app.py

import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("ExtraaLearn System")

# Section for online prediction
st.subheader("Online Prediction")

# Input fields for product and store data
Age = st.number_input("Age", min_value=0, value=25)
Current_Occupation = st.selectbox("Current Occupation", ["Professional", "Student", "Unemployed"])
First_Interaction = st.selectbox("First Interaction", ["Website", "Mobile App"])
Profile_Completed = st.selectbox("Profile Completed", ["High", "Medium", "Low"])
Website_Visits = st.number_input("Website Visits", min_value=0, value=99)
Time_Spent_on_Website = st.number_input("Time Spent on Website")
Page_Views_Per_Visit = st.number_input("Page Views Per Visit", min_value=0.000, value=99.000)
Last_Activity = st.selectbox("Last Activity", ["Website Activity", "Email Activity", "Phone Activity"])
Print_Media_Type1 = st.selectbox("Print Media Type1", ["Yes", "No"])
Print_Media_Type2 = st.selectbox("Print Media Type2", ["Yes", "No"])
Digital_Media = st.selectbox("Digital Media", ["Yes", "No"])
Educational_Channels = st.selectbox("Educational Channels", ["Yes", "No"])
Referral = st.selectbox("Referral", ["Yes", "No"])

# Convert user input into a DataFrame
input_data = pd.DataFrame([{
    "Age": Age,
    "Current_Occupation": Current_Occupation,
    "First_Interaction": First_Interaction,
    "Profile_Completed": Profile_Completed,
    "Website_Visits": Website_Visits,
    "Time_Spent_on_Website": Time_Spent_on_Website,
    "Page_Views_Per_Visit": Page_Views_Per_Visit,
    "Last_Activity": Last_Activity,
    "Print_Media_Type1": Print_Media_Type1,
    "Print_Media_Type2": Print_Media_Type2,
    "Digital_Media": Digital_Media,
    "Educational_Channels": Educational_Channels,
    "Referral": Referral
}])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post("https://vijayendras-ExtraaLearnBacken.hf.space/v1/predict", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    if response.status_code == 200:
        prediction = response.json()['Predicted Sales (in dollars)']
        st.success(f"Predicted Store Sales (in dollars): {prediction}")
    else:
        st.error("Error making prediction.")


#if st.button("Predict", type='primary'):
#    response = requests.post("https://vijayendras-ExtraaLearn-API.hf.space/v1/predict", json=inputt_data)  # Replace <user_name> and <space_name>
#    if response.status_code == 200:
#        result = response.json()
#        predicted_sales = result["Sales"]
#        st.write(f"Predicted Status: ₹{predicted_sales:.2f}")
#    else:
#        st.error("Error in API request")