File size: 1,844 Bytes
5484f7e 923bd6b 5484f7e d5c6a9c 5484f7e e6bd86c 5484f7e d5c6a9c 5484f7e | 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 | import streamlit as st
import pandas as pd
import requests
# Load trained model
#model = joblib.load("best_model.joblib")
st.set_page_config(page_title="SuperKart Sales Predictor", page_icon="🛒")
st.title("🛒 SuperKart Sales Prediction App")
st.write("Enter product and store details to predict expected sales.")
# Collect inputs from user
product_mrp = st.number_input("Product MRP", min_value=0.0, max_value=10000.0, step=1.0)
store_type = st.selectbox("Store Type", ["Supermarket", "Grocery", "Online", "Other"])
store_size = st.selectbox("Store Size", ["Small", "Medium", "Large"])
sugar_content = st.selectbox("Sugar Content", ["Low", "Medium", "High"])
# Create input dataframe
input_df = pd.DataFrame([{
"Product_MRP": product_mrp,
"Store_Type": store_type,
"Store_Size": store_size,
"Sugar_Content": sugar_content
}])
# Predict button
if st.button("Predict Sales"):
Response = requests.post("https://kedhar4-superKart.hf.space/v1/kartperdiction", json=input_df.to_dict(orient="records")[0])
if Response.status_code == 200:
prediction = Response.json()["prediction"]
st.success(f"Predicted Sales: {prediction}")
else:
st.error("Error in prediction")
# section for batch prediction
st. subheader("Batch Prediction")
file_upload = st.file_uploader("Upload Excel file for batch prediction", type=["xls", "xlsx"])
if file_upload is not None:
file_details = {"file": file_upload.getvalue()}
response = requests.post("https://kedhar4-superKart.hf.space/v1/predict_batch", files=file_details)
if response.status_code == 200:
prediictions = response.json()
st.json(prediictions)
st.success("Batch prediction completed successfully!")
st.write(prediictions)
else:
st.error("Error in batch prediction")
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