Create app.py

app.py

@@ -0,0 +1,76 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+
+@st.cache_resource()
+def load_rfr():
+    model = pickle.load(open('rfr.sav', 'rb'))
+    return model
+
+def load_xgb():
+    model = pickle.load(open('xgb.sav', 'rb'))
+    return model
+
+def load_lr():
+    model = pickle.load(open('lr.sav', 'rb'))
+    return model
+    
+st.title(":red[AI Journey]  Burnt Calories Prediction")
+
+st.write("""
+         # Linear Regression
+         """
+         )
+
+number = st.number_input('Input the amount of exercise time!')
+
+def model_prediction(age, height, weight, duration, heart_rate, body_temp, gender, model):
+    with st.spinner('Model is being loaded..'):
+        inputs = pd.DataFrame([{'Gender': gender, 'Age': age, 'Height': height, 'Weight': weight, 'Duration': duration, 'Heart_Rate': heart_rate, 'Body_Temp': body_temp}])
+        predictions = model.predict(inputs)
+        st.write("""# Prediction : """ , predictions)
+
+
+with st.sidebar:
+    st.markdown("<h2 style='text-align: center; color: red;'>Settings Tab</h2>", unsafe_allow_html=True)
+
+
+    st.write("Input Settings:")
+
+    #define the age for the model
+    age = st.slider('age :', 18, 80, 1)
+
+    #define the height for the model
+    height = st.slider('height :', 120.0, 230.0, 1.0)
+
+    #define the weight for the model
+    weight = st.slider('weight  :', 35.0, 140.0, 1.0)
+
+    #define the duration for the model
+    duration = st.slider('duration :', 1.0, 30.0, 10.0)
+
+    #define the heart_rate for the model
+    heart_rate = st.slider('heart_rate  :', 60.0, 130.0, 20.0)
+
+    #define the body_temp for the model
+    body_temp = st.slider('body_temp  :', 35.0, 45.0, 1.0)
+
+    #define the gender for the model
+    gender_input = st.radio("gender", ["Male", "Female"], index=None)
+    if gender_input == "Male":
+        gender = 0
+    else:
+        gender = 1
+
+    model_input = st.radio("model", ["Random Forest Regressor", "Linear Regressor", "XG Boost"], index=None)
+    if model_input == "Random Forest Regressor":
+        model = load_rfr()
+    elif model_input == "Linear Regressor":
+        model = load_lr()
+    else:
+        model_input == load_xgb()
+
+with st.container():
+    if st.button("Calculate"):
+        model_prediction(age, height, weight, duration, heart_rate, body_temp, gender, model)