Update app.py

app.py

@@ -1,4 +1,198 @@
-# Smart Study Planner - FIXED VERSION
+# # Smart Study Planner - FIXED VERSION
+
+# import streamlit as st
+# import numpy as np
+# import pandas as pd
+
+# # -------------------------------
+# # PAGE CONFIG
+# # -------------------------------
+# st.set_page_config(page_title="Smart Study Planner", page_icon="📚", layout="centered")
+
+# st.title("📚 Smart Study Planner with Performance Prediction")
+# st.markdown("Track your study progress and predict performance smartly 🚀")
+
+# # -------------------------------
+# # OOP CLASSES
+# # -------------------------------
+
+# class Subject:
+#     def __init__(self, name, hours, marks):
+#         self.name = name
+#         self.hours = 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_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 = [s.marks for s in self.subjects]
+#         return np.mean(marks) if marks else 0
+
+#     def get_max_min(self):
+#         marks = [s.marks for s in self.subjects]
+#         return np.max(marks), np.min(marks)
+
+#     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]
+#         return 0
+
+#     def predict_performance(self):
+#         trend = self.performance_trend()
+#         predicted = []
+
+#         for s in self.subjects:
+#             boost = (2 * 4) + (trend * 5)  # smart logic
+#             predicted.append(min(s.marks + boost, 100))
+
+#         return np.mean(predicted) if predicted 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"
+#         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):
+#         return f"Focus more on {self.weak_subject()} and increase study time by 2 hours."
+
+
+# # -------------------------------
+# # SESSION STATE FIX (IMPORTANT)
+# # -------------------------------
+
+# if "student" not in st.session_state:
+#     st.session_state.student = None
+
+# if "student_name" not in st.session_state:
+#     st.session_state.student_name = ""
+
+# # -------------------------------
+# # INPUT UI
+# # -------------------------------
+
+# st.subheader("👤 Student Information")
+
+# name = st.text_input("Enter Student Name", value=st.session_state.student_name)
+
+# if name:
+#     st.session_state.student_name = name
+
+#     # FIX: only create once
+#     if st.session_state.student is None:
+#         st.session_state.student = Student(name)
+
+
+# st.subheader("📘 Add Subject")
+
+# col1, col2, col3 = st.columns(3)
+
+# with col1:
+#     subject_name = st.text_input("Subject")
+
+# with col2:
+#     hours = st.number_input("Study Hours", min_value=0.0, step=0.5)
+
+# with col3:
+#     marks = st.number_input("Marks", min_value=0, max_value=100)
+
+# # -------------------------------
+# # ADD SUBJECT
+# # -------------------------------
+
+# if st.button("➕ Add Subject"):
+#     if not subject_name:
+#         st.error("Please enter subject name")
+#     elif st.session_state.student is None:
+#         st.error("Please 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!")
+
+# # -------------------------------
+# # ANALYSIS
+# # -------------------------------
+
+# st.markdown("---")
+
+# if st.button("📊 Analyze Performance"):
+
+#     student = st.session_state.student
+
+#     if student is None or len(student.subjects) == 0:
+#         st.error("Please add subjects first!")
+#     else:
+#         df = student.get_dataframe()
+
+#         st.subheader("📋 Performance Table")
+#         st.dataframe(df, use_container_width=True)
+
+#         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)
+
+#         # ---------------- METRICS ----------------
+#         col1, col2, col3 = st.columns(3)
+
+#         col1.metric("Average Marks", f"{avg:.2f}")
+#         col2.metric("Highest Marks", max_m)
+#         col3.metric("Lowest Marks", min_m)
+
+#         # ---------------- PROGRESS BAR ----------------
+#         st.subheader("📈 Performance Progress")
+#         st.progress(int(avg))
+
+#         st.write(f"🎯 Current Grade: **{grade}**")
+
+#         # ---------------- PREDICTION ----------------
+#         st.subheader("🔮 Smart Prediction")
+
+#         st.success(f"Expected Marks (if improved): {predicted:.2f}")
+#         st.success(f"Predicted Grade: {predicted_grade}")
+
+#         # ---------------- INSIGHTS ----------------
+#         st.subheader("📌 Insights")
+
+#         st.write(f"🏆 Best Subject: **{student.best_subject()}**")
+#         st.write(f"⚠ Weak Subject: **{student.weak_subject()}**")
+
+#         st.info(student.suggest_improvement())
+
+#         # ---------------- CHART ----------------
+#         st.subheader("📉 Study vs Performance Trend")
+#         st.line_chart(df.set_index("Subject"))
+# Smart Study Planner - FINAL VERSION
 
 import streamlit as st
 import numpy as np
@@ -9,11 +203,11 @@ import pandas as pd
 # -------------------------------
 st.set_page_config(page_title="Smart Study Planner", page_icon="📚", layout="centered")
 
-st.title("📚 Smart Study Planner with Performance Prediction")
-st.markdown("Track your study progress and predict performance smartly 🚀")
+st.title("📚 Smart Study Planner with Smart Prediction")
+st.markdown("Analyze your study habits and improve performance 🚀")
 
 # -------------------------------
-# OOP CLASSES
+# CLASSES (OOP)
 # -------------------------------
 
 class Subject:
@@ -54,15 +248,35 @@ class Student:
             return np.corrcoef(hours, marks)[0][1]
         return 0
 
+    # -------------------------------
+    # AI-STYLE SMART PREDICTION
+    # -------------------------------
     def predict_performance(self):
-        trend = self.performance_trend()
-        predicted = []
+        hours = np.array([s.hours for s in self.subjects])
+        marks = np.array([s.marks for s in self.subjects])
 
-        for s in self.subjects:
-            boost = (2 * 4) + (trend * 5)  # smart logic
-            predicted.append(min(s.marks + boost, 100))
+        if len(hours) == 0:
+            return 0
 
-        return np.mean(predicted) if predicted else 0
+        avg_marks = np.mean(marks)
+        avg_hours = np.mean(hours)
+
+        # Trend (relationship between hours and marks)
+        if len(hours) > 1:
+            trend = np.corrcoef(hours, marks)[0][1]
+        else:
+            trend = 0
+
+        # Consistency (low variance = better)
+        consistency = 1 / (1 + np.var(marks))
+
+        # Smart prediction formula
+        predicted = avg_marks \
+                    + (avg_hours * 1.5) \
+                    + (trend * 10) \
+                    + (consistency * 5)
+
+        return min(predicted, 100)
 
     def get_grade(self, marks):
         if marks >= 85:
@@ -82,11 +296,12 @@ class Student:
         return min(self.subjects, key=lambda x: x.marks).name
 
     def suggest_improvement(self):
-        return f"Focus more on {self.weak_subject()} and increase study time by 2 hours."
+        weak = self.weak_subject()
+        return f"Focus more on {weak} and increase study hours by at least 2 hours."
 
 
 # -------------------------------
-# SESSION STATE FIX (IMPORTANT)
+# SESSION STATE (FIXED LOGIC)
 # -------------------------------
 
 if "student" not in st.session_state:
@@ -96,20 +311,21 @@ if "student_name" not in st.session_state:
     st.session_state.student_name = ""
 
 # -------------------------------
-# INPUT UI
+# INPUT SECTION
 # -------------------------------
 
 st.subheader("👤 Student Information")
 
 name = st.text_input("Enter Student Name", value=st.session_state.student_name)
 
-if name:
+# FIX: create new student when name changes
+if name != st.session_state.student_name:
     st.session_state.student_name = name
+    st.session_state.student = Student(name)
 
-    # FIX: only create once
-    if st.session_state.student is None:
-        st.session_state.student = Student(name)
-
+# -------------------------------
+# ADD SUBJECT
+# -------------------------------
 
 st.subheader("📘 Add Subject")
 
@@ -124,22 +340,18 @@ with col2:
 with col3:
     marks = st.number_input("Marks", min_value=0, max_value=100)
 
-# -------------------------------
-# ADD SUBJECT
-# -------------------------------
-
 if st.button("➕ Add Subject"):
-    if not subject_name:
-        st.error("Please enter subject name")
-    elif st.session_state.student is None:
-        st.error("Please enter student name first")
+    if not name:
+        st.error("Please enter student name first!")
+    elif not subject_name:
+        st.error("Please enter subject name!")
     else:
         sub = Subject(subject_name, hours, marks)
         st.session_state.student.add_subject(sub)
         st.success(f"{subject_name} added successfully!")
 
 # -------------------------------
-# ANALYSIS
+# ANALYSIS SECTION
 # -------------------------------
 
 st.markdown("---")
@@ -159,12 +371,12 @@ if st.button("📊 Analyze Performance"):
         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)
 
         # ---------------- METRICS ----------------
         col1, col2, col3 = st.columns(3)
-
         col1.metric("Average Marks", f"{avg:.2f}")
         col2.metric("Highest Marks", max_m)
         col3.metric("Lowest Marks", min_m)
@@ -177,18 +389,21 @@ if st.button("📊 Analyze Performance"):
 
         # ---------------- PREDICTION ----------------
         st.subheader("🔮 Smart Prediction")
-
-        st.success(f"Expected Marks (if improved): {predicted:.2f}")
+        st.success(f"Expected Marks: {predicted:.2f}")
         st.success(f"Predicted Grade: {predicted_grade}")
 
-        # ---------------- INSIGHTS ----------------
+        # ---------------- EXTRA INSIGHTS ----------------
         st.subheader("📌 Insights")
-
         st.write(f"🏆 Best Subject: **{student.best_subject()}**")
         st.write(f"⚠ Weak Subject: **{student.weak_subject()}**")
 
         st.info(student.suggest_improvement())
 
+        # ---------------- CONSISTENCY SCORE ----------------
+        marks_list = [s.marks for s in student.subjects]
+        consistency_score = 1 / (1 + np.var(marks_list))
+        st.write(f"📊 Consistency Score: {round(consistency_score, 2)}")
+
         # ---------------- CHART ----------------
         st.subheader("📉 Study vs Performance Trend")
         st.line_chart(df.set_index("Subject"))