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app.py

@@ -2,49 +2,37 @@ import streamlit as st
 import pandas as pd
 import requests
 
-# ---------------------------------------------------------------
-# 🎯 App Header
-# ---------------------------------------------------------------
-st.set_page_config(page_title="SuperKart Sales Predictor", page_icon="🛒")
-st.title("🛒 SuperKart Total Sales Prediction App")
-
+# Set the title of the Streamlit app
+st.title("SuperKart Total Sales Prediction App")
 st.markdown("""
-Welcome to the **SuperKart Sales Forecasting Tool**!  
-Predict total sales for a single product–store combination, or upload a CSV for batch forecasting.
+Welcome to the **SuperKart Sales Forecasting Tool**!
+Predict total sales for a product-store combination or upload a batch of product records for multi-store forecasting.
 """)
 
-# ---------------------------------------------------------------
-# 🧩 API Endpoint
-# ---------------------------------------------------------------
-BACKEND_URL = "https://rahulsuren12-TotalSalesPredictionBackend.hf.space"
-
-# ---------------------------------------------------------------
-# 🔹 Single Prediction
-# ---------------------------------------------------------------
-st.subheader("🔹 Online Prediction (Single Product)")
-
-col1, col2 = st.columns(2)
-
-with col1:
-    product_weight = st.number_input("Product Weight (kg)", min_value=0.0, step=0.1)
-    product_area = st.number_input("Product Allocated Area (sq. m.)", min_value=0.0, step=0.1)
-    product_mrp = st.number_input("Product MRP (₹)", min_value=0.0, step=0.1)
-    store_age = st.number_input("Store Age (years)", min_value=0, step=1)
-
-with col2:
-    product_sugar = st.selectbox("Product Sugar Content", ["Low", "Regular", "No Sugar"])
-    product_type = st.selectbox("Product Type", [
-        "Dairy", "Soft Drinks", "Snacks", "Frozen Foods", "Baking Goods",
-        "Household", "Others"
-    ])
-    store_size = st.selectbox("Store Size", ["Small", "Medium", "Large"])
-    store_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
-    store_type = st.selectbox("Store Type", [
-        "Supermarket Type1", "Supermarket Type2", "Departmental", "Grocery"
-    ])
-    store_id = st.selectbox("Store ID", ["ST001", "ST002", "ST003", "ST004"])
-
-# Prepare payload
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for property features
+product_weight = st.number_input("Product Weight (kg)", min_value=0.0, step=0.1)
+product_area = st.number_input("Product Allocated Area (sq. m.)", min_value=0.0, step=0.1)
+product_mrp = st.number_input("Product MRP (₹)", min_value=0.0, step=0.1)
+store_age = st.number_input("Store Age (years)", min_value=0, step=1)
+
+product_sugar = st.selectbox("Product Sugar Content", ["Low", "Regular", "No Sugar"])
+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", "Starchy Foods", "Seafood", "Others"
+])
+store_size = st.selectbox("Store Size", ["Small", "Medium", "Large"])
+store_city_type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox("Store Type", [
+    "Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"
+])
+store_id = st.selectbox("Store ID", ["ST001", "ST002", "ST003", "ST004"])
+
+
+# Convert the inputs into a dictionary for the backend
 input_data = {
     "Product_Weight": product_weight,
     "Product_Allocated_Area": product_area,
@@ -58,59 +46,15 @@ input_data = {
     "Store_Id": store_id
 }
 
-if st.button("🔮 Predict Sales"):
-    try:
-        response = requests.post(f"{BACKEND_URL}/v1/sales", json=input_data)
-        if response.status_code == 200:
-            result = response.json()
-            if "Predicted_Sales_Total" in result:
-                st.success(f"✅ Predicted Total Sales: ₹ {result['Predicted_Sales_Total']:,.2f}")
-            else:
-                st.error(f"⚠️ Unexpected response format: {result}")
-        else:
-            st.error(f"❌ Backend Error {response.status_code}: {response.text}")
-    except requests.exceptions.RequestException as e:
-        st.error(f"🚫 Connection failed: {e}")
-
-# ---------------------------------------------------------------
-# 🔹 Batch Prediction
-# ---------------------------------------------------------------
-st.subheader("📦 Batch Prediction (CSV Upload)")
-
-st.markdown("Upload a CSV file containing multiple product–store records for batch sales predictions.")
-
-uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"])
-
-if uploaded_file is not None:
-    if st.button("📈 Predict Batch Sales"):
-        try:
-            # Create proper file payload
-            files = {"file": (uploaded_file.name, uploaded_file.getvalue(), "text/csv")}
-            response = requests.post(f"{BACKEND_URL}/v1/salesbatch", files=files)
-
-            if response.status_code == 200:
-                result = response.json()
-                if isinstance(result, dict) and "error" not in result:
-                    st.success("✅ Batch predictions completed successfully!")
-                    results_df = pd.DataFrame(list(result.items()), columns=["Product_Id", "Predicted_Sales_Total"])
-                    st.dataframe(results_df, use_container_width=True)
-                    csv_buffer = io.BytesIO()
-                    results_df.to_csv(csv_buffer, index=False)
-                    st.download_button(
-                        label="⬇️ Download Predictions as CSV",
-                        data=csv_buffer.getvalue(),
-                        file_name="SuperKart_Batch_Predictions.csv",
-                        mime="text/csv"
-                    )
-                else:
-                    st.error(f"⚠️ Backend returned an error: {result.get('error', 'Unknown issue')}")
-            else:
-                # Non-200 HTTP status
-                try:
-                    err_json = response.json()
-                    st.error(f"❌ Error {response.status_code}: {err_json.get('error', response.text)}")
-                except Exception:
-                    st.error(f"❌ Error {response.status_code}: {response.text}")
-
-        except requests.exceptions.RequestException as e:
-            st.error(f"🚫 Network or connection error: {e}")
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict Sales"):
+  # Validate inputs before sending
+  if product_weight == 0 or product_area == 0 or product_mrp == 0:
+    st.warning("Please enter valid values for product details before predicting.")
+  else:
+    response = requests.post("https://rahulsuren12-TotalSalesPredictionBackend.hf.space/v1/sales", json=input_data)  # Send data to Flask API
+    if response.status_code == 200:
+        prediction = response.json()['Predicted_Sales_Total']
+        st.success(f"Predicted Total Sales: ₹ {prediction:,.2f}")
+    else:
+        st.error("Error making prediction.")