Update app.py

app.py

@@ -1,62 +1,129 @@
 import streamlit as st
-import pandas as pd
-import requests # Import requests for API calls
+import requests
 
-# --- Configuration for Backend API --- #
-# This URL should point to your deployed backend Hugging Face Space
-BACKEND_API_URL = "https://lokiiparihar-superkart-api-t.hf.space" # Replace with your actual backend space URL
+# ----------------------------------
+# Backend API Configuration
+# ----------------------------------
+BACKEND_API_URL = "https://lokiiparihar-superkart-api-t.hf.space"
 PREDICT_ENDPOINT = f"{BACKEND_API_URL}/v1/sales"
 
-# Streamlit UI for Sales Prediction
+# ----------------------------------
+# Streamlit UI
+# ----------------------------------
 st.title("SuperKart Sales Prediction App")
-st.write("This tool predicts the sales revenue for a specific product in a SuperKart store.")
-
-st.subheader("Enter the product and store details:")
-
-# Collect user input for SuperKart sales prediction
-product_id = st.selectbox("Product ID Prefix", ['FD', 'NC', 'DR'])
-product_weight = st.number_input("Product Weight", min_value=0.0, value=12.0)
-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=0.05)
-product_type = st.selectbox("Product Type", ['Perishables', 'Non Perishables'])
-product_mrp = st.number_input("Product MRP", min_value=0.0, value=150.0)
-store_id = st.selectbox("Store ID", ['OUT004', 'OUT003', 'OUT001', 'OUT002'])
-store_size = st.selectbox("Store Size", ['Medium', 'High', 'Small'])
-store_location_city_type = st.selectbox("Store Location City Type", ['Tier 2', 'Tier 1', 'Tier 3'])
-store_current_age = st.number_input("Store Current Age (Years)", min_value=0, value=15)
-
-# Convert user input into a dictionary for JSON payload
+st.write(
+    "This tool predicts the sales revenue for a specific product in a SuperKart store "
+    "using a trained machine learning model."
+)
+
+st.subheader("Enter the product and store details")
+
+# ----------------------------------
+# User Inputs
+# ----------------------------------
+product_id = st.selectbox("Product ID Prefix", ["FD", "NC", "DR"])
+
+product_weight = st.number_input(
+    "Product Weight",
+    min_value=0.0,
+    value=12.0
+)
+
+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=0.05
+)
+
+product_type = st.selectbox(
+    "Product Type",
+    ["Perishables", "Non Perishables"]
+)
+
+product_mrp = st.number_input(
+    "Product MRP",
+    min_value=0.0,
+    value=150.0
+)
+
+store_id = st.selectbox(
+    "Store ID",
+    ["OUT004", "OUT003", "OUT001", "OUT002"]
+)
+
+store_size = st.selectbox(
+    "Store Size",
+    ["Small", "Medium", "High"]
+)
+
+store_location_city_type = st.selectbox(
+    "Store Location City Type",
+    ["Tier 1", "Tier 2", "Tier 3"]
+)
+
+store_type = st.selectbox(
+    "Store Type",
+    [
+        "Grocery Store",
+        "Supermarket Type1",
+        "Supermarket Type2",
+        "Supermarket Type3"
+    ]
+)
+
+store_current_age = st.number_input(
+    "Store Current Age (Years)",
+    min_value=0,
+    value=15
+)
+
+# ----------------------------------
+# Payload for API
+# ----------------------------------
 input_data = {
-    'Product_Id': product_id,
-    '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_Size': store_size,
-    'Store_Location_City_Type': store_location_city_type,
-    'Store_Current_Age': store_current_age
+    "Product_Id": product_id,
+    "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_Size": store_size,
+    "Store_Location_City_Type": store_location_city_type,
+    "Store_Type": store_type,                 # ✅ REQUIRED
+    "Store_Current_Age": store_current_age
 }
 
-# Predict button
+# ----------------------------------
+# Predict Button
+# ----------------------------------
 if st.button("Predict Sales"):
-    try:
-        # Make a POST request to the backend API
-        response = requests.post(PREDICT_ENDPOINT, json=input_data)
-        response.raise_for_status() # Raise an exception for HTTP errors (4xx or 5xx)
-
-        predicted_sales = response.json().get('Predicted Sales')
-        if predicted_sales is not None:
-            st.write(f"The predicted sales revenue is ${predicted_sales:.2f}.")
-        else:
-            st.error("Prediction failed: Unexpected response from backend.")
-            st.json(response.json()) # Display full response for debugging
-
-    except requests.exceptions.ConnectionError:
-        st.error("Could not connect to the backend API. Please ensure the backend Space is running and accessible.")
-    except requests.exceptions.RequestException as e:
-        st.error(f"An error occurred during the API request: {e}")
-        st.text(response.text) # Display raw response text for debugging
-    except Exception as e:
-        st.error(f"An unexpected error occurred: {e}")
+    with st.spinner("Predicting sales..."):
+        try:
+            response = requests.post(
+                PREDICT_ENDPOINT,
+                json=input_data,
+                timeout=10
+            )
+
+            if response.status_code == 200:
+                result = response.json()
+                st.success(
+                    f"Predicted Sales Revenue: ${result['Predicted Sales']:.2f}"
+                )
+            else:
+                st.error("Prediction failed")
+                st.json(response.json())
+
+        except requests.exceptions.ConnectionError:
+            st.error("Could not connect to backend API.")
+        except requests.exceptions.Timeout:
+            st.error("Request timed out. Please try again.")
+        except Exception as e:
+            st.error(f"Unexpected error: {e}")
+