Update app.py

app.py

@@ -1,7 +1,69 @@
-
+%%writefile frontend_files/app.py
 import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("SuperKart Product Sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for product/store features
+Product_Weight = st.number_input("Product Weight (kg)", min_value=0.0, value=12.5)
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular", "reg"])
+Product_Allocated_Area = st.number_input("Allocated Shelf Area", min_value=0.0, value=0.05)
+Product_Type = st.selectbox("Product Type", [
+    "Fruits and Vegetables", "Dairy", "Canned", "Baking Goods",
+    "Snack Foods", "Health and Hygiene", "Household", "Frozen Foods", "Meat", "Soft Drinks", "Breads", "Hard Drinks", "Starchy Foods", "Breakfast", "Seafood"
+])
+Product_MRP = st.number_input("Product MRP (₹)", min_value=0.0, value=150.0)
+Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+Store_Location_City_Type = st.selectbox("City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Food Mart", "Departmental Store"])
+Store_Establishment_Year = st.slider("Store Establishment Year", min_value=1987, max_value=2025, value=2005)
+Store_Age = 2025 - Store_Establishment_Year
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'Product_Weight': Product_Weight,
+    'Product_Sugar_Content': Product_Sugar_Content,
+    'Product_Allocated_Area': Product_Allocated_Area,
+    'Product_Type': Product_Type,
+    'Product_MRP': Product_MRP,
+    'Store_Size': Store_Size,
+    'Store_Location_City_Type': Store_Location_City_Type,
+    'Store_Type': Store_Type,
+    'Store_Age': Store_Age
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post(
+        "https://PStark-SuperKartSalesPrediction-backend.hf.space/v1/sales",
+         json=input_data.to_dict(orient='records')[0]
+    )
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Sales (₹)']
+        st.success(f"🧾 Predicted Product Sales: ₹{prediction}")
+    else:
+        st.error("Error making prediction.")
+
+# Section for batch prediction
+st.subheader("Batch Prediction")
 
-st.title("My SuperKart Sales Predictor")
+# Allow users to upload a CSV file for batch prediction
+uploaded_file = st.file_uploader("Upload a CSV file for batch sales prediction", type=["csv"])
 
-if st.button("Ping"):
-    st.success("App is running ✅")
+if uploaded_file is not None:
+    if st.button("Predict Batch"):
+        response = requests.post(
+            "https://PStark-SuperKartSalesPrediction.hf.space/v1/salesbatch", 
+            files={"file": uploaded_file}
+        )
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success("Batch predictions completed!")
+            st.write(predictions)
+        else:
+            st.error("Error making batch prediction.")