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Update app.py
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app.py
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# Smart Study Planner -
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import streamlit as st
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import numpy as np
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import pandas as pd
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# -------------------------------
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# PAGE CONFIG
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# -------------------------------
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st.set_page_config(page_title="Smart Study Planner", page_icon="๐", layout="centered")
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st.title("๐ Smart Study Planner with
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st.markdown("
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# -------------------------------
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#
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# -------------------------------
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class Subject:
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marks = [s.marks for s in self.subjects]
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if len(hours) > 1:
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return np.corrcoef(hours, marks)[0][1]
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return 0
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def predict_performance(self):
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predicted = []
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for s in self.subjects:
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predicted.append(min(s.marks +
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return np.mean(predicted) if predicted else 0
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return min(self.subjects, key=lambda x: x.marks).name
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def suggest_improvement(self):
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return f"Focus more on {weak} and increase study hours by at least 2 hours."
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# -------------------------------
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# SESSION STATE
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# -------------------------------
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if "student" not in st.session_state:
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st.session_state.student = None
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# -------------------------------
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# INPUT
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# -------------------------------
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st.session_state.student = Student(name)
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with st.container():
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st.subheader("๐ Add Subject")
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# -------------------------------
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# ANALYSIS
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# -------------------------------
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st.markdown("---")
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if st.button("๐ Analyze Performance"):
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student = st.session_state.student
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else:
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df = student.get_dataframe()
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st.subheader("๐
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st.dataframe(df, use_container_width=True)
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avg = student.calculate_average()
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predicted = student.predict_performance()
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predicted_grade = student.get_grade(predicted)
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# -------------------------------
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# METRICS UI
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# -------------------------------
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col1, col2, col3 = st.columns(3)
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col1.metric("Average", f"{avg:.2f}")
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col2.metric("Highest", max_m)
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col3.metric("Lowest", min_m)
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# -------------------------------
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# PROGRESS BAR (NEW FEATURE)
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# -------------------------------
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st.subheader("๐ Performance Progress")
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st.progress(int(avg))
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st.write(f"๐ฏ Current Grade: **{grade}**")
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# -------------------------------
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# SMART PREDICTION
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# -------------------------------
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st.subheader("๐ฎ Smart Prediction")
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st.
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st.success(f"๐ Expected Marks: {predicted:.2f}")
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st.success(f"๐ Predicted Grade: {predicted_grade}")
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# -------------------------------
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# INSIGHTS
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# -------------------------------
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st.subheader("๐ Insights")
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st.write(f"๐ Best Subject: **{student.best_subject()}**")
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st.info(student.suggest_improvement())
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# -------------------------------
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# -------------------------------
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st.subheader("๐ Study Trend")
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st.line_chart(df.set_index("Subject"))
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# Smart Study Planner - FIXED VERSION
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import streamlit as st
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import numpy as np
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import pandas as pd
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# -------------------------------
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# PAGE CONFIG
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# -------------------------------
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st.set_page_config(page_title="Smart Study Planner", page_icon="๐", layout="centered")
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st.title("๐ Smart Study Planner with Performance Prediction")
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st.markdown("Track your study progress and predict performance smartly ๐")
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# -------------------------------
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# OOP CLASSES
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# -------------------------------
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class Subject:
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marks = [s.marks for s in self.subjects]
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if len(hours) > 1:
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return np.corrcoef(hours, marks)[0][1]
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return 0
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def predict_performance(self):
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predicted = []
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for s in self.subjects:
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boost = (2 * 4) + (trend * 5) # smart logic
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predicted.append(min(s.marks + boost, 100))
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return np.mean(predicted) if predicted else 0
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return min(self.subjects, key=lambda x: x.marks).name
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def suggest_improvement(self):
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return f"Focus more on {self.weak_subject()} and increase study time by 2 hours."
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# -------------------------------
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# SESSION STATE FIX (IMPORTANT)
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# -------------------------------
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if "student" not in st.session_state:
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st.session_state.student = None
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if "student_name" not in st.session_state:
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st.session_state.student_name = ""
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# -------------------------------
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# INPUT UI
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# -------------------------------
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st.subheader("๐ค Student Information")
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name = st.text_input("Enter Student Name", value=st.session_state.student_name)
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if name:
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st.session_state.student_name = name
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# FIX: only create once
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if st.session_state.student is None:
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st.session_state.student = Student(name)
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st.subheader("๐ Add Subject")
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col1, col2, col3 = st.columns(3)
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with col1:
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subject_name = st.text_input("Subject")
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with col2:
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hours = st.number_input("Study Hours", min_value=0.0, step=0.5)
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with col3:
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marks = st.number_input("Marks", min_value=0, max_value=100)
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# -------------------------------
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# ADD SUBJECT
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# -------------------------------
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if st.button("โ Add Subject"):
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if not subject_name:
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st.error("Please enter subject name")
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elif st.session_state.student is None:
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st.error("Please enter student name first")
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else:
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sub = Subject(subject_name, hours, marks)
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st.session_state.student.add_subject(sub)
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st.success(f"{subject_name} added successfully!")
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# -------------------------------
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# ANALYSIS
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# -------------------------------
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st.markdown("---")
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if st.button("๐ Analyze Performance"):
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student = st.session_state.student
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else:
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df = student.get_dataframe()
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st.subheader("๐ Performance Table")
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st.dataframe(df, use_container_width=True)
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avg = student.calculate_average()
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predicted = student.predict_performance()
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predicted_grade = student.get_grade(predicted)
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# ---------------- METRICS ----------------
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col1, col2, col3 = st.columns(3)
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col1.metric("Average Marks", f"{avg:.2f}")
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col2.metric("Highest Marks", max_m)
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col3.metric("Lowest Marks", min_m)
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# ---------------- PROGRESS BAR ----------------
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st.subheader("๐ Performance Progress")
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st.progress(int(avg))
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st.write(f"๐ฏ Current Grade: **{grade}**")
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# ---------------- PREDICTION ----------------
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st.subheader("๐ฎ Smart Prediction")
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st.success(f"Expected Marks (if improved): {predicted:.2f}")
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st.success(f"Predicted Grade: {predicted_grade}")
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# ---------------- INSIGHTS ----------------
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st.subheader("๐ Insights")
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st.write(f"๐ Best Subject: **{student.best_subject()}**")
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st.info(student.suggest_improvement())
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# ---------------- CHART ----------------
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st.subheader("๐ Study vs Performance Trend")
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st.line_chart(df.set_index("Subject"))
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