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import gradio as gr
import joblib
import numpy as np
import os
# Get the current file directory
current_dir = os.path.dirname(os.path.abspath(__file__))
# 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"]
inputs = [gr.inputs.Number(label=label) for label in input_labels]
output = gr.outputs.Textbox(label="Predicted Department")
# Create the Gradio app
app = gr.Interface(fn=predict_department, inputs=inputs, outputs=output, title="Department Predictor")
# Launch the app
if __name__ == "__main__":
app.launch()
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