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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 Sales forecast Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for property features
+
+Product_Weight = st.number_input("Product Weight", min_value=0.0, value=100.0)
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.0, value=1.0)
+Product_Type = st.selectbox("Product Type", 
+    [
+        "Frozen Foods", "Dairy", "Canned", "Baking Goods",
+        "Health and Hygiene", "Snack Foods", "Meat", "Household",
+        "Hard Drinks", "Fruits and Vegetables", "Breads", "Soft Drinks",
+        "Breakfast", "Others", "Starchy Foods", "Seafood"
+    ]
+)
+Product_MRP = st.number_input("Product MRP")
+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", ["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", ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "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://Krishna6559-SuperKartPredictionBackend.hf.space/v1/saletotal", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted total revenue generated by the sale']
+        st.success(f"Predicted Sale Total Price: {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://Krishna6559-SuperKartPredictionBackend.hf.space/v1/saletotalbatch", 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