Update app.py

app.py

@@ -1,13 +1,7 @@
 import numpy as np
 from datascience import *
-import matplotlib.pyplot as plt
 import gradio as gr
 
-plt.style.use('fivethirtyeight')
-
-# ----------------------------
-# Load and prepare data
-# ----------------------------
 SPD = Table.read_table('Student_Performance_Data.csv')
 
 def study_effectiveness(study_time, absences, parental_support, tutoring):
@@ -32,13 +26,10 @@ def pass_or_fail(grade):
 passes = SPD.apply(pass_or_fail, 'GradeClass')
 SPD = SPD.with_column('Pass', passes)
 
-# ----------------------------
-# Build feature matrix
-# ----------------------------
 X = []
 
 for i in np.arange(SPD.num_rows):
-    row = [
+    X.append([
         SPD.column('StudyTimeWeekly').item(i) / 20,
         SPD.column('Absences').item(i) / 30,
         SPD.column('Tutoring').item(i),
@@ -48,15 +39,11 @@ for i in np.arange(SPD.num_rows):
         SPD.column('Music').item(i),
         SPD.column('Volunteering').item(i),
         SPD.column('StudyEffectiveness').item(i) / 40
-    ]
-    X.append(row)
+    ])
 
 X = np.array(X)
 y = np.array(SPD.column('Pass'))
 
-# ----------------------------
-# Neural network setup
-# ----------------------------
 np.random.seed(1)
 W1 = np.random.normal(0, 1, (9, 16))
 W2 = np.random.normal(0, 1, (16, 1))
@@ -64,36 +51,22 @@ W2 = np.random.normal(0, 1, (16, 1))
 def sigmoid(x):
     return 1 / (1 + np.exp(-x))
 
-# ----------------------------
-# Train model
-# ----------------------------
 for i in range(20000):
     hidden = sigmoid(np.dot(X, W1))
     output = sigmoid(np.dot(hidden, W2))
-
     error = y.reshape(len(y), 1) - output
-
     output_change = error * output * (1 - output)
     hidden_error = np.dot(output_change, W2.T)
     hidden_change = hidden_error * hidden * (1 - hidden)
-
     W2 += 0.001 * np.dot(hidden.T, output_change)
     W1 += 0.001 * np.dot(X.T, hidden_change)
 
-# Training accuracy
 hidden = sigmoid(np.dot(X, W1))
 output = sigmoid(np.dot(hidden, W2))
 predictions = output > 0.5
-accuracynn = np.mean(predictions.flatten() == y)
-
-print("Training accuracy:", accuracynn)
-
-# ----------------------------
-# Prediction function for UI
-# ----------------------------
-def predict(student_time, absences, tutoring, parental_support,
-            extracurricular, sports, music, volunteering):
+accuracy_nn = np.mean(predictions.flatten() == y)
 
+def predict(student_time, absences, tutoring, parental_support, extracurricular, sports, music, volunteering):
     study_effect = (student_time * 1.5) + (parental_support * 2) + (tutoring * 3) - (absences * 0.7)
 
     x = np.array([
@@ -114,29 +87,38 @@ def predict(student_time, absences, tutoring, parental_support,
     result = "PASS" if output[0] > 0.5 else "FAIL"
     confidence = float(output[0])
 
-    return result, confidence
-
-# ----------------------------
-# Gradio Interface
-# ----------------------------
-interface = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Number(label="Study Time Weekly"),
-        gr.Number(label="Absences"),
-        gr.Number(label="Tutoring (0 or 1)"),
-        gr.Number(label="Parental Support (0–4)"),
-        gr.Number(label="Extracurricular (0 or 1)"),
-        gr.Number(label="Sports (0 or 1)"),
-        gr.Number(label="Music (0 or 1)"),
-        gr.Number(label="Volunteering (0 or 1)")
-    ],
-    outputs=[
-        gr.Textbox(label="Prediction"),
-        gr.Number(label="Confidence")
-    ],
-    title="Student Performance Predictor",
-    description="Predict whether a student will pass or fail based on academic and activity inputs."
-)
+    return result, round(confidence, 4)
+
+theme = gr.themes.Soft()
+
+with gr.Blocks(theme=theme) as app:
+    gr.Markdown("# Student Performance Predictor")
+    gr.Markdown("Predict whether a student will pass or fail based on academic behavior and activities.")
+
+    with gr.Row():
+        with gr.Column():
+            student_time = gr.Slider(0, 20, value=10, label="Study Time Weekly")
+            absences = gr.Slider(0, 30, value=5, label="Absences")
+            parental_support = gr.Slider(0, 4, value=2, step=1, label="Parental Support")
+
+            tutoring = gr.Checkbox(label="Tutoring")
+            extracurricular = gr.Checkbox(label="Extracurricular Activities")
+            sports = gr.Checkbox(label="Sports")
+            music = gr.Checkbox(label="Music")
+            volunteering = gr.Checkbox(label="Volunteering")
+
+            btn = gr.Button("Predict", variant="primary")
+
+        with gr.Column():
+            result = gr.Textbox(label="Prediction")
+            confidence = gr.Number(label="Confidence Score")
+
+    btn.click(
+        predict,
+        inputs=[student_time, absences, tutoring, parental_support, extracurricular, sports, music, volunteering],
+        outputs=[result, confidence]
+    )
+
+    gr.Markdown(f"Model training accuracy: {accuracy_nn:.3f}")
 
-interface.launch()
+app.launch()