction), float(Age) |
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]) |
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diagnosis = diabetes_prediction(input_data, loaded_model) |
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except ValueError: |
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diagnosis = "Invalid input. Please enter numeric values for all fields." |
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st.success(diagnosis) |
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st.markdown('### Demo - Test with Dummy Entries') |
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default_values = { |
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"Number of Pregnancies": 5, |
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"Glucose Level": 130, |
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"Blood Pressure Value": 80, |
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"Skin Thickness Value": 30, |
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"Insulin Level": 100, |
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"BMI Value": 35, |
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"Diabetes Pedigree Function Value": 0.5, |
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"Age of the Person": 40 |
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} |
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if st.button('Run Demo'): |
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demo_input_data = np.array([ |
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default_values["Number of Pregnancies"], default_values["Glucose Level"], |
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default_values["Blood Pressure Value"], default_values["Skin Thickness Value"], |
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default_values["Insulin Level"], default_values["BMI Value"], |
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default_values["Diabetes Pedigree Function Value"], default_values["Age of the Person"] |
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]) |
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demo_diagnosis = diabetes_prediction(demo_input_data, loaded_model) |
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st.success(f'Demo Result: {demo_diagnosis} with inputs: {default_values}') |
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if __name__ == '__main__': |
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main() |
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