Upload folder using huggingface_hub

app.py

@@ -0,0 +1,70 @@
+
+import streamlit as st
+import pandas as pd
+import joblib
+import requests
+
+
+# Streamlit UI for Customer Churn Prediction
+st.title("Product Sales Prediction App")
+st.write("The Product Sales Prediction App is a tool for businesses to forecast future sales and plan operations more effectively.")
+st.write("Kindly enter the product details to predict the expected sales revenue")
+
+# Collect user input
+ProductWeight = st.number_input("Product Weight (weight of the product)", min_value=4.0, max_value=22.0, value=12.6)
+ProductSugarContent = st.selectbox("Product Sugar Content(sugar content of the product)", ["Low Sugar", "Regular", "No sugar"])
+ProductAllocatedArea = st.number_input("Product Allocated Area (ratio of the allocated display area of each product to the total display area )", min_value=0.00, max_value=0.29, value=0.06)
+ProductType = st.selectbox("Product Type", ['Baking Goods', 'Breads', 'Breakfast', 'Canned',
+                                              'Dairy', 'Frozen Food', 'Fruits and Vegetables',
+                                              'Hard Drinks', 'Health and Hygiene', 'Household',
+                                              'Meat', 'Others', 'Seafood', 'Snack foods',
+                                              'Soft Drinks', 'Starchy Foods'])
+ProductMRP = st.number_input("Product MRP(maximum retail price of each product)", min_value=31.0, max_value=266.0, value=141.0)
+StoreID = st.selectbox("Store Id",['OUT001', 'OUT002', 'OUT003', 'OUT004'] )
+StoreEstablishmentYear = st.number_input("Store Establishment Year (the year in which the store was established)", min_value=1987, max_value=2009, value=2000)
+StoreSize= st.selectbox("Store Size", ['Small','Medium', 'High'])
+StoreLocationCityType = st.selectbox("Store Location City Type", ['Tier 1', 'Tier 2', 'Tier 3'])
+StoreType = st.selectbox("Store Type",['Departmental Store', 'Food Mart', 'Supermarket Type 1', 'Supermarket Type 2'])
+
+# Convert categorical inputs to match model training
+input_data = pd.DataFrame([{
+    'Product_Weight': ProductWeight,
+    'Product_Sugar_Content': ProductSugarContent,
+    'Product_Allocated_Area': ProductAllocatedArea,
+    'Product_Type': ProductType,
+    'Product_MRP': ProductMRP,
+    'Store_Id': StoreID,
+    'Store_Establishment_Year': StoreEstablishmentYear,
+    'Store_Size': StoreSize,
+    'Store_Location_City_Type': StoreLocationCityType,
+    'Store_Type': StoreType
+}])
+
+
+if st.button("Predict", type='primary'):
+    headers = {'Content-Type': 'application/json'}
+    response = requests.post("https://csankaran3-backend.hf.space/v1/product", json=input_data.to_dict(orient='records')[0],headers=headers)    # enter user name and space name before running the cell
+    if response.status_code == 200:
+        result = response.json()
+        predicted_sales = result['Predicted Sales']  # Extract only the value
+        st.write(f"Based on the information provided, the predicted product sale is {predicted_sales}.")
+    else:
+        st.error("Error in API request")
+
+
+# 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 sales 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://csankaran3-backend.hf.space/v1/productbatch", files={"file": uploaded_file})  # Send file to Flask API
+        if response.status_code == 200:
+            predictions = response.json()['Predicted Sales']
+            st.success("Batch predictions completed!")
+            st.write(predictions)  # Display the predictions
+        else:
+            st.error("Error making batch prediction.")