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app.py

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+import streamlit as st
+import pickle
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+
+# Veri ön işleme nesnesini dosyadan yükleme
+with open('preprocessor.pkl', 'rb') as f:
+    preprocessor = pickle.load(f)
+
+# Modeli dosyadan yükleme
+with open('obesity.pkl', 'rb') as f:
+    model = pickle.load(f)
+
+
+
+# Uygulama başlığı ve açıklama
+st.title("Obesity Risk Classifier")
+st.write("Calculate your obesity risk here! Because who doesn't want to procrastinate starting a diet?")
+
+# Kullanıcıdan veriler almak için input alanları
+st.header("Please fill in the following information:")
+
+# Numeric sütunlar
+Age = st.number_input("Age", min_value=0, max_value=100, value=25, step=1)
+Height = st.number_input("Height (cm)", min_value=100, max_value=250, value=170, step=1)
+Weight = st.number_input("Weight (kg)", min_value=30, max_value=200, value=70, step=1)
+FCVC = st.number_input("Frequency of consumption of vegetables", min_value=0.0, max_value=3.0, value=1.0, step=0.5)
+NCP = st.number_input("Number of main meals", min_value=0, max_value=6, value=3, step=1)
+CH2O = st.number_input("Daily water consumption", min_value=0.0, max_value=3.0, value=1.0, step=0.5)
+FAF = st.number_input("Physical activity frequency", min_value=0.0, max_value=3.0, value=1.0, step=0.5)
+TUE = st.number_input("Time spent sitting (hours)", min_value=0.0, max_value=16.0, value=8.0, step=1.0)
+
+# Categoric sütunlar
+Gender = st.selectbox("Gender", ("Female","Male"))
+family_history_with_overweight = st.selectbox("Family history with overweight", ("yes", "no"))
+FAVC = st.selectbox("Frequent consumption of high caloric food", ("yes", "no"))
+CAEC = st.selectbox("Regular consumption of vegetables", ("Sometimes", "Frequently","Always","no"))
+SMOKE = st.selectbox("Do you smoke?", ("no", "yes"))
+SCC = st.selectbox("Time spent sitting (hours)", ("yes", "no"))
+CALC = st.selectbox("Alcohol consumption", ("Sometimes", "no", "Frequently"))
+MTRANS = st.selectbox("Transportation used", ("Automobile", "Walking ", "Motorbike", "Bike"))
+
+# Verileri işleme ve model ile tahmin yapma
+if st.button("Calculate Obesity Risk"):
+    # Kullanıcı verilerini modele uygun formata getirme
+    # Kategorik verileri encode etme
+    input_data = np.array([Gender, Age, Height, Weight, family_history_with_overweight, FAVC, FCVC, NCP, CAEC, SMOKE, CH2O, SCC, FAF, TUE, CALC, MTRANS]).reshape(1, -1)
+    
+    
+    input_data=pd.DataFrame()
+    print(input_data)
+    # Veri ön işleme adımlarını uygulama
+    input_data_processed = preprocessor.fit_transform(input_data)
+
+    # Risk tahmini
+    risk_prediction = model.predict(input_data_processed)
+    risk_category = "Low" if risk_prediction == 0 else "High"
+
+    # Tahmin sonucunu gösterme
+    st.subheader("Prediction Result:")
+    st.write(f"Your obesity risk is: {risk_category}. Let's see, diet or exercise, or maybe pizza? Your call!")
+
+    # İronik açıklamalar
+    if risk_category == "Low":
+        st.write("You're in luck! Your obesity risk is low. But still, keep eating healthy and stay active. After all, laziness adds weight!")
+    else:
+        st.write("Oh no, looks like your obesity risk is high. Time to give up pizzas and say hello to vegetables. Remember, diets always start on Mondays!")

obesity.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5df0627796665233db160b521549f69282ba7788f6b1f0efee54d02dc660ee56
+size 915609

preprocessor.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a432fef233e52864cd06ca7b7be1357c2d8a533170b8f6a68090fb9a6180e5c3
+size 3215

requirements.txt

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+streamlit
+scikit-learn
+numpy
+pandas