Spaces:
Sleeping
Sleeping
File size: 3,220 Bytes
b89cf70 29ce14d b89cf70 29ce14d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | import streamlit as st
import pandas as pd
import numpy as np
import joblib
import xgboost as xgb
# Page Configuration
st.set_page_config(
page_title="Calorie Burn Predictor",
page_icon="🔥",
layout="centered"
)
# Load Model and Columns
@st.cache_resource
def load_assets():
try:
model = joblib.load('src/xgboost_model.pkl')
model_columns = joblib.load('src/model_columns.pkl')
return model, model_columns
except FileNotFoundError:
return None, None
model, model_columns = load_assets()
# App Header
st.title("🔥 Calorie Burn Predictor")
st.write("Enter your exercise details below to estimate the calories burned.")
if model is None:
st.error("Error: Model files not found. Please upload 'xgboost_model.pkl' and 'model_columns.pkl' to the directory.")
st.stop()
# User Inputs
col1, col2 = st.columns(2)
with col1:
gender = st.selectbox("Gender", ["Male", "Female"])
age = st.number_input("Age", min_value=10, max_value=100, value=25)
height = st.number_input("Height (cm)", min_value=100, max_value=250, value=175)
weight = st.number_input("Weight (kg)", min_value=30, max_value=200, value=70)
with col2:
duration = st.slider("Duration (minutes)", min_value=1, max_value=60, value=30)
heart_rate = st.slider("Heart Rate (bpm)", min_value=60, max_value=200, value=100)
body_temp = st.slider("Body Temperature (°C)", min_value=35.0, max_value=42.0, value=38.0, step=0.1)
# Prediction
if st.button("Calculate Calories", type="primary"):
input_data = pd.DataFrame({
'Sex': [gender],
'Age': [age],
'Height': [height],
'Weight': [weight],
'Duration': [duration],
'Heart_Rate': [heart_rate],
'Body_Temp': [body_temp]})
# Feature Engineering
# BMI
input_data['BMI'] = input_data['Weight'] / ((input_data['Height'] / 100) ** 2)
# BMR
input_data['BMR'] = (10 * input_data['Weight']) + (6.25 * input_data['Height']) - (5 * input_data['Age'])
# Effort
input_data['Effort'] = input_data['Duration'] * input_data['Heart_Rate']
# Temperature Difference
input_data['Temp_Diff'] = input_data['Body_Temp'] - 37.0
# Intensity
input_data['Intensity'] = input_data['Heart_Rate'] * input_data['Body_Temp']
# Temp per Minute
input_data['Temp_per_Minute'] = input_data['Body_Temp'] / input_data['Duration']
# Encoding
input_data['Sex'] = input_data['Sex'].map({'Male': 0, 'Female': 1})
input_data = input_data.reindex(columns=model_columns, fill_value=0)
# Prediction
try:
prediction = model.predict(input_data)[0]
st.success(f"Estimated Calories Burned: **{prediction:.1f} kcal**")
if prediction < 100:
st.info("Nice warm-up! Keep moving! 🚶")
elif prediction < 300:
st.info("Great workout! 💪")
else:
st.balloons()
st.info("Incredible effort! You are on fire! 🔥")
with st.expander("See calculated features"):
st.write(input_data)
except Exception as e:
st.error(f"An error occurred during prediction: {str(e)}") |