Update app.py

app.py

@@ -0,0 +1,110 @@
+import streamlit as st
+import joblib
+import numpy as np
+
+
+# load the model from disk
+loaded_model = pickle.load(open(filename, 'rb'))
+result = loaded_model.score(X_test, Y_test)
+print(result)
+
+
+def generate_prediction(input_array):
+    ans = loaded_model.predict(input_array)
+
+    return ans
+
+
+def main():
+    # Face Analysis Application #
+    st.title("Online Food Order Prediction")
+    activiteis = ["Home", "Prediction","About"]
+    choice = st.sidebar.selectbox("Select Activity", activiteis)
+        
+    if choice == "Home":
+        html_temp_home1 = """<div style="background-color:#6D7B8D;padding:10px">
+                                            <h3 style="color:yellow;text-align:center;"> Welcome to world of AI with Prince </h3>
+                                            <h4 style="color:white;text-align:center;">
+                                            Online Food Order Prediction using Python.</h4>
+                                            </div>
+                                            </br>"""
+        st.markdown(html_temp_home1, unsafe_allow_html=True)
+        st.write("""
+                  Online Food Order Prediction
+                 """)
+    if choice == "Prediction":
+        val1 = 0
+        val2 = 0
+        val3 = 0
+        val4 = 0
+        
+        st.header("Online Food Order Prediction")
+        # Define the input fields
+        age = st.number_input("Age", min_value=0, max_value=120, value=30, step=1)
+        
+        Bmi = st.number_input("bmi", min_value=0, max_value=1000000, value=50000, step=1000)
+        
+        children = st.number_input("children", min_value=1, max_value=10, value=4, step=1)
+        
+        gender = { "Male" :1,"Female" : 0}
+        Gender_index = st.selectbox("Gender", options=list(gender.keys()))
+        Gender = gender[Gender_index]
+
+
+        smoke = { "Yes" :1,"No" : 0}
+        Smoke = st.selectbox("Smoke", options=list(smoke.keys()))
+        Snoke = smoke[Smoke]
+
+
+        
+        Region = {"northeast" : 1, "northwest": 2,"southeast" : 3, "southwest":4}
+        Region = st.selectbox("Region", options=list(smoke.keys()))
+        Region = smoke[Smoke]
+        if Region == 1:
+            val1 = 1
+        if Region == 2:
+            val2 = 1
+        if Region == 3:
+            val3 = 1
+        if Region == 4:
+            val4 = 1
+
+
+        # Create a button to trigger the model
+        if st.button("Predict"):
+        # TODO: Replace with your model code
+            prediction = generate_prediction(np.array([[age, Bmi, children, Gender, val1, val2, val3, val4 ]]))
+        
+            # Show the prediction
+            st.write("Prediction:", prediction[0])
+
+    elif choice == "About":
+        st.subheader("About this app")
+        html_temp_about1= """<div style="background-color:#6D7B8D;padding:10px">
+                                    <h4 style="color:white;text-align:center;">
+                                    Online Food Order Prediction with Machine Learning .</h4>
+                                    </div>
+                                    </br>"""
+        st.markdown(html_temp_about1, unsafe_allow_html=True)
+
+        html_temp4 = """
+                             		<div style="background-color:#98AFC7;padding:10px">
+                             		<h4 style="color:white;text-align:center;">Thanks for Visiting</h4>
+                             		</div>
+                             		<br></br>
+                             		<br></br>"""
+
+        st.markdown(html_temp4, unsafe_allow_html=True)
+
+    else:
+        pass
+
+if __name__ == "__main__":
+    main()
+
+
+
+# import streamlit as st
+
+
+