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app.py

@@ -8,7 +8,7 @@ import os
 # Page configuration
 st.set_page_config(
     page_title=\"SuperKart Sales Predictor\",
-    page_icon=\"🛒\",
+    page_icon=\"\",
     layout=\"wide\",
     initial_sidebar_state=\"expanded\"
 )
@@ -16,7 +16,7 @@ st.set_page_config(
 # Get backend URL from environment or use default
 BACKEND_URL = os.getenv('BACKEND_URL', 'https://erikjacobs-superkart-backend.hf.space')
 
-st.title(\"🛒 SuperKart Sales Predictor\")
+st.title(\" SuperKart Sales Predictor\")
 st.write(\"Predict sales revenue based on product and store characteristics\")
 
 # Sidebar for navigation
@@ -38,16 +38,16 @@ def check_backend_health():
 health_status, health_info = check_backend_health()
 
 if not health_status:
-    st.error(f\"⚠️ Backend service is not available: {health_info}\")
+    st.error(f\" Backend service is not available: {health_info}\")
     st.info(\"The backend service may be starting up. Please wait a moment and refresh.\")
 else:
-    st.success(\"✅ Backend service is healthy\")
+    st.success(\" Backend service is healthy\")
 
 if page == \"Single Prediction\":
     st.header(\"Single Sales Prediction\")
-    
+
     col1, col2 = st.columns(2)
-    
+
     with col1:
         st.subheader(\"Product Information\")
         product_weight = st.number_input(\"Product Weight\", min_value=0.0, max_value=50.0, value=12.0)
@@ -59,17 +59,17 @@ if page == \"Single Prediction\":
             \"Hard Drinks\", \"Breakfast\", \"Snack Foods\", \"Bread\", \"Seafood\", \"Others\"
         ])
         product_mrp = st.number_input(\"Product MRP\", min_value=0.0, max_value=500.0, value=150.0)
-    
+
     with col2:
         st.subheader(\"Store Information\")
-        store_establishment_year = st.number_input(\"Store Establishment Year\", 
+        store_establishment_year = st.number_input(\"Store Establishment Year\",
                                                  min_value=1980, max_value=2024, value=2000)
         store_size = st.selectbox(\"Store Size\", [\"Small\", \"Medium\", \"High\"])
         store_location_city_type = st.selectbox(\"City Type\", [\"Tier 1\", \"Tier 2\", \"Tier 3\"])
         store_type = st.selectbox(\"Store Type\", [
             \"Supermarket Type1\", \"Supermarket Type2\", \"Departmental Store\", \"Food Mart\"
         ])
-    
+
     if st.button(\"Predict Sales\", type=\"primary\"):
         if not health_status:
             st.error(\"Cannot make prediction - backend service is not available\")
@@ -86,19 +86,19 @@ if page == \"Single Prediction\":
                 \"Store_Location_City_Type\": store_location_city_type,
                 \"Store_Type\": store_type
             }
-            
+
             try:
                 with st.spinner(\"Making prediction...\"):
-                    response = requests.post(f\"{BACKEND_URL}/predict\", 
-                                           json=prediction_data, 
+                    response = requests.post(f\"{BACKEND_URL}/predict\",
+                                           json=prediction_data,
                                            timeout=30)
-                
+
                 if response.status_code == 200:
                     result = response.json()
                     prediction = result['prediction']
-                    
-                    st.success(f\"🎯 Predicted Sales Revenue: ${prediction:,.2f}\")
-                    
+
+                    st.success(f\" Predicted Sales Revenue: ${prediction:,.2f}\")
+
                     # Display additional info
                     col1, col2, col3 = st.columns(3)
                     with col1:
@@ -107,48 +107,48 @@ if page == \"Single Prediction\":
                         st.metric(\"Model Type\", result.get('model_type', 'Unknown'))
                     with col3:
                         st.metric(\"Timestamp\", result.get('timestamp', 'Unknown'))
-                        
+
                 else:
                     st.error(f\"Prediction failed: {response.text}\")
-                    
+
             except Exception as e:
                 st.error(f\"Error making prediction: {str(e)}\")
 
 elif page == \"Model Info\":
     st.header(\"Model Information\")
-    
+
     if not health_status:
         st.error(\"Cannot get model info - backend service is not available\")
     else:
         try:
             response = requests.get(f\"{BACKEND_URL}/model_info\", timeout=10)
-            
+
             if response.status_code == 200:
                 model_info = response.json()
-                
+
                 col1, col2 = st.columns(2)
-                
+
                 with col1:
                     st.subheader(\"Model Details\")
                     st.write(f\"**Model Type:** {model_info.get('model_type', 'Unknown')}\")
                     st.write(f\"**Training Samples:** {model_info.get('training_samples', 'Unknown'):,}\")
                     st.write(f\"**Test Samples:** {model_info.get('test_samples', 'Unknown'):,}\")
-                
+
                 with col2:
                     st.subheader(\"Performance Metrics\")
                     st.write(f\"**R² Score:** {model_info.get('test_r2_score', 0):.4f}\")
                     st.write(f\"**RMSE:** {model_info.get('test_rmse', 0):.4f}\")
                     st.write(f\"**MAE:** {model_info.get('test_mae', 0):.4f}\")
-                
+
                 if 'features' in model_info:
                     st.subheader(\"Model Features\")
                     features = model_info['features']
                     st.write(f\"Total features: {len(features)}\")
                     st.write(features)
-                    
+
             else:
                 st.error(f\"Failed to get model info: {response.text}\")
-                
+
         except Exception as e:
             st.error(f\"Error getting model info: {str(e)}\")