Upload 16 files

app.py

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+import streamlit as st
+import pickle
+from sklearn.ensemble import RandomForestRegressor
+import numpy as np
+
+# Load the saved model
+with open('rf_model.pkl', 'rb') as file:
+    model = pickle.load(file)
+
+# Define the function to make predictions
+def make_prediction(model, input_data):
+    prediction = model.predict(input_data)
+    return prediction
+
+# Create the Streamlit app
+def main():
+    # Set page title and configure layout
+    st.set_page_config(page_title="Exam Score Prediction", layout="wide")
+
+    # Add a title and description
+    st.title("Exam Score Prediction")
+    st.markdown(
+        "This app predicts exam scores based on input features such as level, course units, attendance, mid-semester score, and assignments."
+    )
+
+    # Create input fields
+    col1, col2 = st.columns(2)
+    with col1:
+        level = st.number_input("Level", min_value=200, max_value=400, step=1)
+        course_units = st.number_input("Course Units", min_value=1, max_value=4, step=1)
+    with col2:
+        attendance = st.slider("Attendance", min_value=1, max_value=10, step=1)
+        mid_semester = st.slider("Mid Semester Score", min_value=1, max_value=20, step=1)
+        assignments = st.slider("Assignments", min_value=1, max_value=10, step=1)
+
+    # Create input data
+    input_data = np.array([[level, course_units, attendance, mid_semester, assignments]])
+
+    # Make prediction
+    if st.button("Predict Exam Score"):
+        prediction = make_prediction(model, input_data)
+        st.write(f"Predicted Exam Score: {prediction[0]:.2f}")
+
+if __name__ == '__main__':
+    main()

gb_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b0b75da4aa7fe41c4565edd1150dfd9cff8324e57cd557f461bc0a09454823dc
+size 123720

requirements.txt

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+numpy == 1.23.3
+pandas == 2.0.2
+sklearn == 1.2.1
+tensorflow == 2.13.0

rf_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f0bd693be2a7815e0d5cac32980d7a07dd3424c27b73d8b78072339e42d7686a
+size 132319129

setup.py

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+from setuptools import find_packages, setup
+
+setup(
+    name = 'Student_grade_prediction',
+    version= '0.0.0',
+    author= 'Anulunko Chukwuebuka',
+    author_email= 'chukwuebukaanulunko@gmail.com',
+    packages= find_packages(),
+    install_requires = []
+
+)

template.py

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+import os
+from pathlib import Path
+import logging
+
+logging.basicConfig(level=logging.INFO, format='[%(asctime)s]: %(message)s:')
+
+list_of_files = [
+    "src/__init__.py",
+    "src/helper.py",
+    "src/prompt.py",
+    ".env",
+    "setup.py",
+    "research/trials.ipynb",
+    "app.py",
+    "static/.gitkeep",
+]
+
+
+for filepath in list_of_files:
+   filepath = Path(filepath)
+   filedir, filename = os.path.split(filepath)
+
+   if filedir !="":
+      os.makedirs(filedir, exist_ok=True)
+      logging.info(f"Creating directory; {filedir} for the file {filename}")
+
+   if (not os.path.exists(filepath)) or (os.path.getsize(filepath) == 0):
+      with open(filepath, 'w') as f:
+         pass
+         logging.info(f"Creating empty file: {filepath}")
+
+   else:
+      logging.info(f"{filename} is already created")
+