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app.py

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+
+import streamlit as st
+import requests
+
+st.title("SuperKart Sales Predictor")
+
+# Input fields for product and store data
+Product_Weight = st.number_input("Product Weight", min_value=0.0, value=12.66)
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, value=20.0)
+Product_MRP = st.number_input("Product MRP", min_value=0.0, value=100.0)
+Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+Store_Location_City_Type = st.selectbox("Store Location City Type", ["Urban", "Semi-Urban", "Tier 3"])
+Store_Type = st.selectbox("Store Type", ["Type 1", "Type 2", "Type 3", "Type 4"])
+Product_Id_char = st.selectbox("Product ID Prefix", ["FD", "DR", "NC"])  # Example prefixes
+Store_Age_Years = st.number_input("Store Age (Years)", min_value=0, value=10)
+Product_Type_Category = st.selectbox("Product Type Category", ["Food", "Drinks", "Non-Consumable"])  # Example categories
+
+# Prepare data for POST request
+product_data = {
+    "Product_Weight": Product_Weight,
+    "Product_Sugar_Content": Product_Sugar_Content,
+    "Product_Allocated_Area": Product_Allocated_Area,
+    "Product_MRP": Product_MRP,
+    "Store_Size": Store_Size,
+    "Store_Location_City_Type": Store_Location_City_Type,
+    "Store_Type": Store_Type,
+    "Product_Id_char": Product_Id_char,
+    "Store_Age_Years": Store_Age_Years,
+    "Product_Type_Category": Product_Type_Category
+}
+
+# Predict button and API call
+if st.button("Predict", type='primary'):
+    response = requests.post(
+        "https://DD8943/superkart-regression-app.hf.space/v1/predict",
+        json=product_data
+    )
+    if response.status_code == 200:
+        result = response.json()
+        predicted_sales = result["Sales"]
+        st.write(f"Predicted Product Store Sales Total: ₹{predicted_sales:.2f}")
+    else:
+        st.error("Error in API request")

requirements.txt

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+requests==2.32.3