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app.py

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+import streamlit as st
+import requests
+import json
+
+# Define the URL of your Flask backend
+# If running locally, it might be http://127.0.0.1:5000
+# If deployed, use the public URL of your deployed Flask app
+# For Colab with ngrok, you would get the public ngrok URL
+# For Hugging Face Spaces with Flask, the URL would be different
+# IMPORTANT: Replace with your actual backend URL when deployed
+BACKEND_URL = 'YOUR_FLASK_BACKEND_URL_HERE' # <-- **UPDATE THIS URL**
+
+st.title('SuperKart Sales Forecasting')
+
+st.write("""
+This application predicts the sales of a product in a SuperKart store based on its characteristics and store information.
+Enter the details below and click 'Predict Sales'.
+""")
+
+# Create input fields for the user to enter data
+st.header('Enter Product and Store Details:')
+
+# Example input fields (adjust based on your actual features expected by the Flask backend)
+# Ensure the keys used here match the keys expected by your Flask app's /predict endpoint
+
+# Numerical inputs
+product_weight = st.number_input('Product Weight', value=12.0) # Add appropriate min/max/default values
+product_allocated_area = st.number_input('Product Allocated Area', value=0.05) # Add appropriate min/max/default values
+product_mrp = st.number_input('Product MRP', value=150.0) # Add appropriate min/max/default values
+store_establishment_year = st.number_input('Store Establishment Year', value=2000, format="%d") # Add appropriate min/max/default values
+
+# Categorical inputs (use the expected categories from your original data)
+product_sugar_content = st.selectbox('Product Sugar Content', ['Low Sugar', 'Regular', 'No Sugar']) # Use actual categories
+product_type = st.selectbox('Product Type', ['Dairy', 'Soft Drinks', 'Meat', 'Fruits and Vegetables', 'Baking Goods', 'Health and Hygiene', 'Frozen Foods', 'Breads', 'Household', 'Snack Foods', 'Canned', 'Starchy Foods', 'Breakfast', 'Seafood', 'Others', 'Hard Drinks']) # Use actual categories
+store_id = st.selectbox('Store ID', ['OUT001', 'OUT002', 'OUT003', 'OUT004']) # Use actual categories
+store_size = st.selectbox('Store Size', ['Small', 'Medium', 'High']) # Use actual categories
+store_location_city_type = st.selectbox('Store Location City Type', ['Tier 1', 'Tier 2', 'Tier 3']) # Use actual categories
+store_type = st.selectbox('Store Type', ['Departmental Store', 'Supermarket Type1', 'Supermarket Type2', 'Food Mart']) # Use actual categories
+
+
+# Create a dictionary with the input data
+input_data = {
+    '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 a prediction when the user clicks a button
+if st.button('Predict Sales'):
+    try:
+        # Send the input data to the Flask backend for prediction
+        response = requests.post(f'{BACKEND_URL}/predict', json=input_data)
+
+        if response.status_code == 200:
+            prediction_result = response.json()
+            predicted_sales = prediction_result.get('prediction')
+            st.subheader('Predicted Sales:')
+            st.write(f'The predicted sales for this product in the specified store is: **{predicted_sales:.2f}**')
+        else:
+            st.error(f"Error from backend: {response.status_code} - {response.text}")
+
+    except requests.exceptions.ConnectionError:
+        st.error(f"Connection Error: Could not connect to the backend at {BACKEND_URL}. Please ensure the backend is running and the URL is correct.")
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
+
+st.markdown("---")
+st.markdown("Note: This is a basic frontend. You need to update the `BACKEND_URL` with the actual URL of your deployed Flask backend.")