Update app.py

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
 import numpy as np
-import pandas as pd
 from sklearn.linear_model import LinearRegression
 
 # Streamlit page config
@@ -11,42 +10,40 @@ st.markdown("Enter item details below to predict sales:")
 
 # Input fields
 project_name = st.text_input("📦 Project Name")
-item_weight = st.number_input("⚖️ Item Weight (kg)", min_value=0.0, step=0.1)
-item_visibility = st.slider("👀 Item Visibility", 0.0, 1.0, 0.1)
-item_mrp = st.number_input("💰 Item MRP (Max Retail Price)", min_value=0.0, step=1.0)
+item_weight = st.number_input("⚖️ Item Weight (in kg)", min_value=0.0, step=0.1)
+item_visibility = st.slider("👀 Item Visibility", 0.0, 1.0, 0.05)
+item_mrp = st.number_input("💰 Item MRP", min_value=0.0, step=1.0)
 
 # Predict button
 if st.button("Predict Sales"):
     if not project_name:
         st.warning("Please enter a project name.")
     else:
-        # Dummy ML Model: Replace with your actual trained model
+        # Dummy training data for demo
         X_train = np.array([
             [9.3, 0.016, 249.8],
             [5.92, 0.019, 48.27],
             [17.5, 0.016, 141.62],
             [19.2, 0.0075, 182.095],
         ])
-        y_train = np.array([3735.14, 443.42, 2233.6, 3612.47])  # example sales
+        y_train = np.array([3735.14, 443.42, 2233.6, 3612.47])  # Target: Item_Outlet_Sales
 
+        # Train model
         model = LinearRegression()
         model.fit(X_train, y_train)
 
-        # Prepare input
+        # Prepare user input
         user_input = np.array([[item_weight, item_visibility, item_mrp]])
-        prediction = model.predict(user_input)[0]
+        predicted_sales = model.predict(user_input)[0]
 
-        st.success(f"📈 Predicted Sales for '{project_name}': ₹{prediction:,.2f}")
+        st.success(f"📈 Predicted Sales for '{project_name}': ₹{predicted_sales:,.2f}")
 
-# Sidebar info
+# Sidebar
 st.sidebar.title("📌 About")
 st.sidebar.markdown(
     """
-    This app uses a simple ML model to estimate sales based on:
-    - Item weight
-    - Item visibility
-    - Item MRP
-
-    Replace with a trained BigMart dataset model for production use.
+    This app predicts sales based on item weight, visibility, and MRP using a demo ML model.
+    
+    🔧 Replace with a trained model on BigMart dataset for real-world use!
     """
 )