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Obezite.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a295b837d2204265fa7ebf9cef1c3347335d52b4259f5c687eb820eb92981721
+size 677372

app.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import pickle
+
+# Load the model and scaler
+model = pickle.load(open('Obezite.pkl', 'rb'))
+scaler = pickle.load(open('scaler.pkl', 'rb'))
+
+st.title("Obesity Prediction")
+
+# Input fields
+gender = st.selectbox("Gender", ("Male", "Female"))
+age = st.number_input("Age", min_value=0)
+height = st.number_input("Height (m)", min_value=0.0)
+weight = st.number_input("Weight (kg)", min_value=0.0)
+cholesterol = st.number_input("Cholesterol Level", min_value=0)
+blood_pressure = st.number_input("Blood Pressure", min_value=0)
+smoking = st.selectbox("Smoking Status", ("Non-Smoker", "Smoker"))
+alcohol_consumption = st.selectbox("Alcohol Consumption", ("No", "Yes"))
+physical_activity = st.selectbox("Physical Activity Level", ("Low", "Moderate", "High"))
+diet_quality = st.selectbox("Diet Quality", ("Poor", "Average", "Good"))
+family_history_with_overweight = st.selectbox("Family History of Obesity", ("No", "Yes"))
+FAVC = st.selectbox("Frequency of Eating Fatty Foods", ("No", "Yes"))
+FCVC = st.number_input("Frequency of Vegetables Consumption", min_value=0)
+NCP = st.number_input("Number of Main Meals", min_value=1)
+CAEC = st.selectbox("Consumption of Food Between Meals", ("No", "Yes"))
+CH2O = st.number_input("Water Consumption (L)", min_value=0.0)
+SCC = st.number_input("Consumption of Sugar-Sweetened Beverages", min_value=0)
+FAF = st.number_input("Physical Activity Level (1-5)", min_value=1, max_value=5)
+TUE = st.number_input("Time Spent on Physical Activity", min_value=0)
+CALC = st.selectbox("Caloric Intake", ("Low", "Moderate", "High"))
+MTRANS = st.selectbox("Transportation Type", ("Walking", "Public Transport", "Private Vehicle"))
+
+# Prepare input data for prediction
+input_data = pd.DataFrame({
+    'Gender': [gender],
+    'Age': [age],
+    'Height': [height],
+    'Weight': [weight],
+    'family_history_with_overweight': [1 if family_history_with_overweight == "Yes" else 0],
+    'FAVC': [1 if FAVC == "Yes" else 0],
+    'FCVC': [FCVC],
+    'NCP': [NCP],
+    'CAEC': [1 if CAEC == "Yes" else 0],
+    'SMOKE': [1 if smoking == "Smoker" else 0],
+    'CH2O': [CH2O],
+    'SCC': [SCC],
+    'FAF': [FAF],
+    'TUE': [TUE],
+    'CALC': [CALC],
+    'MTRANS': [MTRANS],
+})
+
+# Convert categorical variables into dummy/indicator variables
+input_data = pd.get_dummies(input_data, drop_first=True)
+
+# Ensure the same features are present as in the scaler
+input_data = input_data.reindex(columns=scaler.get_feature_names_out(), fill_value=0)
+
+# Prediction button
+if st.button('Predict'):
+    input_scaled = scaler.transform(input_data)  # Scale the input data
+    prediction = model.predict(input_scaled)  # Get the prediction
+    predicted_class = np.argmax(prediction, axis=1)  # Get the predicted class
+    st.write(f"Predicted class: {predicted_class[0]}")

requirements.txt

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+streamlit
+scikit-learn
+pandas
+tensorflow

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e6b416be148333fb8871df66f8abee5fb8b96b3121f4704e8395214070ba31e8
+size 1741