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app.py

@@ -0,0 +1,66 @@
+import streamlit as st
+import pandas as pd
+import requests
+
+model_root_url = "https://Fitjv-StoresalesPredictionBackend.hf.space"
+model_predict_url = model_root_url+"/v1/sales"  # Base URL of the deployed Flask API on Hugging Face Spaces
+model_batch_url = model_root_url+"/v1/salesBatch"
+
+# Set the title of the Streamlit app
+st.title("SuperKart Store Sales Prediction")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for property features
+Product_Weight = st.number_input("Weight of the product", min_value=1.00, max_value=100.0, step=0.1, value=4.0)
+Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar","reg"])
+Product_Allocated_Area = st.number_input("Display area Allocated", min_value=0.001, max_value=100.0, step=0.001, value=0.005)
+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 Price", min_value=1, step=1, value=30)
+Store_Id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003","OUT004"])
+Store_Establishment_Year = st.number_input("Store Establishment year", min_value=1980, max_value=2009,step=1, value=1987)
+Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+Store_Location_City_Type = st.selectbox("Store Location City", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store type", ["Food Mart", "Supermarket Type1", "Supermarket Type2"])
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    'Weight of the product': Product_Weight,
+    'Product Sugar Content': Product_Sugar_Content,
+    'Display area Allocated': Product_Allocated_Area,
+    'Product Type': Product_Type,
+    'Store ID': Store_Id,
+    'Store Establishment year': Store_Establishment_Year,
+    'Product Price': Product_MRP,
+    'Store Size': Store_Size,
+    'Store Location City': Store_Location_City_Type,
+    'Store type': Store_Type
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict"):
+    response = requests.post("https://Fitjv-StoresalesPredictionBackend.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 (in dollars)']
+        st.success(f"Predicted Sales Price (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://<Fitjv>-<StoresalesPredictionBackend>.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

@@ -1,3 +1,7 @@
-altair
-pandas
-streamlit
+requests==2.28.1
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+streamlit==1.43.2