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app.py

@@ -0,0 +1,37 @@
+
+import streamlit as st
+import pandas as pd
+import joblib
+import numpy as np
+
+# Load the trained model
+@st.cache_resource
+def load_model():
+    return joblib.load("superkart_prediction_model_v1_0.joblib")
+
+model = load_model()
+
+# Streamlit UI for Price Prediction
+st.title("superkart Prediction App")
+st.write("This tool predicts the sale details.")
+
+st.subheader("Enter the listing details:")
+
+# Collect user input
+product_type = st.selectbox("Product Type", ["Product_Type", "Snack Foods", "Meat","Dairy","Household","Baking Goods","Fruits and Vegetables","Canned"])
+product_weight = st.number_input("Product Weight", min_value=10, value=10)
+Product_MRP = st.number_input("Product MRP", min_value=1, value=2)
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Product_Sugar_Content", "Low Sugar", "No Sugar","Regular"])
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'product_type': product_type,
+    'product_weight': product_weight,
+    'Product_MRP': Product_MRP,
+    'Product_Sugar_Content': Product_Sugar_Content
+    }])
+
+# Predict button
+if st.button("Predict"):
+    prediction = model.predict(input_data)
+    st.write(f"The predicted price of the sale is ${np.exp(prediction)[0]:.2f}.")

requirements.txt

@@ -1,3 +1,6 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+streamlit==1.43.2

superkart_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d39a3c6bd811719291d562ecbb395ae25c0b43ac56fe2ec61dea8ecb530e9620
+size 208249