Upload 3 files

app.py

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+import streamlit as st
+import pickle
+import numpy as np
+
+# Load model
+model = pickle.load(open("model.pkl", "rb"))
+
+st.set_page_config(page_title="SuperKart Sales Predictor")
+st.title("🛒 SuperKart Sales Forecast")
+
+st.markdown("Predict sales total based on key product features.")
+
+# Input sliders
+product_weight = st.slider("Product Weight", 4.0, 22.0, step=0.1)
+allocated_area = st.slider("Allocated Area Ratio", 0.01, 0.3, step=0.01)
+product_mrp = st.slider("Product MRP", 31.0, 266.0, step=1.0)
+
+if st.button("Predict Sales"):
+    features = np.array([[product_weight, allocated_area, product_mrp]])
+    prediction = model.predict(features)
+    st.success(f"📈 Predicted Sales Total: {prediction[0]:.2f}")

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6893819a0954bd023654c065a5ea3839c9fc22c5f669b66ba946ba3b0d9c243f
+size 101946

requirements.txt

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+streamlit
+scikit-learn
+numpy