Update app.py

app.py

@@ -6,36 +6,53 @@ 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
+import os
 
 # Load or train models
-try:
-    with open("scaler.pkl", "rb") as scaler_file:
-        scaler = pickle.load(scaler_file)
-   
-    with open("logistic_regression.pkl", "rb") as model_file:
-        logistic_regression = pickle.load(model_file)
-   
-    with open("perceptron.pkl", "rb") as model_file:
-        perceptron = pickle.load(model_file)
-
-except FileNotFoundError:
-    print("Training models...")
+model_files = ["scaler.pkl", "logistic_regression.pkl", "perceptron.pkl"]
+
+# Check if all model files exist
+if all(os.path.exists(file) for file in model_files):
+    try:
+        with open("scaler.pkl", "rb") as scaler_file:
+            scaler = pickle.load(scaler_file)
+        
+        with open("logistic_regression.pkl", "rb") as model_file:
+            logistic_regression = pickle.load(model_file)
+        
+        with open("perceptron.pkl", "rb") as model_file:
+            perceptron = pickle.load(model_file)
+
+    except Exception as e:
+        print(f"Error loading models: {e}")
+        exit()
+
+else:
+    print("Training models from scratch...")
+
+    # Load dataset
     df = pd.read_excel("Student-Employability-Datasets.xlsx", sheet_name="Data")
-    X = df.iloc[:, 1:-2].values
-    y = (df["CLASS"] == "Employable").astype(int)
-   
+
+    # Prepare dataset
+    X = df.iloc[:, 1:-1].values  # Select all feature columns
+    y = (df["CLASS"] == "Employable").astype(int)  # Convert to binary labels
+
+    # Train-test split
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-   
+
+    # Feature scaling
     scaler = StandardScaler()
     X_train_scaled = scaler.fit_transform(X_train)
     X_test_scaled = scaler.transform(X_test)
-   
+
+    # Train models
     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)
-   
+
+    # Save trained models
     with open("scaler.pkl", "wb") as scaler_file:
         pickle.dump(scaler, scaler_file)
     with open("logistic_regression.pkl", "wb") as model_file:
@@ -43,27 +60,44 @@ except FileNotFoundError:
     with open("perceptron.pkl", "wb") as model_file:
         pickle.dump(perceptron, model_file)
 
-# Prediction function
+    print("Training complete. Models saved.")
+
+# Employability prediction function
 def predict_employability(name, ga, mos, pc, ma, sc, api, cs, model_choice):
-    if not name:
-        name = "The candidate"
-   
-    input_data = np.array([[ga, mos, pc, ma, sc, api, cs]])
-    input_scaled = scaler.transform(input_data)
-   
-    if model_choice == "Logistic Regression":
-        prediction = logistic_regression.predict(input_scaled)
-    else:
-        prediction = perceptron.predict(input_scaled)
-   
-    if prediction[0] == 1:
-        return f"{name} is Employable 😊"
-    else:
-        return f"{name} is Less Employable - Work Hard! 💪"
+    try:
+        if not name:
+            name = "The candidate"
+
+        input_data = np.array([[ga, mos, pc, ma, sc, api, cs]])
+
+        # Ensure scaler is correctly loaded
+        if scaler is None:
+            return "Error: Scaler not loaded properly."
+
+        # Transform input
+        input_scaled = scaler.transform(input_data)
+
+        # Select model and make prediction
+        if model_choice == "Logistic Regression":
+            prediction = logistic_regression.predict(input_scaled)
+        elif model_choice == "Perceptron":
+            prediction = perceptron.predict(input_scaled)
+        else:
+            return "Error: Invalid model selection."
+
+        # Return result
+        if prediction[0] == 1:
+            return f"{name} is Employable 😊"
+        else:
+            return f"{name} is Less Employable - Work Hard! 💪"
+
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 # Gradio UI
 with gr.Blocks() as app:
-    gr.Markdown("#  Employability Evaluation 🚀")
+    gr.Markdown("# 🚀 Employability Evaluation System")
+    
     with gr.Row():
         with gr.Column():
             name = gr.Textbox(label="Name")
@@ -75,7 +109,7 @@ with gr.Blocks() as app:
             api = gr.Slider(1, 5, step=1, label="Ability to Present Ideas")
             cs = gr.Slider(1, 5, step=1, label="Communication Skills")
             model_choice = gr.Radio(["Logistic Regression", "Perceptron"], label="Select Model")
-           
+
             predict_btn = gr.Button("Get Evaluation")
 
         with gr.Column():
@@ -87,4 +121,6 @@ with gr.Blocks() as app:
         inputs=[name, ga, mos, pc, ma, sc, api, cs, model_choice],
         outputs=[result_output]
     )
-    app.launch(server_port=7860, debug=True, share=True)
+
+# Launch the app
+app.launch(server_port=7860, debug=True, share=True)