Upload folder using huggingface_hub

app.py

@@ -4,12 +4,15 @@ import pandas as pd
 import requests
 import altair as alt
 
-st.set_page_config(page_title="SuperKart Sales Predictor", layout="wide")
+from predictor.batch_handler import get_predictions, get_single_prediction
+from predictor.utils import reorder_columns, get_csv_download
+from predictor.chart_plotter import plot_actual_vs_predicted
 
+st.set_page_config(page_title="SuperKart Sales Predictor", layout="wide")
 st.title("🛒 SuperKart Sales Predictor")
 st.markdown("Use the tabs below to predict sales for a single product or upload a CSV file for batch prediction.")
 
-# Create two tabs
+# Create tabs
 tab1, tab2 = st.tabs(["🔍 Single Prediction", "📄 Batch Prediction"])
 
 # ----------------- Tab 1: Single Prediction -----------------
@@ -38,31 +41,29 @@ with tab1:
             ])
             est_year = st.slider("Establishment Year", 1987, 2009, 2002)
 
-    # Submit action
+    # Submit button
     if st.button("🎯 Predict Sales 🎯"):
+        payload = {
+            "Product_Weight": product_weight,
+            "Product_Allocated_Area": product_allocated_area,
+            "Product_MRP": product_mrp,
+            "Product_Sugar_Content": product_sugar_content,
+            "Product_Type": product_type,
+            "Store_Id": store_id,
+            "Store_Size": store_size,
+            "Store_Location_City_Type": store_location,
+            "Store_Type": store_type,
+            "Store_Establishment_Year": est_year
+        }
+
         try:
-            payload = {
-                "Product_Weight": product_weight,
-                "Product_Allocated_Area": product_allocated_area,
-                "Product_MRP": product_mrp,
-                "Product_Sugar_Content": product_sugar_content,
-                "Product_Type": product_type,
-                "Store_Id": store_id,
-                "Store_Size": store_size,
-                "Store_Location_City_Type": store_location,
-                "Store_Type": store_type,
-                "Store_Establishment_Year": est_year
-            }
-
-            try:
-                prediction = get_single_prediction(payload)
-                st.success(f"✅ Predicted Sales: ₹{prediction:,.2f}")
-                st.json({**payload, "Predicted_Sales": prediction})
-            except Exception as e:
-                st.error(f"⚠️ Error during prediction: {e}")
+            prediction = get_single_prediction(payload)
+            st.success(f"✅ Predicted Sales: ₹{prediction:,.2f}")
+            st.json({**payload, "Predicted_Sales": prediction})
+        except Exception as e:
+            st.error(f"⚠️ Error during prediction: {e}")
 
 # ----------------- Tab 2: Batch Prediction -----------------
-# 📄 TAB 2: Batch Prediction
 with tab2:
     st.subheader("📄 Upload CSV for Batch Prediction")
     uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
@@ -95,5 +96,5 @@ with tab2:
                 st.dataframe(df)
                 plot_actual_vs_predicted(df)
 
-        except Exception as e:  # ✅ Required block
+        except Exception as e:
             st.error(f"⚠️ Error while processing the file: {e}")