Update app.py

app.py

@@ -21,6 +21,17 @@ import streamlit as st
 import os
 
 st.title('Supply Chain Causal Analysis')
+
+st.write("""Supply Chain Causal Analysis Model: 
+            This TensorFlow-powered model utilizes advanced machine learning techniques to analyze and predict causal relationships 
+            among key factors in a supply chain, including product demand, lead time, in stock count, pricing, advertising, weather, 
+            and backorder status.            
+            By uncovering these causal relationships, the model enables businesses to optimize their supply chain operations, reduce costs,
+            and improve customer satisfaction. 
+            Developed using TensorFlow, a powerful deep learning framework, this model offers accurate and efficient insights 
+            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')
 # loading the save model
 model = tf.keras.models.load_model(os.path.join('Weights_Updated','Best_model.tf'), compile=False) 
@@ -33,4 +44,109 @@ 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"))
+    if Product=='Product A':
+        Product=0
+    elif Product=="Product B":
+        Product=1
+    elif Product=="Product C": 
+        Product=2
+    else:
+        Product=4
+    # For Lead_time
+    Lead_time = st.sidebar.slider('Lead_time', 1,25,9)
+    # For Demand
+    Demand = st.sidebar.slider('Demand', 20,182,105)
+    # For In_stock
+    In_stock = st.sidebar.slider('In_stock', 20,250,219)
+    # For Price
+    Price = st.sidebar.slider('Price', 10,100,64)
+    # For Advertising
+    Advertising = st.sidebar.slider('Advertising', 1000,4500,2364)
+    # For Weather
+    Weather = st.sidebar.slider('Weather', 30,110,71)
+
+    # Create a DataFrame for the input data
+    user_report_data = {'Product': [Product],
+                        'Lead_time': [Lead_time],
+                        'Demand': [Demand],
+                        'In_stock': [In_stock],
+                        'Price': [Price],
+                        'Advertising': [Advertising],
+                        'Weather': [Weather]}
+
+    # # encoded the Product using loaded product label encoder object
+    # le_product_encoded = le_product.transform([Product])[0]
+    
+    # # scaling the input_data using loaded scaler object
+    # report_data = scaler.transform(input_data)
+
+    report_data = pd.DataFrame(user_report_data, index=[0])
+    return report_data 
+
+    
+# Supply Chain Data Details
+user_data = user_report()
+st.subheader("Supply Chain Data Details")
+st.write(user_data)
+
+# User_function 
+def predict_backordered(user_data):
+
+    df = pd.read_csv('Supply_chain_causal_analysis_Synthetic_Dataset_Final.csv')
+
+    # # encoded the Product using loaded product label encoder object
+    # Product = le_product.transform([Product])[0]
+
+    # scaling the input_data using loaded scaler object
+    user_data = scaler.transform(user_data)
+    
+    # Make predictions using the pre-trained TensorFlow model
+    predictions = model.predict(user_data)
+    if predictions == 1:
+      return "Backorders are likely to occur."
+    else:
+      return "Backorders are unlikely to occur."
+
+# Function calling
+y_pred = predict_backordered(user_data)
+if st.button("Predict"):
+    st.subheader(y_pred)
+
+
+st.write("""Features Used:
+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.
+2: Lead_time: The average number of days taken to deliver the product after placing the order.
+3: Demand: The number of units of the product demanded during a specific time period.
+4: In_stock: The number of units of the product currently available in the inventory.
+5: Price: The selling price of the product.
+6: Advertising: The amount spent on advertising the product during a specific time period.
+7: Weather: Weather condition during a specific time period that could affect the demand for the 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 was backordered (1) or not (0) during a specific 
+             time period. This is the target variable that we want to predict""")
+
+
+
+
+# # user_data = user_report()
+# # st.subheader("Component Details")
+# # st.write(user_data)
+
+# # Function calling
+# y_pred = prediction(user_data)
+# st.write("Click here to see the Predictions")
+# if st.button("Predict"):
+#     st.subheader(f"Next Failure is {y_pred} hours ")
+
+# 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)