Update app.py

app.py

@@ -12,6 +12,7 @@ from sklearn.preprocessing import MinMaxScaler
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense
 import matplotlib.pyplot as plt
+import seaborn as sns
 import pickle
 
 import warnings
@@ -32,7 +33,8 @@ st.write("""Supply Chain Causal Analysis Model:
             into the complex dynamics of supply chain operations, empowering businesses to make data-driven decisions and drive 
             operational excellence""")
 
-st.sidebar.header('Supply Chain Data')
+st.sidebar.header('Supply Chain Features')
+
 # loading the save model
 model = tf.keras.models.load_model(os.path.join('Weights_Updated','Best_model.tf'), compile=False) 
 
@@ -44,10 +46,14 @@ with open ('le_product.pkl','rb') as file:
 with open ('scaler_scca.pkl','rb') as file1:
   scaler = pickle.load(file1)
 
-# DATA from user
+
 def user_report():
-    # For Product
-    Product = st.sidebar.selectbox("Product Name",("Product A", "Product B","Product C","Product D"))
+    
+    # # For Product
+    
+    st.sidebar.write("**1: Product Name**")
+    st.sidebar.write("Name of the Product")
+    Product = st.sidebar.selectbox("",("Product A", "Product B","Product C","Product D"))
     if Product=='Product A':
         Product=0
     elif Product=="Product B":
@@ -55,19 +61,42 @@ def user_report():
     elif Product=="Product C": 
         Product=2
     else:
-        Product=3
+        Product=4
+        
     # For Lead_time
-    Lead_time = st.sidebar.slider('Lead_time', 1,25,9)
+    st.sidebar.write("**2: Lead_time**")
+    st.sidebar.write("The average number of days taken to deliver the product after placing the order.")
+    Lead_time = st.sidebar.slider('', 1,25,9)
+
+    
     # For Demand
-    Demand = st.sidebar.slider('Demand', 20,182,105)
+    st.sidebar.write("**3: Demand**")
+    st.sidebar.write("The number of units of the product demanded during a specific time period.")
+    Demand = st.sidebar.slider('', 20,182,105)
+
+    
     # For In_stock
-    In_stock = st.sidebar.slider('In_stock', 20,250,219)
+    st.sidebar.write("**4: In_stock**")
+    st.sidebar.write("The number of units of the product currently available in the inventory.")
+    In_stock = st.sidebar.slider('', 20,250,219)
+
+    
     # For Price
-    Price = st.sidebar.slider('Price', 10,100,64)
+    st.sidebar.write("**5: Price**")
+    st.sidebar.write("The selling price of the product.")
+    Price = st.sidebar.slider('', 10,100,64)
+
+    
     # For Advertising
-    Advertising = st.sidebar.slider('Advertising', 1000,4500,2364)
+    st.sidebar.write("**6: Advertising**")
+    st.sidebar.write("The amount spent on advertising the product during a specific time period.")
+    Advertising = st.sidebar.slider('', 1000,4500,2364)
+
+    
     # For Weather
-    Weather = st.sidebar.slider('Weather', 30,110,71)
+    st.sidebar.write("**7: Weather**")
+    st.sidebar.write("Weather condition during a specific time period that could affect the demand for the product.")
+    Weather = st.sidebar.slider('', 30,110,71)
 
     # Create a DataFrame for the input data
     user_report_data = {'Product': [Product],
@@ -77,6 +106,8 @@ def user_report():
                         'Price': [Price],
                         'Advertising': [Advertising],
                         'Weather': [Weather]}
+    
+
 
     # # encoded the Product using loaded product label encoder object
     # le_product_encoded = le_product.transform([Product])[0]
@@ -90,7 +121,7 @@ def user_report():
     
 # Supply Chain Data Details
 user_data = user_report()
-st.subheader("Supply Chain Data Details")
+st.subheader("Selected Values of Supply Chain Features")
 st.write(user_data)
 
 # User_function 
@@ -111,47 +142,58 @@ def predict_backordered(user_data):
     else:
       return "Backorders are unlikely to occur."
 
-# Function calling
+        
+# CSS code for changing color of the button
+st.markdown("""
+    <style>
+    .stButton button {
+        background-color: #668f45;                        
+        color: white;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+
+# predictions
 y_pred = predict_backordered(user_data)
-if st.button("Predict"):
+if st.button("Predict Probability of the Product being Backordered"):
     st.subheader(y_pred)
 
 
-st.write("""Features Used:
+# Display the title
+st.title("Deployment in Real-World Scenarios")
+# Display the title image
+st.image("pasteImg.png", use_column_width=True)
 
-The following are the input Varibles from the End user which needs to be enter, and then the application will predict whether 
-the particular Product has the chances of having Backorder or not.
 
-1: Product: Name of the product.
+# background-color: lightgreen;   (in CSS)
 
-2: Lead_time: The average number of days taken to deliver the product after placing the order.
+# st.write("""Features Used:
 
-3: Demand: The number of units of the product demanded during a specific time period.
+# The following are the input Varibles from the End user which needs to be enter, and then the application will predict whether 
+# the particular Product has the chances of having Backorder or not.
 
-4: In_stock: The number of units of the product currently available in the inventory.
+# 1: Product: Name of the product.
 
-5: Price: The selling price of the product.
+# 2: Lead_time: The average number of days taken to deliver the product after placing the order.
 
-6: Advertising: The amount spent on advertising the product during a specific time period.
+# 3: Demand: The number of units of the product demanded during a specific time period.
 
-7: Weather: Weather condition during a specific time period that could affect the demand for the product.
+# 4: In_stock: The number of units of the product currently available in the inventory.
 
-In a retail scenario, weather could be measured in terms of temperature in Fahrenheit or Celsius,
-and since temperature affects the demand for products such as clothing, food, and beverages. It is also one of the important factor
-to be considered for causal analysis of Supply chain management.
+# 5: Price: The selling price of the product.
 
-Target Column/Prediction: 
-Backordered: A binary variable indicating whether the product will be backordered (1) or not (0) during a specific 
-             time period. This is the target variable that we want to predict
+# 6: Advertising: The amount spent on advertising the product during a specific time period.
 
-Backorder refers to a situation where a requested item is not immediately available due to insufficient inventory or other factors. 
-It can impact customer satisfaction, result in delayed deliveries, and potentially lead to revenue loss. Businesses need to manage 
-backorders effectively by monitoring inventory levels, optimizing supply chain processes, and providing timely updates to customers. 
-Backorders can also be used strategically as a marketing tactic, but must be handled carefully to avoid customer disappointment and 
-maintain a positive reputation.
+# 7: Weather: Weather condition during a specific time period that could affect the demand for the product.
 
-In short, Backorder is one of the most important factor in terms of supply chain which means when a particular order goes of out of 
-stock and demand comes for that out of stock product.""")
+# In a retail scenario, weather could be measured in terms of temperature in Fahrenheit or Celsius,
+# and since temperature affects the demand for products such as clothing, food, and beverages. It is also one of the important factor
+# to be considered for causal analysis of Supply chain management.
+
+# Target Column/Prediction: 
+# Backordered: A binary variable indicating whether the product will be backordered (1) or not (0) during a specific 
+#              time period. This is the target variable that we want to predict""")
 
 
 
@@ -166,7 +208,5 @@ stock and demand comes for that out of stock product.""")
 # if st.button("Predict"):
 #     st.subheader(f"Next Failure is {y_pred} hours ")
 
-# Test these data
 # Product D, 9.0, 105.0, 219.0, 64.0, 2364.0, 71.24 - for this 0 (Backorders are unlikely to occur)
-# #predict_backordered('Product C', 5.0, 105.0, 177.0, 38.0, 1598.0, 83.31) - for this 1 (Backorders are likely to occur)
-
+# #predict_backordered('Product C', 5.0, 105.0, 177.0, 38.0, 1598.0, 83.31) - for this 1 (Backorders are likely to occur)