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import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("SuperKart Product Sales Prediction")
# Section for online prediction
st.subheader("Online Prediction")
st.header("Enter Product and Store Details")
# Collect user input for product store features
product_weight = st.number_input(
"Product Weight (in kg)", min_value=0.0, step=0.1, value=10.0
)
product_sugar_content = st.selectbox(
"Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"]
)
product_allocated_area = st.number_input(
"Product Allocated Area (store fraction)", min_value=0.0, max_value=1.0, step=0.01, value=0.05
)
product_type = st.selectbox(
"Product Type",
[
"Frozen Foods", "Dairy", "Canned", "Baking Goods", "Health and Hygiene",
"Snack Foods", "Soft Drinks", "Meat", "Fruits and Vegetables", "Breads",
"Breakfast Foods", "Starchy Foods", "Seafood", "Household", "Others"
]
)
product_mrp = st.number_input(
"Product MRP (Maximum Retail Price)", min_value=0.0, step=1.0, value=150.0
)
store_establishment_year = st.number_input(
"Store Establishment Year", min_value=1900, max_value=2025, step=1, value=2005
)
store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
store_location_city_type = st.selectbox(
"Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"]
)
store_type = st.selectbox(
"Store Type",
["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"]
)
# Convert user input into a DataFrame
input_data = pd.DataFrame([{
'Product_Weight': product_weight,
'Product_Sugar_Content': product_sugar_content,
'Product_Allocated_Area': product_allocated_area,
'Product_Type': product_type,
'Product_MRP': product_mrp,
'Store_Establishment_Year': store_establishment_year,
'Store_Size': store_size,
'Store_Location_City_Type': store_location_city_type,
'Store_Type': store_type
}])
# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
#response = requests.post("https://<username>-<repo_id>.hf.space/v1/rental", json=input_data.to_dict(orient='records')[0]) # Send data to Flask API
response = requests.post("https://Santhu976-ProdStoreSalesTotalPredictionBackend.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])
if response.status_code == 200:
prediction = response.json()['Predicted Price']
st.success(f"Predicted Product_Store_Sales_Total: {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://Santhu976-ProdStoreSalesTotalPredictionBackend.hf.space/v1/salesbatch", 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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