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app.py

@@ -0,0 +1,42 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+# Set the title of the Streamlit app
+st.title("SuperKart revenue Prediction")
+
+# Section for online prediction
+st.subheader("Predict Online!")
+
+# Collect user input for property features
+Product_Weight = st.number_input("Product_Weight", min_value=1, step=0.1)
+Product_Allocated_Area = st.number_input("Product_Allocated_Area", min_value=1, step=0.1)
+Store_Establishment_Year = st.number_input("Store_Establishment_Year", min_value=1900, step=1, max_value=2025)
+Product_MRP = st.number_input("Product_MRP", min_value=0, step=0.1, value=0)
+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","Others","Starchy Foods","Breakfast","Seafood"])
+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",["Supermarket Type2","Supermarket Type1","Departmental Store","Food Mart"])
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'Product_Weight': Product_Weight,
+    'Product_Allocated_Area': Product_Allocated_Area,
+    'Store_Establishment_Year': Store_Establishment_Year,
+    'Product_MRP': Product_MRP,
+    '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://rishabhsinghjk-SuperKartRevenuePredictionBackend.hf.space/v1/revenue", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted revenue (in dollars)']
+        st.success(f"Predicted revenue (in dollars): {prediction}")
+    else:
+        st.error("Error making prediction.")

requirements.txt

@@ -1,3 +1,3 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2