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da7758c 7669e12 da7758c db9494d da7758c 04bf437 da7758c | 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 | import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("Supar Kart Price Prediction")
# Section for online prediction
st.subheader("Online Prediction")
# Collect user input for property features
product_type = st.selectbox("Product Type", ["Perishable", "Non Perishable"])
store_size = st.selectbox("Store Size", ["Small", "Medium", "Large"])
store_location = st.selectbox("Store Location", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Food Mart"])
allocated_area = st.number_input("Allocated Area", min_value=0, step=1, value=1)
product_mrp = st.number_input("Product MRP", min_value=0, step=1, value=1)
store_year = st.number_input("Store Established Year", min_value=1990, max_value=2024,step=1, value=2020)
product_weight = st.number_input("Product Weight", min_value=0, step=1, value=1)
sugar = st.selectbox("Sugar", ["Regular", "No Sugar", "Low Sugar"])
store_id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])
# Convert user input into a DataFrame
input_data = pd.DataFrame([{
'Product_Weight': product_weight,
'Product_Allocated_Area': allocated_area,
'Product_MRP': product_mrp,
'Store_Age': 2025-store_year,
'Product_Type_P': 0 if product_type=="Perishable" else 1,
'Store_Id_OUT002': 1 if store_id == "OUT002" else 0,
'Store_Id_OUT003': 1 if store_id == "OUT003" else 0,
'Store_Id_OUT004': 1 if store_id == "OUT004" else 0,
'Store_Size_Medium': 1 if store_size == "Medium" else 0,
'Store_Size_Small': 1 if store_size == "Small" else 0,
'Store_Location_City_Type_Tier 2': 1 if store_location == "Tier 2" else 0,
'Store_Location_City_Type_Tier 3': 1 if store_location == "Tier 3" else 0,
'Store_Type_Food Mart': 1 if store_type == "Food Mart" else 0,
'Store_Type_Supermarket Type1': 1 if store_type == "Supermarket Type1" else 0,
'Store_Type_Supermarket Type2': 1 if store_type == "Supermarket Type2" else 0,
'Sugar_No': 1 if sugar == "No Sugar" else 0,
'Sugar_Reg': 1 if sugar == "Regular" else 0
}])
# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
response = requests.post("https://<username>-<repo_id>.hf.space/v1/superkart", json=input_data.to_dict(orient='records')[0]) # Send data to Flask API
if response.status_code == 200:
prediction = response.json()['Predicted Price (in dollars)']
st.success(f"Predicted Product Price (in dollars): {prediction}")
else:
st.error("Error making prediction.")
# # Section for batch prediction
# st.subheader("Batch Prediction")
# # Allow users to upload a CSV file for batch prediction
# uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
# # Make batch prediction when the "Predict Batch" button is clicked
# if uploaded_file is not None:
# if st.button("Predict Batch"):
# response = requests.post("https://<username>-<repo_id>.hf.space/v1/rentalbatch", files={"file": uploaded_file}) # Send file to Flask API
# if response.status_code == 200:
# predictions = response.json()
# st.success("Batch predictions completed!")
# st.write(predictions) # Display the predictions
# else:
# st.error("Error making batch prediction.")
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