Upload 4 files

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+
+# Load model
+with open("model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+def predict_pass(study_hours, attendance, assignments_completed, previous_marks):
+
+    data = np.array([[study_hours, attendance, assignments_completed, previous_marks]])
+
+    prediction = model.predict(data)[0]
+
+    if prediction == 1:
+        return "✅ Student Will PASS"
+    else:
+        return "❌ Student Will FAIL"
+
+# Gradio UI
+interface = gr.Interface(
+    fn=predict_pass,
+    inputs=[
+        gr.Number(label="Study Hours"),
+        gr.Number(label="Attendance (%)"),
+        gr.Number(label="Assignments Completed"),
+        gr.Number(label="Previous Marks")
+    ],
+    outputs="text",
+    title="🎓 Student Pass/Fail Predictor",
+    description="Predict whether a student will pass based on study hours, attendance, assignments, and previous marks."
+)
+
+interface.launch()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4328f560626c678b471f1d54c9aebd7cd4ccadd3ac6aba09b45f649ae1b43686
+size 73628

requirements.txt

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+gradio
+xgboost
+scikit-learn
+pandas
+numpy

train_model.py

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+import xgboost as xgb
+import pandas as pd
+import pickle
+from sklearn.model_selection import train_test_split
+
+data = {
+"study_hours": [1,2,3,4,5,6,7,8,2,3,5,6,7,1,4],
+"attendance": [50,55,60,65,70,75,80,85,60,65,75,80,85,45,68],
+"assignments_completed": [1,1,2,2,3,3,4,4,2,2,3,3,4,1,2],
+"previous_marks": [30,35,40,45,50,55,60,65,42,48,53,58,62,28,47],
+"pass": [0,0,0,0,1,1,1,1,0,0,1,1,1,0,0]
+}
+
+df = pd.DataFrame(data)
+
+X = df.drop("pass", axis=1)
+y = df["pass"]
+
+X_train, X_test, y_train, y_test = train_test_split(
+X, y, test_size=0.2, random_state=42
+)
+
+model = xgb.XGBClassifier(
+objective="binary:logistic",
+eval_metric="logloss",
+use_label_encoder=False
+)
+
+model.fit(X_train, y_train)
+with open("model.pkl", "wb") as f:
+    pickle.dump(model, f)
+
+print("Model saved as model.pkl")