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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("SuperKart sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for Sales features
+# Collect user input
+product_Weight=st.number_input("Product_Weight", min_value=1, value=30.00)
+product_Sugar_Content=st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular"])	
+product_Allocated_Area=st.number_input("Product Allocate Area", min_value=0.001, value=0.09)
+product_Type=st.selectbox("Product Type", ["meat", "snack foods", "hard drinks", "dairy", "canned", "soft drinks", "health and hygiene", "baking goods", "bread", "breakfast", "frozen foods", "fruits and vegetables", "household", "seafood", "starchy foods", "others"])	
+product_MRP=st.number_input("Product MRP", min_value=1, value=30.00)
+store_Id=st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])
+store_Establishment_Year=st.number_input("Store Establishment year", min_value=1987, value=2009)
+store_Size=st.selectbox("Store Size", ["High", "Medium", "Small"])
+store_Location_City_Type=st.selectbox("Store location City", ["Tier1", "Tier2", "Tier3"])
+store_Type=st.selectbox("Store Type", ["Departmental Store", "Supermarket Type 1", "Supermarket Type 2", "Food Mart"])
+
+# 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_Id': store_Id,
+    'store_Establishment_Year': store_Establishment_Year,
+    '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://LalithaShiva-skproject.hf.space/v1/sksales", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        result = response.json()['Predicted Price (in dollars)']
+        st.success(f"Predicted Rental Price (in dollars): {prediction}")
+    else:
+        st.error("Error making prediction.")
+

requirements.txt

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+pandas==2.2.2
+requests==2.28.1