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import os
import warnings
import numpy as np
import pandas as pd
from tensorflow.keras.models import load_model
import pickle
from dotenv import load_dotenv
import gradio as gr
# Suppress TensorFlow and other warnings
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
warnings.filterwarnings("ignore")
# Load environment variables
load_dotenv()
# Load the trained model and scaler
model = None
scaler = None
def load_model_and_scaler():
global model, scaler
try:
model = load_model('final_marks_predictor_model.h5')
with open('scaler.pkl', 'rb') as f:
scaler = pickle.load(f)
except Exception as e:
print(f"Error loading model or scaler: {e}")
# Load model and scaler when the application starts
load_model_and_scaler()
def predict_new_input(age, year1_marks, year2_marks, studytime, failures):
try:
feature_names = ['age', 'year1_marks', 'year2_marks', 'studytime', 'failures']
new_input_df = pd.DataFrame([[age, year1_marks, year2_marks, studytime, failures]], columns=feature_names)
if model is None or scaler is None:
return "Model or scaler is not loaded."
new_input_scaled = scaler.transform(new_input_df)
predicted_marks = model.predict(new_input_scaled, verbose=0)
return round(float(predicted_marks[0][0]), 2)
except Exception as e:
print(f"Error during prediction: {e}")
return "Error during prediction"
# Define Gradio Interface
def gradio_predict(age, year1_marks, year2_marks, studytime, failures):
return predict_new_input(age, year1_marks, year2_marks, studytime, failures)
if __name__ == '__main__':
# Create the Gradio interface
with gr.Blocks(theme=gr.themes.Soft(), css=".gradio-container {max-width: 600px; margin: auto; padding: 20px; border: 1px solid #e0e0e0; border-radius: 10px; box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);}") as interface:
with gr.Column():
gr.Markdown("## Student Performance Prediction")
gr.Markdown("Please fill in all the required fields and click 'Predict' to see your final predicted marks.")
# Create form fields
with gr.Row():
age = gr.Number(label="Age", interactive=True, elem_id="age-input")
with gr.Row():
year1_marks = gr.Number(label="First Year Marks", interactive=True, elem_id="year1-input")
with gr.Row():
year2_marks = gr.Number(label="Second Year Marks", interactive=True, elem_id="year2-input")
with gr.Row():
studytime = gr.Number(label="Study Time (hours/week)", interactive=True, elem_id="studytime-input")
with gr.Row():
failures = gr.Number(label="Number of Failures", interactive=True, elem_id="failures-input")
submit_button = gr.Button("Predict", elem_id="predict-button", variant="primary")
# Create output display
output = gr.Textbox(label="Predicted Final Marks", interactive=False, elem_id="output-box")
# Button action
submit_button.click(gradio_predict, inputs=[age, year1_marks, year2_marks, studytime, failures], outputs=output)
interface.launch(share=False)
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