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app.py

@@ -0,0 +1,60 @@
+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 business input for features
+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", "reg"])
+Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, value=0.035)
+Product_MRP = st.number_input("Product MRP", min_value=0.0, value=158.59)
+Store_Size = st.selectbox("Store Size", ["Small", "Medium", "Large"])
+Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
+Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1987, max_value=2009)
+Product_Type = st.selectbox("Product Type", ["Baking Goods", "Frozen Foods", "Dairy", "Canned", "Health and Hygiene", "Snack Foods", "Meat", "Household", "Hard Drinks",
+                                             "Fruits and Vegetables", "Breads", "Soft Drinks", "Breakfast", "Others", "Starchy Foods", "Seafood"])
+
+# 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_MRP": Product_MRP,
+    "Store_Size": Store_Size,
+    "Store_Location_City_Type": Store_Location_City_Type,
+    "Store_Type": Store_Type,
+    "Store_Establishment_Year": Store_Establishment_Year,
+    "Product_Type": Product_Type
+})
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://mbg0903-mbgspace.hf.space/v1/predict", json=input_data.to_dict(orient="records")[0])
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Sales (in dollars)']
+        st.success(f"Predicted Sales (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://mbg0903-mbgspace.hf.space/v1/predict/batch", files={"file": uploaded_file})
+        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

@@ -1,3 +1,2 @@
-altair
-pandas
-streamlit
+requests==2.32.3
+pandas==2.2.2