Create app.py

app.py

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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)