Update app.py

app.py

@@ -1,17 +1,25 @@
-# Smart Study Planner with Performance Prediction
+# Smart Study Planner - Enhanced Version
 
 import streamlit as st
 import numpy as np
 import pandas as pd
 
 # -------------------------------
-# OOP CLASSES
+# PAGE CONFIG (Better UI)
+# -------------------------------
+st.set_page_config(page_title="Smart Study Planner", page_icon="📚", layout="centered")
+
+st.title("📚 Smart Study Planner with AI Prediction")
+st.markdown("Analyze your study habits and improve performance 🚀")
+
+# -------------------------------
+# CLASSES (OOP)
 # -------------------------------
 
 class Subject:
-    def __init__(self, name, study_hours, marks):
+    def __init__(self, name, hours, marks):
         self.name = name
-        self.study_hours = study_hours
+        self.hours = hours
         self.marks = marks
 
 
@@ -23,32 +31,38 @@ class Student:
     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 get_dataframe(self):
+        return pd.DataFrame({
+            "Subject": [s.name for s in self.subjects],
+            "Study Hours": [s.hours for s in self.subjects],
+            "Marks": [s.marks for s in self.subjects]
+        })
 
     def calculate_average(self):
-        marks = [sub.marks for sub in self.subjects]
+        marks = [s.marks for s in self.subjects]
         return np.mean(marks) if marks else 0
 
     def get_max_min(self):
-        marks = [sub.marks for sub in self.subjects]
+        marks = [s.marks for s 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 performance_trend(self):
+        hours = [s.hours for s in self.subjects]
+        marks = [s.marks for s in self.subjects]
+
+        if len(hours) > 1:
+            return np.corrcoef(hours, marks)[0][1]  # correlation
+        return 0
+
+    def predict_performance(self):
+        trend = self.performance_trend()
+        predicted = []
+
+        for s in self.subjects:
+            improvement = (2 * 4) + (trend * 5)  # smarter logic
+            predicted.append(min(s.marks + improvement, 100))
+
+        return np.mean(predicted) if predicted else 0
 
     def get_grade(self, marks):
         if marks >= 85:
@@ -59,8 +73,7 @@ class Student:
             return "C"
         elif marks >= 40:
             return "D"
-        else:
-            return "F"
+        return "F"
 
     def best_subject(self):
         return max(self.subjects, key=lambda x: x.marks).name
@@ -70,96 +83,113 @@ class Student:
 
     def suggest_improvement(self):
         weak = self.weak_subject()
-        return f"Focus more on {weak} and increase study hours."
+        return f"Focus more on {weak} and increase study hours by at least 2 hours."
 
 
 # -------------------------------
-# STREAMLIT UI
+# SESSION STATE
 # -------------------------------
+if "student" not in st.session_state:
+    st.session_state.student = None
 
-st.title("📚 Smart Study Planner")
+# -------------------------------
+# INPUT SECTION (Styled)
+# -------------------------------
 
-# Student Name
-student_name = st.text_input("Enter Student Name")
+with st.container():
+    st.subheader("👤 Student Details")
 
-# Initialize session state
-if "student" not in st.session_state:
-    st.session_state.student = None
+    name = st.text_input("Enter Student Name")
 
-if student_name:
-    st.session_state.student = Student(student_name)
+    if name:
+        st.session_state.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)
+with st.container():
+    st.subheader("📘 Add Subject")
 
-# 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!")
+    col1, col2, col3 = st.columns(3)
 
-# Analyze Button
-if st.button("Analyze Performance"):
-    student = st.session_state.student
+    with col1:
+        subject_name = st.text_input("Subject")
 
-    if student and student.subjects:
-        df = student.get_data()
+    with col2:
+        hours = st.number_input("Study Hours", min_value=0.0, step=0.5)
 
-        st.subheader("📊 Data Table")
-        st.dataframe(df)
+    with col3:
+        marks = st.number_input("Marks", min_value=0, max_value=100)
 
-        avg = student.calculate_average()
-        st.write(f"Average Marks: {avg:.2f}")
+    if st.button("➕ Add Subject"):
+        if not subject_name:
+            st.error("Enter subject name!")
+        elif st.session_state.student is None:
+            st.error("Enter student name first!")
+        else:
+            sub = Subject(subject_name, hours, marks)
+            st.session_state.student.add_subject(sub)
+            st.success(f"{subject_name} added successfully!")
 
-        max_marks, min_marks = student.get_max_min()
-        st.write(f"Highest Marks: {max_marks}")
-        st.write(f"Lowest Marks: {min_marks}")
+# -------------------------------
+# ANALYSIS SECTION
+# -------------------------------
 
-        grade = student.get_grade(avg)
-        st.write(f"Current Grade: {grade}")
+st.markdown("---")
+if st.button("📊 Analyze Performance"):
 
-        predicted = student.predict_performance(extra_hours=2)
-        predicted_grade = student.get_grade(predicted)
+    student = st.session_state.student
 
-        st.write(f"📈 If you study 2 more hours, expected marks: {predicted:.2f}")
-        st.write(f"Predicted Grade: {predicted_grade}")
+    if student is None or len(student.subjects) == 0:
+        st.error("Please add subjects first!")
+    else:
+        df = student.get_dataframe()
 
-        st.write(f"🏆 Best Subject: {student.best_subject()}")
-        st.write(f"⚠ Weak Subject: {student.weak_subject()}")
+        st.subheader("📋 Subject Data")
+        st.dataframe(df, use_container_width=True)
 
-        st.write("💡 Suggestion:", student.suggest_improvement())
+        avg = student.calculate_average()
+        max_m, min_m = student.get_max_min()
+        grade = student.get_grade(avg)
+        predicted = student.predict_performance()
+        predicted_grade = student.get_grade(predicted)
 
-        # Simple chart
-        st.subheader("📉 Study Hours vs Marks")
-        st.line_chart(df.set_index("Subject")[["Study Hours", "Marks"]])
+        # -------------------------------
+        # METRICS UI
+        # -------------------------------
+        col1, col2, col3 = st.columns(3)
 
-    else:
-        st.error("Please add subjects first!")
+        col1.metric("Average", f"{avg:.2f}")
+        col2.metric("Highest", max_m)
+        col3.metric("Lowest", min_m)
 
-# -------------------------------
-# KAGGLE DATASET INTEGRATION (OPTIONAL)
-# -------------------------------
+        # -------------------------------
+        # PROGRESS BAR (NEW FEATURE)
+        # -------------------------------
+        st.subheader("📈 Performance Progress")
+        st.progress(int(avg))
 
-st.subheader("📂 Optional: Load Dataset")
+        st.write(f"🎯 Current Grade: **{grade}**")
 
-uploaded_file = st.file_uploader("Upload CSV Dataset", type=["csv"])
+        # -------------------------------
+        # SMART PREDICTION
+        # -------------------------------
+        st.subheader("🔮 Smart Prediction")
 
-if uploaded_file:
-    df = pd.read_csv(uploaded_file)
+        st.write(f"If you study 2 more hours daily:")
 
-    st.write("Raw Data:", df.head())
+        st.success(f"📊 Expected Marks: {predicted:.2f}")
+        st.success(f"🏆 Predicted Grade: {predicted_grade}")
 
-    # Cleaning
-    df = df.drop_duplicates()
-    df = df.fillna(df.mean(numeric_only=True))
+        # -------------------------------
+        # INSIGHTS
+        # -------------------------------
+        st.subheader("📌 Insights")
 
-    st.write("Cleaned Data:", df.head())
+        st.write(f"🏆 Best Subject: **{student.best_subject()}**")
+        st.write(f"⚠ Weak Subject: **{student.weak_subject()}**")
 
-    st.write("Dataset Average:", df.mean(numeric_only=True))
+        st.info(student.suggest_improvement())
 
-    st.success("Dataset processed successfully!")
+        # -------------------------------
+        # CHART
+        # -------------------------------
+        st.subheader("📉 Study Trend")
+        st.line_chart(df.set_index("Subject"))