Update app.py

app.py

@@ -1,86 +1,81 @@
-import joblib
 import gradio as gr
 import numpy as np
+import joblib
 from datetime import datetime, timedelta
 
-# Load the saved model and encoders
+# Load model and label encoders
 model = joblib.load("best_model.pkl")
 le_operation_type = joblib.load("le_operation_type.pkl")
 le_operator_id = joblib.load("le_operator_id.pkl")
 
-def predict_end_date(start_date, quantity, operation_type, operator_id, day_of_week):
-    # Convert input types
-    quantity = float(quantity)
-    day_of_week = int(day_of_week)
+def predict_end_date(start_date_str, quantity, operation_type, operator_id, day_of_week):
+    try:
+        start_date = datetime.strptime(start_date_str, "%d-%m-%Y")
+    except ValueError:
+        return "Error: Start Date must be in DD-MM-YYYY format"
 
-    # Use label encoders to convert strings to codes
-    operation_code = le_op.transform([operation_type])[0]
-    operator_code = le_opr.transform([operator_id])[0]
+    # Encode categorical inputs
+    try:
+        operation_code = le_operation_type.transform([operation_type])[0]
+    except ValueError:
+        return f"Unknown Operation Type: {operation_type}"
+
+    try:
+        operator_code = le_operator_id.transform([operator_id])[0]
+    except ValueError:
+        return f"Unknown Operator ID: {operator_id}"
 
     # Feature engineering
     quantity_log = np.log1p(quantity)
     quantity_log_squared = quantity_log ** 2
-    quantity_log_cubed = quantity_log ** 3
     quantity_operation_interaction = quantity_log * operation_code
     quantity_operator_interaction = quantity_log * operator_code
-    quantity_operator_interaction_squared = quantity_operator_interaction ** 2
-    operation_operator_interaction = operation_code * operator_code
 
-    X = pd.DataFrame([[
+    # Create feature vector for prediction
+    X = np.array([[
         quantity_log,
         quantity_log_squared,
-        quantity_log_cubed,
         quantity_operation_interaction,
         quantity_operator_interaction,
-        quantity_operator_interaction_squared,
-        operation_operator_interaction,
         operation_code,
         operator_code,
         day_of_week
-    ]], columns=[
-        'Quantity_log',
-        'Quantity_log_squared',
-        'Quantity_log_cubed',
-        'Quantity_Operation_interaction',
-        'Quantity_Operator_interaction',
-        'Quantity_Operator_interaction_squared',
-        'Operation_Operator_interaction',
-        'Operation_Code',
-        'Operator_Code',
-        'DayOfWeek'
-    ])
+    ]])
 
     pred_days = model.predict(X)[0]
 
-    # Convert predicted days to readable date + time
-    from datetime import datetime, timedelta
-    start_dt = datetime.strptime(start_date, "%d-%m-%Y")
-    end_dt = start_dt + timedelta(days=pred_days)
+    whole_days = int(pred_days)
+    fractional_day = pred_days - whole_days
+
+    total_seconds = fractional_day * 24 * 3600
+    hours = int(total_seconds // 3600)
+    minutes = int((total_seconds % 3600) // 60)
 
-    total_minutes = pred_days * 24 * 60
-    days = int(total_minutes // (24 * 60))
-    hours = int((total_minutes % (24 * 60)) // 60)
-    minutes = int(total_minutes % 60)
+    est_end_datetime = start_date + timedelta(days=whole_days, hours=hours, minutes=minutes)
+    est_end_date_str = est_end_datetime.strftime("%d-%m-%Y %H:%M")
+    duration_str = f"{whole_days} days, {hours} hours, {minutes} minutes"
 
-    return f"Estimated end date: {end_dt.strftime('%d-%m-%Y')} ({days} days {hours} hours {minutes} minutes)"
+    return f"Estimated End Date & Time: {est_end_date_str}\nEstimated Duration: {duration_str}"
 
+# Build Gradio interface
+def gradio_interface(start_date, quantity, operation_type, operator_id, day_of_week):
+    return predict_end_date(start_date, quantity, operation_type, operator_id, day_of_week)
 
-# Get all options for dropdowns from the encoders
-operation_types = le_operation_type.classes_.tolist()
-operator_ids = le_operator_id.classes_.tolist()
+operation_options = list(le_operation_type.classes_)
+operator_options = list(le_operator_id.classes_)
 
 iface = gr.Interface(
-    fn=predict_end_date,
+    fn=gradio_interface,
     inputs=[
-        gr.Textbox(label="Start Date (DD-MM-YYYY)", value="01-01-2025"),
-        gr.Number(label="Quantity", value=1),
-        gr.Dropdown(operation_types, label="Operation Type"),
-        gr.Dropdown(operator_ids, label="Operator ID"),
-        gr.Slider(0, 6, step=1, label="Day of Week (0=Monday, 6=Sunday)", value=0),
+        gr.Textbox(label="Start Date (DD-MM-YYYY)", value="21-07-2025"),
+        gr.Number(label="Quantity", value=6000),
+        gr.Dropdown(choices=operation_options, label="Operation Type", value=operation_options[0]),
+        gr.Dropdown(choices=operator_options, label="Operator ID", value=operator_options[0]),
+        gr.Slider(minimum=0, maximum=6, step=1, label="Day of Week (0=Monday, 6=Sunday)", value=1)
     ],
     outputs="text",
-    title="Manufacturing Production Scheduling",
-    description="Predict estimated end date and duration from production inputs."
+    title="Manufacturing Scheduler Prediction",
+    description="Predict the estimated end date and duration of a manufacturing job based on input parameters."
 )
 
 if __name__ == "__main__":