Create app.py

app.py

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+import os
+import joblib
+import numpy as np
+import gradio as gr
+from pathlib import Path
+import sys
+
+# Get the current directory of the script or fallback to the current working directory
+if hasattr(sys, 'frozen'):
+    current_dir = Path(sys.executable).parent
+elif '__file__' in globals():
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+else:
+    current_dir = Path().absolute()
+
+# Load the trained model from the same directory
+model_path = os.path.join(current_dir, "trained_model.joblib")
+model = joblib.load(model_path)
+
+# Define the prediction function
+def predict_department(CSC101_total, CSC201_total, CSC203_total, CSC205_total, CSC102_total, 
+                       MAT202_total, MAT203_total, MAT103_total, CSC206_total, MAN101_total, 
+                       SWE201_total, SWE301_total, SWE303_total, CNE202_total, CNE203_total, 
+                       CNE304_total, CSC301_total, CNE302_total, CSC309_total, CSC302_total, 
+                       CSC303_total, CNE308_total):
+    
+    try:
+        # Convert the input data to a numpy array
+        input_data = np.array([[CSC101_total, CSC201_total, CSC203_total, CSC205_total, 
+                                CSC102_total, MAT202_total, MAT203_total, MAT103_total, 
+                                CSC206_total, MAN101_total, SWE201_total, SWE301_total, 
+                                SWE303_total, CNE202_total, CNE203_total, CNE304_total, 
+                                CSC301_total, CNE302_total, CSC309_total, CSC302_total, 
+                                CSC303_total, CNE308_total]])
+
+        # Make the prediction
+        prediction = model.predict(input_data)
+        
+        # Map the prediction to department name
+        department_mapping = {0: 'Swe', 1: 'Cs', 2: 'Cne', 3: 'Ai'}
+        predicted_department = department_mapping[prediction[0]]
+
+        return predicted_department
+
+    except Exception as e:
+        return str(e)
+
+# Define the Gradio interface
+input_labels = ["CSC101_total", "CSC201_total", "CSC203_total", "CSC205_total", "CSC102_total", 
+                "MAT202_total", "MAT203_total", "MAT103_total", "CSC206_total", "MAN101_total", 
+                "SWE201_total", "SWE301_total", "SWE303_total", "CNE202_total", "CNE203_total", 
+                "CNE304_total", "CSC301_total", "CNE302_total", "CSC309_total", "CSC302_total", 
+                "CSC303_total", "CNE308_total"]
+
+# Create a list of number inputs corresponding to the input labels
+inputs = [gr.Number(label=label) for label in input_labels]
+
+# Define the output as a text box that will show the predicted department
+output = gr.Textbox(label="Predicted Department")
+
+# Create the Gradio app interface
+app = gr.Interface(fn=predict_department, inputs=inputs, outputs=output, title="Department Predictor")
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch()