Delete app.py

app.py

@@ -1,129 +0,0 @@
-import streamlit as st
-import joblib
-
-model_path = 'Best_model.joblib'
-loaded_model = joblib.load(model_path)
-
-
-# Preprocess input function
-def preprocess_input(input_data):
-    age = input_data['age']
-    bmi = input_data.get('bmi', None)
-    height = input_data.get('height', None)
-    weight = input_data.get('weight', None)
-    children = input_data['children']
-
-    # Convert height to meters based on the selected unit
-    height_unit = input_data.get('height_unit', 'meters')
-    if height is not None and height_unit != 'meters':
-        if height_unit == 'centimeters':
-            height /= 100
-        elif height_unit == 'feet':
-            height *= 0.3048  # 1 foot = 0.3048 meters
-
-    # Calculate BMI if height and weight are provided and height is not zero
-    if height is not None and height != 0 and weight is not None:
-        bmi = weight / (height ** 2)
-
-    # Convert sex to binary representation
-    sex_0 = 1 if input_data['sex'] == 'female' else 0
-    sex_1 = 1 - sex_0
-
-    # Convert smoker to binary representation
-    smoker_0 = 1 if input_data['smoker'] == 'no' else 0
-    smoker_1 = 1 - smoker_0
-
-    # Map region name to numerical representation
-    region_mapping = {'southeast': 1, 'southwest': 2, 'northwest': 3, 'northeast': 4}
-    region = region_mapping.get(input_data['region'], 0)
-
-    # Create binary representations for each region
-    region_1 = 1 if region == 1 else 0
-    region_2 = 1 if region == 2 else 0
-    region_3 = 1 if region == 3 else 0
-    region_4 = 1 if region == 4 else 0
-
-    # Create the formatted input list with 11 features
-    formatted_input = [age, bmi, children, sex_0, sex_1, region_1, region_2, region_3, region_4, smoker_0, smoker_1]
-
-    return formatted_input
-
-
-# Input page
-def input_page():
-    st.title('HealthInsure Claim Amount Predictor')
-    st.write('Please fill in the following details:')
-    
-    age = None
-    height = None
-    weight = None
-    
-    age_warning = ''
-    height_warning = ''
-    weight_warning = ''
-    
-    age = st.number_input('Age', min_value=0, step=1, value=age)
-    if age == 0:
-        age_warning = 'Please enter correct age.'
-        st.warning(age_warning)
-    sex = st.radio('Sex', ('male', 'female'))
-
-    # Side-by-side input for height unit and height
-    col1, col2 = st.columns(2)
-    with col1:
-        height_unit = st.selectbox('Height Unit', ('meters', 'centimeters', 'feet'))
-    with col2:
-        height = st.number_input('Height', min_value=0.0, step=0.01, value=height)
-    if height == 0:
-        height_warning = 'Please enter correct height.'
-        st.warning(height_warning)
-    weight = st.number_input('Weight (in kg)', min_value=0.0, step=0.1, value=weight)
-    if weight == 0:
-        weight_warning = 'Please enter correct weight.'
-        st.warning(weight_warning)
-
-    # Calculate BMI immediately after entering height and weight if height is not zero
-    bmi = None
-    if height is not None and height != 0.0 and weight is not None:
-        # Convert height based on selected height unit
-        if height_unit != 'meters':
-            if height_unit == 'centimeters':
-                height /= 100
-            elif height_unit == 'feet':
-                height *= 0.3048  # 1 foot = 0.3048 meters
-
-        # Calculate BMI
-        bmi = weight / (height ** 2)
-        st.write(f'BMI: {bmi:.2f}')
-
-    children = st.number_input('Number of Children', min_value=0, step=1)
-    smoker = st.selectbox('Smoker', ('yes', 'no'))
-    region = st.selectbox('Region', ('southeast', 'southwest', 'northwest', 'northeast'))
-
-    if st.button('Predict'):
-        if age_warning or height_warning or weight_warning:
-            st.error('Please correct the following input errors:')
-            if age_warning:
-                st.error(age_warning)
-            if height_warning:
-                st.error(height_warning)
-            if weight_warning:
-                st.error(weight_warning)
-        else:
-            input_data = {'age': age, 'sex': sex, 'height': height, 'weight': weight, 'children': children,
-                          'smoker': smoker, 'region': region, 'bmi': bmi, 'height_unit': height_unit}
-            processed_input = preprocess_input(input_data)
-            charges = loaded_model.predict([processed_input])[0]
-            st.write('## Estimated Claim Amount')
-            st.write(f'Estimated Claim Amount: {charges:.2f}', unsafe_allow_html=True)
-            st.write('The following value is estimated based on historical data and predictive modeling techniques and may not represent the exact amount.')
-
-
-# Main function
-def main():
-    input_page()
-
-
-if __name__ == '__main__':
-    main()
-