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app.py

@@ -1,21 +1,13 @@
 import streamlit as st
 import pandas as pd
-import joblib
-import numpy as np
+import requests
 from datetime import datetime
 
-# Load the trained model
-@st.cache_resource
-def load_model():
-    return joblib.load("SuperKart_model_v1_0.joblib")
+# Set the title of the Streamlit app
+st.title("Retail Product Sales Prediction")
 
-model = load_model()
-
-# Streamlit UI for Sales Prediction
-st.title("Retail Product Sales Prediction App")
-st.write("This tool predicts the total sales for a product in a retail store based on product and store characteristics.")
-
-st.subheader("Enter the product and store details:")
+# Section for single prediction
+st.subheader("Single Product-Store Prediction")
 
 # Create two columns for better layout
 col1, col2 = st.columns(2)
@@ -46,67 +38,85 @@ with col2:
         "Departmental Store", "Food Mart"
     ])
 
-# Calculate derived features
-store_age = current_year - establishment_year
-product_density = product_weight / (product_area + 1e-6)
-price_per_weight = product_mrp / (product_weight + 1e-6)
-
-# Convert user input into a DataFrame
-input_data = pd.DataFrame([{
-    'Product_Weight': product_weight,
-    'Product_Sugar_Content': product_sugar,
-    'Product_Allocated_Area': product_area,
-    'Product_Type': product_type,
-    'Product_MRP': product_mrp,
-    'Store_Id': store_id,
-    'Store_Establishment_Year': establishment_year,
-    'Store_Size': store_size,
-    'Store_Location_City_Type': city_type,
-    'Store_Type': store_type,
-    'Store_Age': store_age,
-    'Product_Density': product_density,
-    'Price_Per_Unit_Weight': price_per_weight,
-    'Product_Size_Category': 'Small' if product_weight <= 10 else ('Medium' if product_weight <= 15 else 'Large'),
-    'Store_Tier_Size': f"{city_type}_{store_size}"
-}])
+# Prepare input data
+input_data = {
+    "product_weight": product_weight,
+    "product_sugar_content": product_sugar,
+    "product_allocated_area": product_area,
+    "product_type": product_type,
+    "product_mrp": product_mrp,
+    "store_id": store_id,
+    "store_establishment_year": establishment_year,
+    "store_size": store_size,
+    "store_location_city_type": city_type,
+    "store_type": store_type
+}
 
-# Predict button
+# Make prediction when the "Predict" button is clicked
 if st.button("Predict Sales"):
     try:
-        prediction = model.predict(input_data)
-        st.success(f"Predicted Sales Total: ${prediction[0]:.2f}")
-        
-        # Show some statistics
-        st.subheader("Statistics")
-        st.write(f"Store Age: {store_age} years")
-        st.write(f"Product Density: {product_density:.2f} kg/sqm")
-        st.write(f"Price per Unit Weight: ${price_per_weight:.2f}/kg")
-        
+        response = requests.post("http://DD009-SuperKartBackend/v1/sales", json=input_data)
+        if response.status_code == 200:
+            result = response.json()
+            st.success(f"Predicted Sales Total: ${result['predicted_sales']}")
+            
+            # Display additional metrics
+            st.markdown("**Additional Metrics**")
+            st.write(f"- Store Age: {result['store_age']} years")
+            st.write(f"- Product Density: {result['product_density']} kg/sqm")
+            st.write(f"- Price per Unit Weight: ${result['price_per_weight']}/kg")
+        else:
+            st.error(f"Error making prediction: {response.json().get('error', 'Unknown error')}")
     except Exception as e:
-        st.error(f"Error in prediction: {str(e)}")
+        st.error(f"Connection error: {str(e)}")
+
+# Section for batch prediction
+st.subheader("Batch Prediction")
+st.write("Upload a CSV file containing multiple product-store combinations")
+
+# File uploader for batch predictions
+uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
 
-# Add some helpful information
-st.sidebar.markdown("""
-**Data Statistics:**
-- Average Product Weight: 12.65 kg
-- Average Allocated Area: 0.07 sqm
-- Average MRP: $147.03
-- Average Sales: $3464.00
-""")
+if uploaded_file is not None:
+    if st.button("Predict Batch Sales"):
+        try:
+            files = {'file': uploaded_file.getvalue()}
+            response = requests.post("http://DD009-SuperKartBackend/v1/salesbatch", files=files)
+            
+            if response.status_code == 200:
+                results = response.json()['predictions']
+                results_df = pd.DataFrame(results)
+                
+                st.success("Batch predictions completed!")
+                st.dataframe(results_df)
+                
+                # Download button for results
+                csv = results_df.to_csv(index=False)
+                st.download_button(
+                    label="Download predictions as CSV",
+                    data=csv,
+                    file_name='sales_predictions.csv',
+                    mime='text/csv'
+                )
+            else:
+                st.error(f"Error making predictions: {response.json().get('error', 'Unknown error')}")
+        except Exception as e:
+            st.error(f"Connection error: {str(e)}")
 
-# Add a sample data button
+# Add sample data section
+st.sidebar.markdown("### Sample Data")
 if st.sidebar.button("Load Sample Data"):
     sample_data = {
-        'Product_Weight': 12.66,
-        'Product_Sugar_Content': "Low Sugar",
-        'Product_Allocated_Area': 0.027,
-        'Product_Type': "Frozen Foods",
-        'Product_MRP': 117.08,
-        'Store_Id': "OUT004",
-        'Store_Establishment_Year': 2009,
-        'Store_Size': "Medium",
-        'Store_Location_City_Type': "Tier 2",
-        'Store_Type': "Supermarket Type2"
+        "product_weight": 12.66,
+        "product_sugar_content": "Low Sugar",
+        "product_allocated_area": 0.027,
+        "product_type": "Frozen Foods",
+        "product_mrp": 117.08,
+        "store_id": "OUT004",
+        "store_establishment_year": 2009,
+        "store_size": "Medium",
+        "store_location_city_type": "Tier 2",
+        "store_type": "Supermarket Type2"
     }
     
     # Update all widgets with sample data