Create app.py

app.py

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+# Smart Study Planner with Performance Prediction
+
+import streamlit as st
+import numpy as np
+import pandas as pd
+
+# -------------------------------
+# OOP CLASSES
+# -------------------------------
+
+class Subject:
+    def __init__(self, name, study_hours, marks):
+        self.name = name
+        self.study_hours = study_hours
+        self.marks = marks
+
+
+class Student:
+    def __init__(self, name):
+        self.name = name
+        self.subjects = []
+
+    def add_subject(self, subject):
+        self.subjects.append(subject)
+
+    def get_data(self):
+        data = {
+            "Subject": [],
+            "Study Hours": [],
+            "Marks": []
+        }
+        for sub in self.subjects:
+            data["Subject"].append(sub.name)
+            data["Study Hours"].append(sub.study_hours)
+            data["Marks"].append(sub.marks)
+        return pd.DataFrame(data)
+
+    def calculate_average(self):
+        marks = [sub.marks for sub in self.subjects]
+        return np.mean(marks) if marks else 0
+
+    def get_max_min(self):
+        marks = [sub.marks for sub in self.subjects]
+        return np.max(marks), np.min(marks)
+
+    def predict_performance(self, extra_hours=0):
+        predicted_marks = []
+        for sub in self.subjects:
+            increase = extra_hours * 3  # simple formula
+            predicted_marks.append(min(sub.marks + increase, 100))
+        return np.mean(predicted_marks) if predicted_marks else 0
+
+    def get_grade(self, marks):
+        if marks >= 85:
+            return "A"
+        elif marks >= 70:
+            return "B"
+        elif marks >= 55:
+            return "C"
+        elif marks >= 40:
+            return "D"
+        else:
+            return "F"
+
+    def best_subject(self):
+        return max(self.subjects, key=lambda x: x.marks).name
+
+    def weak_subject(self):
+        return min(self.subjects, key=lambda x: x.marks).name
+
+    def suggest_improvement(self):
+        weak = self.weak_subject()
+        return f"Focus more on {weak} and increase study hours."
+
+
+# -------------------------------
+# STREAMLIT UI
+# -------------------------------
+
+st.title("📚 Smart Study Planner")
+
+# Student Name
+student_name = st.text_input("Enter Student Name")
+
+# Initialize session state
+if "student" not in st.session_state:
+    st.session_state.student = None
+
+if student_name:
+    st.session_state.student = Student(student_name)
+
+# Input Fields
+subject_name = st.text_input("Subject Name")
+study_hours = st.number_input("Study Hours", min_value=0.0, step=0.5)
+marks = st.number_input("Marks", min_value=0, max_value=100)
+
+# Add Subject Button
+if st.button("Add Subject"):
+    if subject_name and st.session_state.student:
+        subject = Subject(subject_name, study_hours, marks)
+        st.session_state.student.add_subject(subject)
+        st.success(f"{subject_name} added!")
+    else:
+        st.error("Please enter valid data!")
+
+# Analyze Button
+if st.button("Analyze Performance"):
+    student = st.session_state.student
+
+    if student and student.subjects:
+        df = student.get_data()
+
+        st.subheader("📊 Data Table")
+        st.dataframe(df)
+
+        avg = student.calculate_average()
+        st.write(f"Average Marks: {avg:.2f}")
+
+        max_marks, min_marks = student.get_max_min()
+        st.write(f"Highest Marks: {max_marks}")
+        st.write(f"Lowest Marks: {min_marks}")
+
+        grade = student.get_grade(avg)
+        st.write(f"Current Grade: {grade}")
+
+        predicted = student.predict_performance(extra_hours=2)
+        predicted_grade = student.get_grade(predicted)
+
+        st.write(f"📈 If you study 2 more hours, expected marks: {predicted:.2f}")
+        st.write(f"Predicted Grade: {predicted_grade}")
+
+        st.write(f"🏆 Best Subject: {student.best_subject()}")
+        st.write(f"⚠ Weak Subject: {student.weak_subject()}")
+
+        st.write("💡 Suggestion:", student.suggest_improvement())
+
+        # Simple chart
+        st.subheader("📉 Study Hours vs Marks")
+        st.line_chart(df.set_index("Subject")[["Study Hours", "Marks"]])
+
+    else:
+        st.error("Please add subjects first!")
+
+# -------------------------------
+# KAGGLE DATASET INTEGRATION (OPTIONAL)
+# -------------------------------
+
+st.subheader("📂 Optional: Load Dataset")
+
+uploaded_file = st.file_uploader("Upload CSV Dataset", type=["csv"])
+
+if uploaded_file:
+    df = pd.read_csv(uploaded_file)
+
+    st.write("Raw Data:", df.head())
+
+    # Cleaning
+    df = df.drop_duplicates()
+    df = df.fillna(df.mean(numeric_only=True))
+
+    st.write("Cleaned Data:", df.head())
+
+    st.write("Dataset Average:", df.mean(numeric_only=True))
+
+    st.success("Dataset processed successfully!")