Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,61 @@
-import altair as alt
 import numpy as np
-import pandas as pd
+import joblib
 import streamlit as st
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# Load the trained model
+import os
+model_path = os.path.join(os.path.dirname(__file__), "student_performance_model.h5")
+model = joblib.load(model_path)
+
+
+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()