Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import requests # Import requests for API calls
+
+# --- Configuration for Backend API --- #
+# This URL should point to your deployed backend Hugging Face Space
+BACKEND_API_URL = "https://vyasmax9-superkartsalesbackend.hf.space" # Replace with your actual backend space URL
+PREDICT_ENDPOINT = f"{BACKEND_API_URL}/v1/sales"
+
+# Streamlit UI for Sales Prediction
+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
+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
+}
+
+# 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}")