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import gradio as gr
import numpy as np
import pickle
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression, Perceptron
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
def load_or_train_models():
"""Load models from disk or train and save them if not found."""
try:
with open("scaler.pkl", "rb") as f:
scaler = pickle.load(f)
with open("logistic_regression.pkl", "rb") as f:
logistic_regression = pickle.load(f)
with open("perceptron.pkl", "rb") as f:
perceptron = pickle.load(f)
except FileNotFoundError:
print("Training models...")
df = pd.read_excel("Student-Employability-Datasets.xlsx", sheet_name="Data")
X = df.iloc[:, 1:-2].values
y = (df["CLASS"] == "Employable").astype(int)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
logistic_regression = LogisticRegression(random_state=42)
logistic_regression.fit(X_train_scaled, y_train)
perceptron = Perceptron(random_state=42)
perceptron.fit(X_train_scaled, y_train)
with open("scaler.pkl", "wb") as f:
pickle.dump(scaler, f)
with open("logistic_regression.pkl", "wb") as f:
pickle.dump(logistic_regression, f)
with open("perceptron.pkl", "wb") as f:
pickle.dump(perceptron, f)
return scaler, logistic_regression, perceptron
scaler, logistic_regression, perceptron = load_or_train_models()
def predict_employability(name, ga, mos, pc, ma, sc, api, cs, model_choice):
"""Predict employability based on input features and selected model."""
name = name if name else "The candidate"
input_data = np.array([[ga, mos, pc, ma, sc, api, cs]])
input_scaled = scaler.transform(input_data)
model = logistic_regression if model_choice == "Logistic Regression" else perceptron
prediction = model.predict(input_scaled)[0]
return f"{name} is {'Employable 😊' if prediction == 1 else 'Less Employable - Work Hard! 💪'}"
with gr.Blocks() as app:
gr.Markdown("# Employability Evaluation ")
with gr.Row():
with gr.Column():
name = gr.Textbox(label="Name")
sliders = [
gr.Slider(1, 5, step=1, label=label) for label in [
"General Appearance", "Manner of Speaking", "Physical Condition",
"Mental Alertness", "Self Confidence", "Ability to Present Ideas",
"Communication Skills"
]
]
model_choice = gr.Radio(["Logistic Regression", "Perceptron"], label="Select Model")
predict_btn = gr.Button("Get Evaluation")
with gr.Column():
result_output = gr.Textbox(label="Employability Prediction")
predict_btn.click(
fn=predict_employability,
inputs=[name] + sliders + [model_choice],
outputs=[result_output]
)
app.launch(share=True) |