Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import gradio as gr
+
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_absolute_error, root_mean_squared_error
+
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Input
+from tensorflow.keras.callbacks import EarlyStopping
+import keras_tuner as kt
+
+# Load dataset
+df = pd.read_csv("Dataset/Student_Performance.csv")
+
+# Features
+num_feature = ['Hours Studied', 'Previous Scores', 'Sleep Hours', 'Sample Question Papers Practiced']
+cat_feature = ['Extracurricular Activities']
+
+# Preprocessing
+preprocess = ColumnTransformer(transformers=[
+    ('num', StandardScaler(), num_feature),
+    ('cat', OneHotEncoder(drop='first'), cat_feature)
+])
+
+x = df.drop(columns=['Performance Index'])
+y = df['Performance Index']
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=42)
+
+x_train_scaled = preprocess.fit_transform(x_train)
+x_test_scaled = preprocess.transform(x_test)
+
+# Build model
+def build_model(hp):
+    model = Sequential([
+        Input(shape=(x_test_scaled.shape[1],)),
+        Dense(units=hp.Int('u1', 32, 256, step=32), activation='relu'),
+        Dense(units=hp.Int('u2', 16, 128, step=16), activation='relu'),
+        Dense(units=hp.Int('u3', 8, 64, step=8), activation='relu'),
+        Dense(1)
+    ])
+    model.compile(optimizer="adam", loss="mse", metrics=["mae"])
+    return model
+
+# Hyperparameter tuning
+tuner = kt.RandomSearch(build_model, objective="val_loss", max_trials=3, overwrite=True)
+early_stop = EarlyStopping(monitor="val_loss", patience=10, restore_best_weights=True)
+
+tuner.search(x_train_scaled, y_train, epochs=50, batch_size=32, validation_split=0.2,
+             callbacks=[early_stop], verbose=0)
+
+best_model = tuner.get_best_models(1)[0]
+
+# Define prediction function for Gradio
+def predict_performance(hours, prev_score, sleep, papers, activity):
+    user_input = pd.DataFrame([{
+        'Hours Studied': hours,
+        'Previous Scores': prev_score,
+        'Sleep Hours': sleep,
+        'Sample Question Papers Practiced': papers,
+        'Extracurricular Activities': activity
+    }])
+    X_user = preprocess.transform(user_input)
+    pred = best_model.predict(X_user).item()
+    return f"📊 Predicted Performance Index: {pred:.2f}"
+
+# Gradio UI
+inputs = [
+    gr.Number(label="Hours Studied"),
+    gr.Number(label="Previous Scores"),
+    gr.Number(label="Sleep Hours"),
+    gr.Number(label="Sample Question Papers Practiced"),
+    gr.Radio(choices=["Yes", "No"], label="Extracurricular Activities")
+]
+
+demo = gr.Interface(
+    fn=predict_performance,
+    inputs=inputs,
+    outputs="text",
+    title="🎓 Student Performance Prediction",
+    description="Enter student study habits and get predicted performance index."
+)
+
+if __name__ == "__main__":
+    demo.launch()