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")

# -------------------------------
# CUSTOM CSS (UI DESIGN)
# -------------------------------
st.markdown("""
<style>
/* Background */
.stApp {
    background: linear-gradient(to right, #e0f7fa, #e1bee7);
}

/* Headings */
h1, h2, h3 {
    color: #4b0082;
    text-align: center;
}

/* Buttons */
.stButton>button {
    background-color: #6c63ff;
    color: white;
    border-radius: 10px;
    height: 3em;
    width: 100%;
    font-size: 16px;
    font-weight: bold;
}

/* Dataframe */
.css-1d391kg {
    border-radius: 10px;
}

/* Input boxes */
input {
    border-radius: 8px !important;
}
</style>
""", unsafe_allow_html=True)
st.title("📚 Smart Study Planner with Smart Prediction")
st.markdown("Analyze your study habits and improve performance 🚀")

# -------------------------------
# CLASSES (OOP)
# -------------------------------

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)

    # -------------------------------
    # SAFE TREND CALCULATION
    # -------------------------------
    def performance_trend(self):
        hours = np.array([s.hours for s in self.subjects])
        marks = np.array([s.marks for s in self.subjects])

        if len(hours) > 1:
            trend = np.corrcoef(hours, marks)[0][1]
            if np.isnan(trend):
                trend = 0
            return np.clip(trend, -1, 1)
        return 0

    # -------------------------------
    # AI-STYLE SMART PREDICTION (FIXED)
    # -------------------------------
    def predict_performance(self):
        hours = np.array([s.hours for s in self.subjects])
        marks = np.array([s.marks for s in self.subjects])

        if len(hours) == 0:
            return 0

        avg_marks = np.mean(marks)
        avg_hours = np.mean(hours)
        trend = self.performance_trend()

        variance = np.var(marks)
        consistency = 1 / (1 + variance)

        predicted = avg_marks \
                    + (avg_hours * 1.5) \
                    + (trend * 10) \
                    + (consistency * 5)

        return float(min(predicted, 100))

    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 (FIXED)
# -------------------------------

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 SECTION
# -------------------------------

st.subheader("👤 Student Information")

name = st.text_input("Enter Student Name", value=st.session_state.student_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)

# -------------------------------
# ADD SUBJECT
# -------------------------------

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)

if st.button("➕ Add Subject"):
    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 SECTION
# -------------------------------

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")

        if np.isnan(predicted):
            st.error("Prediction error due to insufficient variation in data")
        else:
            st.success(f"Expected Marks: {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())

        # ---------------- CONSISTENCY ----------------
        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"))