Indentation Error Fix

streamlit_app.py

@@ -7,98 +7,98 @@ import streamlit as st
 # ------------------------
 # Page & header
 # ------------------------
-st.set_page_config(page_title=\"SuperKart Sales Forecasting\", page_icon=\"🛒\", layout=\"centered\")
-st.title(\"🛒 SuperKart Sales Forecasting\")
-st.caption(\"Enter details, call your backend /predict endpoint, and see the forecasted Product_Store_Sales_Total.\")
+st.set_page_config(page_title="SuperKart Sales Forecasting", page_icon="🛒", layout="centered")
+st.title("🛒 SuperKart Sales Forecasting")
+st.caption("Enter details, call your backend /predict endpoint, and see the forecasted Product_Store_Sales_Total.")
 
 # ------------------------
 # Sidebar: backend URL
 # ------------------------
-default_api = os.environ.get(\"SUPERKART_API_URL\", \"http://localhost:7860\")
+default_api = os.environ.get("SUPERKART_API_URL", "http://localhost:7860")
 api_base = st.sidebar.text_input(
-    \"Backend API base URL\",
+    "Backend API base URL",
     value=default_api,
-    help=\"Example: https://<username>-superkart-forecasting.hf.space or http://localhost:7860\",
+    help="Example: https://<username>-superkart-forecasting.hf.space or http://localhost:7860",
 )
 
-st.sidebar.info(\"Tip: Ensure backend is running and category labels match training (e.g., 'Supermarket Type2').\")
+st.sidebar.info("Tip: Ensure backend is running and category labels match training (e.g., 'Supermarket Type2').")
 
 # ------------------------
 # Form: inputs
 # ------------------------
-with st.form(key=\"predict_form\"):
-    st.subheader(\"Input Features\")
+with st.form(key="predict_form"):
+    st.subheader("Input Features")
 
     col1, col2 = st.columns(2)
 
     with col1:
-        product_weight = st.number_input(\"Product_Weight\", min_value=0.0, value=12.5, step=0.1)
-        product_allocated_area = st.number_input(\"Product_Allocated_Area\", min_value=0.0, max_value=1.0, value=0.08, step=0.01)
-        product_mrp = st.number_input(\"Product_MRP\", min_value=0.0, value=150.0, step=1.0)
-        store_est_year = st.number_input(\"Store_Establishment_Year\", min_value=1950, max_value=2025, value=2002, step=1)
+        product_weight = st.number_input("Product_Weight", min_value=0.0, value=12.5, step=0.1)
+        product_allocated_area = st.number_input("Product_Allocated_Area", min_value=0.0, max_value=1.0, value=0.08, step=0.01)
+        product_mrp = st.number_input("Product_MRP", min_value=0.0, value=150.0, step=1.0)
+        store_est_year = st.number_input("Store_Establishment_Year", min_value=1950, max_value=2025, value=2002, step=1)
 
     with col2:
-        sugar = st.selectbox(\"Product_Sugar_Content\", [\"Low Sugar\", \"Regular\", \"No Sugar\"])
+        sugar = st.selectbox("Product_Sugar_Content", ["Low Sugar", "Regular", "No Sugar"])
         ptype = st.selectbox(
-            \"Product_Type\",
+            "Product_Type",
             [
-                \"Meat\", \"Snack Foods\", \"Hard Drinks\", \"Dairy\", \"Canned\", \"Soft Drinks\",
-                \"Health and Hygiene\", \"Baking Goods\", \"Bread\", \"Breakfast\", \"Frozen Foods\",
-                \"Fruits and Vegetables\", \"Household\", \"Seafood\", \"Starchy Foods\", \"Others\",
+                "Meat", "Snack Foods", "Hard Drinks", "Dairy", "Canned", "Soft Drinks",
+                "Health and Hygiene", "Baking Goods", "Bread", "Breakfast", "Frozen Foods",
+                "Fruits and Vegetables", "Household", "Seafood", "Starchy Foods", "Others",
             ],
             index=1,
         )
-        store_id = st.selectbox(\"Store_Id\", [\"OUT001\", \"OUT002\", \"OUT003\", \"OUT004\"] , index=3)
-        store_size = st.selectbox(\"Store_Size\", [\"Low\", \"Medium\", \"High\"] , index=1)
-        city_type = st.selectbox(\"Store_Location_City_Type\", [\"Tier 1\", \"Tier 2\", \"Tier 3\"] , index=1)
+        store_id = st.selectbox("Store_Id", ["OUT001", "OUT002", "OUT003", "OUT004"] , index=3)
+        store_size = st.selectbox("Store_Size", ["Low", "Medium", "High"] , index=1)
+        city_type = st.selectbox("Store_Location_City_Type", ["Tier 1", "Tier 2", "Tier 3"] , index=1)
         store_type = st.selectbox(
-            \"Store_Type\",
-            [\"Departmental Store\", \"Supermarket Type1\", \"Supermarket Type2\", \"Food Mart\"] ,
+            "Store_Type",
+            ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"] ,
             index=2,
         )
 
-    submitted = st.form_submit_button(\"🔮 Predict\")
+    submitted = st.form_submit_button("🔮 Predict")
 
 # ------------------------
 # Build payload preview
 # ------------------------
 payload = {
-    \"records\": [
+    "records": [
         {
-            \"Product_Weight\": product_weight,
-            \"Product_Allocated_Area\": product_allocated_area,
-            \"Product_MRP\": product_mrp,
-            \"Product_Sugar_Content\": sugar,
-            \"Product_Type\": ptype,
-            \"Store_Id\": store_id,
-            \"Store_Size\": store_size,
-            \"Store_Location_City_Type\": city_type,
-            \"Store_Type\": store_type,
-            \"Store_Establishment_Year\": int(store_est_year),
+            "Product_Weight": product_weight,
+            "Product_Allocated_Area": product_allocated_area,
+            "Product_MRP": product_mrp,
+            "Product_Sugar_Content": sugar,
+            "Product_Type": ptype,
+            "Store_Id": store_id,
+            "Store_Size": store_size,
+            "Store_Location_City_Type": city_type,
+            "Store_Type": store_type,
+            "Store_Establishment_Year": int(store_est_year),
         }
     ]
 }
-st.markdown(\"#### Request Preview\")
-st.code(json.dumps(payload, indent=2), language=\"json\")
+st.markdown("#### Request Preview")
+st.code(json.dumps(payload, indent=2), language="json")
 
 # ------------------------
 # Call backend on submit
 # ------------------------
 if submitted:
     try:
-        url = api_base.rstrip(\"/\") + \"/predict\"
-        with st.spinner(\"Calling backend…\"):
+        url = api_base.rstrip("/") + "/predict"
+        with st.spinner("Calling backend…"):
             t0 = time.time()
             resp = requests.post(url, json=payload, timeout=45)
             dt_ms = int((time.time() - t0) * 1000)
 
         if resp.status_code == 200:
             data = resp.json()
-            pred = data.get(\"predictions\", [None])[0]
-            st.success(f\"✅ Predicted Product_Store_Sales_Total: **{pred:.2f}**  \\n⏱ Response time: {dt_ms} ms\")
+            pred = data.get("predictions", [None])[0]
+            st.success(f"✅ Predicted Product_Store_Sales_Total: **{pred:.2f}**  \\n⏱ Response time: {dt_ms} ms")
         else:
-            st.error(f\"❌ Request failed ({resp.status_code}): {resp.text}\")
+            st.error(f"❌ Request failed ({resp.status_code}): {resp.text}")
     except Exception as e:
-        st.error(f\"❌ Error contacting API: {e}\")
+        st.error(f"❌ Error contacting API: {e}")
 
-st.caption(\"Keep category labels consistent with training (e.g., 'Supermarket Type2').\")
+st.caption("Keep category labels consistent with training (e.g., 'Supermarket Type2').")