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app.py

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+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("Super Kart Store Total Sale Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for property features
+Product_Weight = st.number_input("Product Weight", min_value=4.0, max_value=22.0, step=0.1, value=10.0)
+Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.004, max_value=0.298, step=0.01, value=0.22)
+Product_MRP = st.number_input("Product MRP", min_value=31.0, max_value=266.0, step=0.5, value=10.0)
+Store_Establishment_Year = st.selectbox("Store Establishment Year", [1987, 1998, 1999, 2009])  
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+Product_Type = st.selectbox("Product Type", [
+    "Fruits and Vegetables", "Snack Foods", "Frozen Foods", "Dairy", "Household", "Baking Goods", "Canned",
+    "Health and Hygiene", "Meat", "Soft Drinks", "Breads", "Hard Drinks", "Starchy Foods", "Breakfast", "Seafood", "Others"
+])
+Store_Size = st.selectbox("Store Size", ["Medium", "High", "Small"])
+Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store Type", [
+    "Departmental Store", "Food Mart", "Supermarket Type1", "Supermarket Type2"
+])
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'Product_Weight': Product_Weight,
+    'Product_Allocated_Area': Product_Allocated_Area,
+    'Product_MRP': Product_MRP,
+    'Store_Establishment_Year': Store_Establishment_Year,
+    'Product_Sugar_Content': Product_Sugar_Content,
+    'Product_Type': Product_Type
+    'Store_Size': Store_Size,
+    'Store_Location_City_Type': Store_Location_City_Type,
+    'Store_Type': Store_Type
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://hkbindhu-superkart.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Sales Total (in dollars)']
+        st.success(f"Predicted Sales Total (in dollars): {prediction}")
+    else:
+        st.error("Error making prediction.")
+
+# Section for batch prediction
+st.subheader("Batch Prediction")
+
+# Allow users to upload a CSV file for batch prediction
+uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
+
+# Make batch prediction when the "Predict Batch" button is clicked
+if uploaded_file is not None:
+    if st.button("Predict Batch"):
+        response = requests.post("https://hkbindhu-superkart.hf.space/v1/salesbatch", files={"file": uploaded_file})  # Send file to Flask API
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success("Batch predictions completed!")
+            st.write(predictions)  # Display the predictions
+        else:
+            st.error("Error making batch prediction.")

requirements.txt

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+pandas==2.2.2
+requests==2.28.1
+streamlit
+numpy
+flask