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app (1).py

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+import numpy as np
+import joblib
+import streamlit as st
+
+# Load the trained model
+model = joblib.load("student_performance_model.h5")
+
+def predict_marks(Hours_studied, Previous_Score, Extracurriculum_Activivities, Sleep_Hours, Sample_Question):
+    "Predict the student marks based on the input data"
+    input_data = np.array([[Hours_studied, Previous_Score, Extracurriculum_Activivities, Sleep_Hours, Sample_Question]])
+    prediction = model.predict(input_data)
+    prediction = round(float(prediction), 2)
+    
+    # Ensure the prediction does not exceed 100
+    if prediction > 100:
+        prediction = 100
+    
+    return prediction
+
+def main():
+    # Sidebar Welcome Note with Emojis
+    
+
+    st.title("📚 Student Marks Predictor 📊")
+
+    # Input data
+    name = st.text_input("👤 Enter your name")
+    Hours_studied = st.number_input("📖 Hours you studied", min_value=0.0, max_value=20.0, value=0.0)
+    Previous_Score = st.number_input("📊 Previous exam score", min_value=0, max_value=100, value=0)
+    Extracurriculum_Activivities = st.number_input("🎭 Extracurricular activities done", min_value=0, max_value=10, value=0)
+    Sleep_Hours = st.number_input("😴 Hours you slept", min_value=0.0, max_value=12.0, value=0.0)
+    Sample_Question = st.number_input("✍️ Sample questions practiced", min_value=0, max_value=50, value=0)
+
+    # Sidebar interaction
+    st.sidebar.title(f" # Hey {name}")
+    st.sidebar.title(f"🎉Welcome to your Marks Predictor! 🎉")
+    st.sidebar.write("""
+        Hey there! Ready to see what your future marks might be? 😄
+
+        Remember, I'm here to help you succeed! 💪
+        """)
+
+    st.sidebar.markdown("---")
+
+    # Predict button
+    if st.button("🔮 Predict Your Marks"):
+        prediction = predict_marks(Hours_studied, Previous_Score, Extracurriculum_Activivities, Sleep_Hours, Sample_Question)
+
+        # Display the predictions
+        if prediction >= 90:
+            st.balloons()
+            st.success(f"🌟 **{name}, amazing!** You're on track to score {prediction} marks! Keep up the excellent work! 💪")
+        elif prediction >= 35:
+            st.warning(f"⚠️ **{name}, not bad!** You're likely to pass with {prediction} marks, but there's room to aim higher! 🚀")
+        else:
+            st.error(f"🚨 **{name}, oh no!** You might score below 35 marks. Consider putting in some more effort! 📚")
+
+if __name__ == "__main__":
+    main()

requirements (1).txt

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+joblib==1.2.0
+numpy==1.24.3
+pandas==2.0.3
+streamlit==1.37.0
+scikit-learn

student_performance_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4157ea9e74dc017119fd526cc588e579c595ba7ab8ba62f5b7213eec0316d811
+size 1040