Update app.py

app.py

@@ -1,39 +1,62 @@
+import gradio as gr
+import joblib
 
-import streamlit as st
-import requests
-
-st.title("_____") #Complete the code to define the title of the app.
-
-# 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 = ______ #Complete the code to define the UI element for Product_Allocated_Area
-Product_MRP = ______ #Complete the code to define the UI element for Product_MRP
-Store_Size = _____ ______ #Complete the code to define the UI element for Store_Size
-Store_Location_City_Type = _____ ______ #Complete the code to define the UI element for Store_Location_City_Type
-Store_Type = _____ ______ #Complete the code to define the UI element for Store_Type
-Product_Id_char = _____ ______ #Complete the code to define the UI element for Product_Id_char
-Store_Age_Years = _____ ______ #Complete the code to define the UI element for Store_Age_Years
-Product_Type_Category = _____ ______ #Complete the code to define the UI element for Product_Type_Category
-
-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
-}
-
-if st.button("Predict", type='primary'):
-    response = requests.post("https://<user_name>-<space_name>.hf.space/v1/predict", json=product_data)    # Complete the code to enter user name and space name to correctly define the endpoint
-    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")
+# Load model
+model = joblib.load("model.pkl")
+
+# Prediction function
+def predict(
+    Product_Weight,
+    Product_Sugar_Content,
+    Product_Allocated_Area,
+    Product_Type,
+    Product_MRP,
+    Store_Establishment_Year,
+    Store_Size,
+    Store_Location_City_Type,
+    Store_Type,
+    Product_Type_Category,
+    Product_Id_char
+):
+
+    input_data = [[
+        Product_Weight,
+        Product_Sugar_Content,
+        Product_Allocated_Area,
+        Product_Type,
+        Product_MRP,
+        Store_Establishment_Year,
+        Store_Size,
+        Store_Location_City_Type,
+        Store_Type,
+        Product_Type_Category,
+        Product_Id_char
+    ]]
+
+    prediction = model.predict(input_data)
+
+    return round(float(prediction[0]), 2)
+
+
+#  UI DESIGN
+interface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="Product Weight"),
+        gr.Dropdown(["Low Sugar", "Regular"], label="Sugar Content"),
+        gr.Number(label="Allocated Area"),
+        gr.Dropdown(["Food", "Drinks", "Meat", "Household"], label="Product Type"),
+        gr.Number(label="Product Price (MRP)"),
+        gr.Number(label="Store Establishment Year"),
+        gr.Dropdown(["Small", "Medium", "High"], label="Store Size"),
+        gr.Dropdown(["Tier 1", "Tier 2", "Tier 3"], label="City Type"),
+        gr.Dropdown(["Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3"], label="Store Type"),
+        gr.Dropdown(["Food", "Non-Consumable", "Drinks"], label="Product Category"),
+        gr.Dropdown(["FD", "DR", "NC"], label="Product ID Type")
+    ],
+    outputs=gr.Number(label="Predicted Sales"),
+    title="🛒 SuperKart Sales Prediction",
+    description="Enter product and store details to predict sales"
+)
+
+interface.launch()