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
+
+# 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...")
+    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 scaler_file:
+        pickle.dump(scaler, scaler_file)
+    with open("logistic_regression.pkl", "wb") as model_file:
+        pickle.dump(logistic_regression, model_file)
+    with open("perceptron.pkl", "wb") as model_file:
+        pickle.dump(perceptron, model_file)
+
+# 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! 💪"
+
+# Gradio UI
+with gr.Blocks() as app:
+    gr.Markdown("#  Employability Evaluation 🚀")
+    with gr.Row():
+        with gr.Column():
+            name = gr.Textbox(label="Name")
+            ga = gr.Slider(1, 5, step=1, label="General Appearance")
+            mos = gr.Slider(1, 5, step=1, label="Manner of Speaking")
+            pc = gr.Slider(1, 5, step=1, label="Physical Condition")
+            ma = gr.Slider(1, 5, step=1, label="Mental Alertness")
+            sc = gr.Slider(1, 5, step=1, label="Self Confidence")
+            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():
+            result_output = gr.Textbox(label="Employability Prediction")
+
+    # Button Click Event
+    predict_btn.click(
+        fn=predict_employability,
+        inputs=[name, ga, mos, pc, ma, sc, api, cs, model_choice],
+        outputs=[result_output]
+    )
+
+# Launch the app
+app.launch(share=True) give me the module name and the requirements.txt