app.py

import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import json
from flask import Flask, render_template, request, session, send_file
import pandas as pd
import os
import io
import base64
import numpy as np
from datetime import datetime
from weasyprint import HTML
import tempfile
import warnings
import secrets
from typing import Dict, List, Tuple

app = Flask(__name__)
app.secret_key = secrets.token_hex(16)  # Generates a secure random key
app.config['SESSION_TYPE'] = 'filesystem'
warnings.filterwarnings('ignore')

# Add custom filter for number formatting
@app.template_filter('format_number')
def format_number(value):
    """Format number with commas as thousand separators"""
    try:
        return "{:,}".format(int(value))
    except (ValueError, TypeError):
        return value

# Directory to store generated images
plot_dir = 'static/plots'
os.makedirs(plot_dir, exist_ok=True)

def save_plot(fig, plots_dict, plot_name):
    """Convert Plotly figure to HTML and add to plots dictionary."""
    try:
        plot_html = fig.to_html(full_html=False, include_plotlyjs='cdn')
        plots_dict[plot_name] = plot_html
    except Exception as e:
        print(f"Error saving plot {plot_name}: {str(e)}")

@app.route('/')
def index():
    """Render the main page."""
    return render_template('index.html')

from flask import jsonify

@app.route('/analyze', methods=['POST'])
def analyze():
    """Handle file upload and store data for later analysis."""
    if 'file' not in request.files:
        return render_template('index.html', error='No file uploaded')

    file = request.files['file']
    if file.filename == '':
        return render_template('index.html', error='No file selected')

    if not file.filename.lower().endswith('.csv'):
        return render_template('index.html', error='Only CSV files are allowed')

    try:
        # Read and validate the CSV file
        data = pd.read_csv(file, encoding='utf-8')
        validate_data(data)
        
        # Show questionnaire without analysis
        return render_template('index.html',
            show_scholarship_questionnaire=True,
            file_uploaded=True
        )

    except Exception as e:
        return render_template('index.html', error=f'An error occurred: {str(e)}')

def validate_data(data):
    """Validate the uploaded data."""
    if data.empty:
        raise ValueError("The uploaded file is empty")
        
    required_columns = [
        'Category', 'Scholarship Name', 'Eligibility', 'Benefits',
        'Provider', 'Year', 'Number of Beneficiaries',
        'Total Students Eligible', 'Percentage Benefited'
    ]
    
    missing_columns = [col for col in required_columns if col not in data.columns]
    if missing_columns:
        raise ValueError(f"Missing required columns: {', '.join(missing_columns)}")
        
    # Validate numeric columns
    numeric_columns = ['Number of Beneficiaries', 'Total Students Eligible', 'Percentage Benefited']
    for col in numeric_columns:
        if not pd.to_numeric(data[col], errors='coerce').notnull().all():
            raise ValueError(f"Column '{col}' contains invalid numeric values")
            
    return True

def perform_scholarship_swot(data):
    """Perform SWOT analysis for scholarship data."""
    swot = {
        'strengths': [],
        'weaknesses': [],
        'opportunities': [],
        'threats': []
    }
    
    total_beneficiaries = data['Number of Beneficiaries'].sum()
    total_eligible = data['Total Students Eligible'].sum()
    coverage_rate = (total_beneficiaries / total_eligible) * 100

    # Analyze coverage rate and add to appropriate category
    if coverage_rate >= 75:
        swot['strengths'].append(f"High coverage rate of {coverage_rate:.1f}%")
    elif coverage_rate >= 50:
        swot['opportunities'].append(f"Room to improve coverage rate (currently {coverage_rate:.1f}%)")
        swot['weaknesses'].append(f"Moderate coverage rate needs improvement ({coverage_rate:.1f}%)")
    else:
        swot['weaknesses'].append(f"Low coverage rate of {coverage_rate:.1f}%")

    # Analyze year-over-year growth
    yearly_beneficiaries = data.groupby('Year')['Number of Beneficiaries'].sum()
    if len(yearly_beneficiaries) > 1:
        growth_rate = ((yearly_beneficiaries.iloc[-1] / yearly_beneficiaries.iloc[0]) - 1) * 100
        if growth_rate > 20:
            swot['strengths'].append(f"Strong growth in beneficiaries ({growth_rate:.1f}% overall)")
        elif growth_rate > 0:
            swot['opportunities'].append(f"Moderate growth can be improved ({growth_rate:.1f}% overall)")
        else:
            swot['weaknesses'].append(f"Negative growth in beneficiaries ({growth_rate:.1f}% overall)")
            swot['threats'].append("Declining participation trend")

    # Analyze provider diversity
    provider_count = data['Provider'].nunique()
    if provider_count > 5:
        swot['strengths'].append(f"Diverse range of providers ({provider_count} different providers)")
    else:
        swot['weaknesses'].append(f"Limited provider diversity (only {provider_count} providers)")
        swot['opportunities'].append("Potential to expand provider network")

    # Analyze category distribution
    category_dist = data.groupby('Category')['Number of Beneficiaries'].sum()
    max_category_pct = (category_dist.max() / total_beneficiaries) * 100
    min_category_pct = (category_dist.min() / total_beneficiaries) * 100
    
    if max_category_pct > 40:
        swot['weaknesses'].append(f"Uneven distribution across categories (highest: {max_category_pct:.1f}%)")
    if min_category_pct < 10:
        swot['weaknesses'].append(f"Underrepresented categories (lowest: {min_category_pct:.1f}%)")

    # Analyze utilization rates
    utilization_rates = (data['Number of Beneficiaries'] / data['Total Students Eligible']) * 100
    low_util_count = (utilization_rates < 30).sum()
    if low_util_count > 0:
        swot['weaknesses'].append(f"Low utilization in {low_util_count} scholarship programs")

    # Add general insights
    swot['opportunities'].append("Potential for new scholarship categories")
    swot['threats'].extend([
        "Changes in funding availability may affect program sustainability",
        "Increasing competition for limited scholarship resources",
        "Changing eligibility criteria may affect accessibility"
    ])

    return swot

@app.route('/analyze_diversity', methods=['POST'])
def analyze_diversity():
    """Handle diversity file upload and store data."""
    if 'file' not in request.files:
        return render_template('index.html', error='No file uploaded')

    file = request.files['file']
    if file.filename == '':
        return render_template('index.html', error='No file selected')

    try:
        data = pd.read_csv(file, encoding='utf-8')
        required_columns = ['Gender', 'Category', 'Branch']
        
        missing_columns = [col for col in required_columns if col not in data.columns]
        if missing_columns:
            return render_template('index.html', 
                error=f'Missing required columns: {", ".join(missing_columns)}')

        # Show questionnaire without analysis
        return render_template('index.html',
            show_diversity_questionnaire=True,
            file_uploaded=True
        )

    except Exception as e:
        return render_template('index.html', error=f'An error occurred: {str(e)}')

def generate_scholarship_plots(data):
    """Generate Plotly plots for scholarship data."""
    plots = {}
    
    try:
        # Trend Analysis
        beneficiaries_trend = data.groupby('Year')['Number of Beneficiaries'].sum().reset_index()
        if not beneficiaries_trend.empty:
            fig = px.line(beneficiaries_trend, 
                         x='Year', 
                         y='Number of Beneficiaries',
                         markers=True,
                         title='Trend of Scholarship Beneficiaries Over Years')
            fig.update_layout(
                template='plotly_white',
                xaxis_title='Year',
                yaxis_title='Number of Beneficiaries'
            )
            save_plot(fig, plots, 'trend_analysis')
            
        # Category Distribution
        category_data = data.groupby('Category')['Number of Beneficiaries'].sum()
        fig = px.pie(values=category_data.values, 
                    names=category_data.index,
                    title='Scholarship Distribution by Category')
        fig.update_layout(template='plotly_white')
        save_plot(fig, plots, 'category_distribution')
        
        # Provider Distribution
        provider_data = data.groupby('Provider')['Number of Beneficiaries'].sum()
        fig = px.bar(x=provider_data.index, 
                    y=provider_data.values,
                    title='Distribution by Scholarship Provider')
        fig.update_layout(
            template='plotly_white',
            xaxis_title='Provider',
            yaxis_title='Number of Beneficiaries',
            xaxis_tickangle=45
        )
        save_plot(fig, plots, 'provider_distribution')
        
    except Exception as e:
        print(f"Error in generate_scholarship_plots: {str(e)}")
        
    return plots

def generate_diversity_plots(data):
    """Generate Plotly plots for diversity data."""
    plots = {}

    try:
        # Gender distribution with average percentile
        gender_percentile = data.groupby('Gender')['Percentile_obtained_in_entrance'].mean()
        fig = make_subplots(specs=[[{"secondary_y": True}]])
        
        # Add bar chart for count
        gender_counts = data['Gender'].value_counts()
        fig.add_trace(
            go.Bar(x=gender_counts.index, y=gender_counts.values, name="Count"),
            secondary_y=False
        )
        
        # Add line chart for average percentile
        fig.add_trace(
            go.Scatter(x=gender_percentile.index, y=gender_percentile.values, 
                      name="Avg. Percentile", line=dict(color='red')),
            secondary_y=True
        )
        
        fig.update_layout(
            title='Gender Distribution and Average Entrance Percentile',
            template='plotly_white',
            barmode='group'
        )
        fig.update_yaxes(title_text="Number of Students", secondary_y=False)
        fig.update_yaxes(title_text="Average Entrance Percentile", secondary_y=True)
        
        save_plot(fig, plots, 'gender_distribution')

        # Branch distribution with performance metrics
        branch_metrics = data.groupby('Branch').agg({
            'Percentile_obtained_in_entrance': 'mean',
            'Board_Percentage': 'mean'
        }).round(2)
        
        fig = make_subplots(specs=[[{"secondary_y": True}]])
        
        # Add bar chart for entrance percentile
        fig.add_trace(
            go.Bar(x=branch_metrics.index, 
                  y=branch_metrics['Percentile_obtained_in_entrance'],
                  name="Entrance Percentile"),
            secondary_y=False
        )
        
        # Add line chart for board percentage
        fig.add_trace(
            go.Scatter(x=branch_metrics.index, 
                      y=branch_metrics['Board_Percentage'],
                      name="Board %", line=dict(color='red')),
            secondary_y=True
        )
        
        fig.update_layout(
            title='Branch-wise Performance Metrics',
            template='plotly_white',
            xaxis_tickangle=45
        )
        fig.update_yaxes(title_text="Average Entrance Percentile", secondary_y=False)
        fig.update_yaxes(title_text="Average Board Percentage", secondary_y=True)
        
        save_plot(fig, plots, 'branch_distribution')

        # Category-wise performance box plot
        fig = go.Figure()
        
        fig.add_trace(go.Box(
            x=data['Category'],
            y=data['Percentile_obtained_in_entrance'],
            name='Entrance Percentile'
        ))
        
        fig.update_layout(
            title='Category-wise Entrance Percentile Distribution',
            template='plotly_white',
            yaxis_title='Entrance Percentile',
            xaxis_title='Category'
        )
        
        save_plot(fig, plots, 'category_distribution')

        # Performance correlation heatmap
        numeric_cols = ['Percentile_obtained_in_entrance', 'Board_Percentage']
        corr_matrix = data[numeric_cols].corr()
        
        fig = go.Figure(data=go.Heatmap(
            z=corr_matrix.values,
            x=corr_matrix.columns,
            y=corr_matrix.columns,
            colorscale='RdBu',
            zmin=-1, zmax=1
        ))
        
        fig.update_layout(
            title='Performance Correlation Matrix',
            template='plotly_white'
        )
        
        save_plot(fig, plots, 'correlation_matrix')

    except Exception as e:
        print(f"Error in generate_diversity_plots: {str(e)}")
        
    return plots


def analyze_performance(data):
    """Analyze strengths and weaknesses of the scholarship program."""
    insights = {
        'strengths': [],
        'weaknesses': [],
        'improvements': []
    }
    
    # Calculate key metrics
    total_beneficiaries = data['Number of Beneficiaries'].sum()
    total_eligible = data['Total Students Eligible'].sum()
    coverage_rate = (total_beneficiaries / total_eligible) * 100
    
    # Coverage Rate Analysis
    if coverage_rate >= 75:
        insights['strengths'].append(f"Exceptional coverage rate of {coverage_rate:.1f}%")
    elif coverage_rate >= 50:
        insights['strengths'].append(f"Good coverage rate of {coverage_rate:.1f}%")
    else:
        insights['weaknesses'].append(f"Low coverage rate of {coverage_rate:.1f}%")
        insights['improvements'].append("Implement awareness campaigns to increase scholarship applications")
    
    # Year-over-Year Growth Analysis
    yearly_data = data.groupby('Year')['Number of Beneficiaries'].sum()
    if len(yearly_data) > 1:
        yoy_growth = ((yearly_data.iloc[-1] - yearly_data.iloc[0]) / yearly_data.iloc[0]) * 100
        if yoy_growth > 0:
            insights['strengths'].append(f"Positive growth in beneficiaries ({yoy_growth:.1f}% overall)")
        else:
            insights['weaknesses'].append(f"Declining number of beneficiaries ({abs(yoy_growth):.1f}% decrease)")
            insights['improvements'].append("Review and revise scholarship allocation strategy")
    
    # Provider Analysis
    provider_count = data['Provider'].nunique()
    if provider_count > 10:
        insights['strengths'].append(f"Diverse range of scholarship providers ({provider_count} providers)")
    else:
        insights['weaknesses'].append(f"Limited number of scholarship providers ({provider_count} providers)")
        insights['improvements'].append("Engage with more institutions and organizations for scholarship partnerships")
    
    # Category Distribution Analysis
    category_dist = data.groupby('Category')['Number of Beneficiaries'].sum()
    max_category_pct = (category_dist.max() / total_beneficiaries) * 100
    if max_category_pct > 50:
        insights['weaknesses'].append(f"Uneven distribution across categories (max {max_category_pct:.1f}% in one category)")
        insights['improvements'].append("Balance scholarship distribution across different categories")
    else:
        insights['strengths'].append("Well-balanced distribution across categories")
    
    # Eligibility vs Beneficiaries Analysis
    utilization_rates = (data['Number of Beneficiaries'] / data['Total Students Eligible']) * 100
    low_util_count = (utilization_rates < 30).sum()
    if low_util_count > 0:
        insights['weaknesses'].append(f"{low_util_count} scholarships have utilization rates below 30%")
        insights['improvements'].append("Review eligibility criteria for low-utilization scholarships")
    
    # Additional Improvements
    insights['improvements'].extend([
        "Develop targeted outreach programs for underrepresented groups",
        "Streamline application process to increase accessibility",
        "Implement regular feedback mechanisms from beneficiaries"
    ])
    
    return insights

def perform_swot_analysis(data):
    """Perform SWOT analysis on the diversity dataset."""
    swot = {
        'strengths': [],
        'weaknesses': [],
        'opportunities': [],
        'threats': []
    }
    
    try:
        # Gender diversity analysis
        gender_dist = data['Gender'].value_counts(normalize=True) * 100
        female_ratio = gender_dist.get('Female', 0)
        
        if female_ratio >= 40:
            swot['strengths'].append(f"Strong gender diversity with {female_ratio:.1f}% female students")
            swot['strengths'].append("Above average female representation in STEM fields")
        elif female_ratio >= 30:
            swot['opportunities'].append(f"Potential to improve gender diversity (currently {female_ratio:.1f}% female students)")
            swot['opportunities'].append("Implement targeted recruitment for female students")
        else:
            swot['weaknesses'].append(f"Low gender diversity with only {female_ratio:.1f}% female students")
            swot['threats'].append("Risk of gender imbalance affecting campus culture")

        # Performance analysis
        avg_percentile = data['Percentile_obtained_in_entrance'].mean()
        std_percentile = data['Percentile_obtained_in_entrance'].std()
        
        if avg_percentile >= 80:
            swot['strengths'].append(f"High average entrance percentile ({avg_percentile:.1f})")
            swot['strengths'].append("Strong academic caliber of incoming students")
        elif avg_percentile < 60:
            swot['weaknesses'].append(f"Low average entrance percentile ({avg_percentile:.1f})")
            swot['threats'].append("May affect institution's academic reputation")

        if std_percentile > 20:
            swot['weaknesses'].append("High variability in student performance")
            swot['opportunities'].append("Implement targeted academic support programs")

        # Category representation
        category_dist = data['Category'].value_counts(normalize=True) * 100
        for category, percentage in category_dist.items():
            if percentage < 10:
                swot['weaknesses'].append(f"Low representation of {category} category ({percentage:.1f}%)")
                swot['opportunities'].append(f"Increase outreach to {category} category students")
                swot['threats'].append(f"Risk of {category} category underrepresentation")

        # Branch distribution
        branch_dist = data['Branch'].value_counts(normalize=True) * 100
        branches_above_25 = branch_dist[branch_dist > 25].index.tolist()
        branches_below_10 = branch_dist[branch_dist < 10].index.tolist()

        if branches_above_25:
            swot['strengths'].append(f"Strong presence in: {', '.join(branches_above_25)}")
            swot['threats'].append("Over-dependence on specific branches")

        if branches_below_10:
            swot['opportunities'].append(f"Potential for growth in: {', '.join(branches_below_10)}")
            swot['weaknesses'].append(f"Limited presence in: {', '.join(branches_below_10)}")

        # Performance correlation
        corr = data['Percentile_obtained_in_entrance'].corr(data['Board_Percentage'])
        if corr > 0.7:
            swot['strengths'].append("Strong correlation between board and entrance performance")
            swot['strengths'].append("Consistent academic performance across evaluations")
        elif corr < 0.3:
            swot['weaknesses'].append("Weak correlation between board and entrance performance")
            swot['opportunities'].append("Investigate factors affecting performance inconsistency")
            swot['threats'].append("Unpredictable student performance patterns")

        # Additional general insights
        swot['opportunities'].extend([
            "Develop mentorship programs for underrepresented groups",
            "Implement cross-branch collaborative programs",
            "Create targeted support systems for struggling students"
        ])

        swot['threats'].extend([
            "Increasing competition from other institutions",
            "Changing diversity trends in higher education",
            "Resource allocation challenges across diverse student needs"
        ])

    except Exception as e:
        print(f"Error in perform_swot_analysis: {str(e)}")
        swot['weaknesses'].append("Error in data analysis")

    return swot

def generate_diversity_insights(data):
    """Generate insights from diversity data."""
    insights = []
    
    # Total student count (each row represents a student)
    total_students = len(data)
    insights.append(f"Total number of students: {total_students:,}")
    
    # Gender distribution
    if 'Gender' in data.columns:
        gender_dist = data['Gender'].value_counts()
        for gender, count in gender_dist.items():
            percentage = (count/total_students) * 100
            insights.append(f"{gender}: {count:,} students ({percentage:.1f}%)")
    
    # Branch distribution
    if 'Branch' in data.columns:
        branch_dist = data['Branch'].value_counts()
        insights.append("\nTop 3 branches by enrollment:")
        for branch, count in branch_dist.nlargest(3).items():
            percentage = (count/total_students) * 100
            insights.append(f"{branch}: {count:,} students ({percentage:.1f}%)")
    
    # Category insights
    if 'Category' in data.columns:
        category_dist = data['Category'].value_counts()
        insights.append("\nCategory distribution:")
        for category, count in category_dist.items():
            percentage = (count/total_students) * 100
            insights.append(f"{category}: {count:,} students ({percentage:.1f}%)")
    
    # Performance insights
    if 'Percentile_obtained_in_entrance' in data.columns:
        avg_percentile = data['Percentile_obtained_in_entrance'].mean()
        max_percentile = data['Percentile_obtained_in_entrance'].max()
        min_percentile = data['Percentile_obtained_in_entrance'].min()
        insights.append("\nEntrance Exam Performance:")
        insights.append(f"Average Percentile: {avg_percentile:.2f}")
        insights.append(f"Highest Percentile: {max_percentile:.2f}")
        insights.append(f"Lowest Percentile: {min_percentile:.2f}")
    
    # Board percentage insights
    if 'Board_Percentage' in data.columns:
        avg_board = data['Board_Percentage'].mean()
        max_board = data['Board_Percentage'].max()
        min_board = data['Board_Percentage'].min()
        insights.append("\nBoard Exam Performance:")
        insights.append(f"Average Percentage: {avg_board:.2f}%")
        insights.append(f"Highest Percentage: {max_board:.2f}%")
        insights.append(f"Lowest Percentage: {min_board:.2f}%")
    
    # Category-wise performance
    if all(col in data.columns for col in ['Category', 'Percentile_obtained_in_entrance']):
        insights.append("\nCategory-wise Average Entrance Percentile:")
        cat_perf = data.groupby('Category')['Percentile_obtained_in_entrance'].mean()
        for category, avg in cat_perf.items():
            insights.append(f"{category}: {avg:.2f}")
    
    return insights

@app.route('/analyze_scholarship_questionnaire', methods=['POST'])
def analyze_scholarship_questionnaire():
    """Analyze scholarship data with questionnaire responses."""
    try:
        if 'file' not in request.files:
            return render_template('index.html', error='Please upload the data file again')
            
        file = request.files['file']
        if file.filename == '':
            return render_template('index.html', error='No file selected')
            
        data = pd.read_csv(file, encoding='utf-8')

        # Generate fresh plots and insights for this request only
        plots = generate_scholarship_plots(data)
        insights = analyze_performance(data)
        swot = perform_scholarship_swot(data)

        # Add questionnaire data to SWOT analysis
        mentorship_programs = request.form['mentorship_programs'] == 'yes'
        career_guidance = request.form['career_guidance'] == 'yes'
        academic_support = request.form['academic_support'] == 'yes'
        graduation_rate = float(request.form['graduation_rate'])
        application_success_rate = float(request.form['application_success_rate'])
        funding_sustainability = int(request.form['funding_sustainability'])

        # Enhanced SWOT Analysis based on questionnaire
        if mentorship_programs:
            swot['strengths'].append("Active mentorship program for scholarship recipients")
        else:
            swot['opportunities'].append("Implement mentorship program for better student support")

        if career_guidance:
            swot['strengths'].append("Career guidance services available")
        else:
            swot['opportunities'].append("Introduce career development services")

        if academic_support:
            swot['strengths'].append("Academic support system in place")
        else:
            swot['weaknesses'].append("Lack of academic support services")

        if graduation_rate >= 85:
            swot['strengths'].append(f"High graduation rate ({graduation_rate:.1f}%)")
        elif graduation_rate < 70:
            swot['weaknesses'].append(f"Low graduation rate ({graduation_rate:.1f}%)")

        if application_success_rate >= 75:
            swot['strengths'].append(f"High application success rate ({application_success_rate:.1f}%)")
        elif application_success_rate < 50:
            swot['weaknesses'].append(f"Low application success rate ({application_success_rate:.1f}%)")

        if funding_sustainability >= 5:
            swot['strengths'].append(f"Secure funding for {funding_sustainability} years")
        else:
            swot['threats'].append("Limited long-term funding security")

        return render_template('index.html',
            plots=plots,
            insights=insights,
            swot=swot,
            show_scholarship_results=True,
            hide_questionnaire=True
        )

    except Exception as e:
        return render_template('index.html', error=f'An error occurred: {str(e)}')

@app.route('/analyze_diversity_questionnaire', methods=['POST'])
def analyze_diversity_questionnaire():
    """Analyze diversity data with questionnaire responses."""
    try:
        # Get file data
        if 'file' not in request.files:
            return render_template('index.html', error='Please upload the data file again')
            
        file = request.files['file']
        if file.filename == '':
            return render_template('index.html', error='No file selected')
            
        data = pd.read_csv(file, encoding='utf-8')

        # Get form data
        students_with_disabilities = int(request.form['students_with_disabilities'])
        first_gen_students = int(request.form['first_gen_students'])
        international_students = int(request.form['international_students'])
        
        # Academic Environment
        student_faculty_ratio = float(request.form['student_faculty_ratio'])
        avg_class_size = float(request.form['avg_class_size'])
        research_active_faculty = float(request.form['research_active_faculty'])
        
        # Student Success Metrics
        avg_age = float(request.form['avg_age'])
        retention_rate = float(request.form['retention_rate'])
        graduation_rate = float(request.form['graduation_rate'])
        
        # Support Services
        counseling_services = request.form['counseling_services'] == 'yes'
        career_services = request.form['career_services'] == 'yes'
        tutoring_services = request.form['tutoring_services'] == 'yes'
        
        # Campus Life
        housing_capacity = float(request.form['housing_capacity'])
        student_organizations = int(request.form['student_organizations'])
        athletic_programs = int(request.form['athletic_programs'])

        # Calculate total students from the data
        total_students = data['Number_of_Students'].sum() if 'Number_of_Students' in data.columns else 0
        if total_students == 0:
            # If we can't get total students from file, estimate from the form data
            total_students = max(
                students_with_disabilities + first_gen_students + international_students,
                int(avg_class_size * student_faculty_ratio)  # Estimate from class size and ratio
            )

        # Generate fresh plots for this request only
        plots = generate_diversity_plots(data)
        
        # Generate fresh SWOT analysis
        swot = {
            'strengths': [],
            'weaknesses': [],
            'opportunities': [],
            'threats': []
        }

        # Enhanced SWOT Analysis
        if counseling_services and career_services and tutoring_services:
            swot['strengths'].append("Comprehensive student support services")
        else:
            swot['weaknesses'].append("Gaps in student support services")

        if housing_capacity >= 60:
            swot['strengths'].append(f"Strong residential community ({housing_capacity:.1f}% capacity)")
        elif housing_capacity < 30:
            swot['weaknesses'].append("Limited residential facilities")

        if student_organizations > 50:
            swot['strengths'].append(f"Vibrant campus life with {student_organizations} organizations")
        else:
            swot['opportunities'].append("Room for more student organizations")

        if research_active_faculty >= 70:
            swot['strengths'].append(f"Strong research faculty ({research_active_faculty:.1f}%)")
        elif research_active_faculty < 40:
            swot['weaknesses'].append("Limited research activity")

        if avg_class_size <= 25:
            swot['strengths'].append(f"Small class sizes (avg: {avg_class_size:.1f})")
        elif avg_class_size > 40:
            swot['weaknesses'].append("Large class sizes")

        # Calculate and analyze percentages
        disability_percentage = (students_with_disabilities / total_students * 100)
        first_gen_percentage = (first_gen_students / total_students * 100)
        international_percentage = (international_students / total_students * 100)

        if disability_percentage >= 5:
            swot['strengths'].append(f"Good support for students with disabilities ({disability_percentage:.1f}%)")
        else:
            swot['opportunities'].append("Enhance accessibility and support services")

        if first_gen_percentage >= 30:
            swot['strengths'].append(f"Strong first-generation student representation ({first_gen_percentage:.1f}%)")
        else:
            swot['opportunities'].append("Expand first-generation student outreach")

        if international_percentage >= 10:
            swot['strengths'].append(f"Good international diversity ({international_percentage:.1f}%)")
        else:
            swot['opportunities'].append("Increase international student recruitment")

        return render_template('index.html',
            plots=plots,
            swot=swot,
            show_results=True
        )

    except Exception as e:
        return render_template('index.html', error=f'An error occurred: {str(e)}')

# Add new route for combined analysis
@app.route('/analyze_combined', methods=['POST'])
def analyze_combined():
    """Handle combined analysis of scholarship and diversity data."""
    try:
        # Initialize empty plots and SWOT dictionaries
        scholarship_plots = {}
        diversity_plots = {}
        scholarship_swot = {'strengths': [], 'weaknesses': [], 'opportunities': [], 'threats': []}
        diversity_swot = {'strengths': [], 'weaknesses': [], 'opportunities': [], 'threats': []}
        combined_insights = {'scholarship': {}, 'diversity': []}
        total_students = 0
        
        # Check if this is a PDF download request
        is_pdf_request = request.args.get('format') == 'pdf'
        
        # Get scholarship file data
        if not is_pdf_request:
            # Normal form submission
            scholarship_file = request.files['scholarship_file']
            diversity_file = request.files['diversity_file']
            
            if not scholarship_file or not diversity_file:
                return render_template('index.html', error='Please upload both files')

            try:
                # Read and validate both files
                scholarship_data = pd.read_csv(scholarship_file, encoding='utf-8')
                diversity_data = pd.read_csv(diversity_file, encoding='utf-8')
                
                validate_data(scholarship_data)  # Reuse existing validation
                
                # Generate plots and analyses
                scholarship_plots = generate_scholarship_plots(scholarship_data)
                diversity_plots = generate_diversity_plots(diversity_data)
                scholarship_swot = perform_scholarship_swot(scholarship_data)
                diversity_swot = perform_swot_analysis(diversity_data)
                combined_insights = {
                    'scholarship': analyze_performance(scholarship_data),
                    'diversity': generate_diversity_insights(diversity_data)
                }
                
                # Calculate total students from diversity data
                total_students = len(diversity_data)

                # Store data in session
                session['total_students'] = total_students
                session['scholarship_swot'] = scholarship_swot
                session['diversity_swot'] = diversity_swot

            except Exception as e:
                return render_template('index.html', error=f'Error processing files: {str(e)}')

        # Get form data for both questionnaires
        scholarship_metrics = {
            'graduation_rate': float(request.form['scholarship_graduation_rate']),
            'application_success_rate': float(request.form['application_success_rate']),
            'funding_sustainability': int(request.form['funding_sustainability']),
            'mentorship_programs': request.form.get('mentorship_programs'),
            'career_guidance': request.form.get('career_guidance'),
            'academic_support': request.form.get('academic_support')
        }
        
        diversity_metrics = {
            'total_students': total_students,
            'students_with_disabilities': int(request.form['students_with_disabilities']),
            'first_gen_students': int(request.form['first_gen_students']),
            'international_students': int(request.form['international_students']),
            'student_faculty_ratio': float(request.form['student_faculty_ratio']),
            'research_active_faculty': float(request.form['research_active_faculty'])
        }

        scholarship_score, scholarship_explanations = calculate_scholarship_score(
            scholarship_data, scholarship_metrics)
        diversity_score, diversity_explanations = calculate_diversity_score(
            diversity_data, diversity_metrics)
        
        # Calculate overall score (weighted average)
        overall_score = (scholarship_score + diversity_score) / 2

        return render_template('index.html',
            show_combined_results=True,
            scholarship_plots=scholarship_plots,
            diversity_plots=diversity_plots,
            scholarship_swot=scholarship_swot,
            diversity_swot=diversity_swot,
            combined_insights=combined_insights,
            hide_questionnaire=True,
            total_students=total_students,
            graduation_rate=scholarship_metrics['graduation_rate'],
            application_success_rate=scholarship_metrics['application_success_rate'],
            international_students=diversity_metrics['international_students'],
            student_faculty_ratio=diversity_metrics['student_faculty_ratio'],
            research_active_faculty=diversity_metrics['research_active_faculty'],
            first_gen_students=diversity_metrics['first_gen_students'],
            students_with_disabilities=diversity_metrics['students_with_disabilities'],
            # Add score data
            scholarship_score=scholarship_score,
            scholarship_explanations=scholarship_explanations,
            diversity_score=diversity_score,
            diversity_explanations=diversity_explanations,
            overall_score=overall_score
        )

    except Exception as e:
        return render_template('index.html', error=f'An error occurred: {str(e)}')

def generate_pdf_report(data):
    """Generate and return a PDF report."""
    try:
        # Get the rendered HTML content
        html_content = render_template('pdf_report.html', **data)

        # Create a temporary file to store the PDF
        with tempfile.NamedTemporaryFile(suffix='.pdf', delete=False) as tmp:
            # Generate PDF from HTML
            HTML(string=html_content).write_pdf(tmp.name)
            
            # Send the PDF file
            return send_file(
                tmp.name,
                mimetype='application/pdf',
                as_attachment=True,
                download_name='educational_data_analysis_report.pdf'
            )

    except Exception as e:
        return render_template('index.html', error=f'Error generating PDF: {str(e)}')

@app.route('/download_report')
def download_report():
    """Generate and download PDF report."""
    try:
        # Get the rendered HTML content
        html_content = render_template('pdf_report.html',
            timestamp=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            total_students=session.get('total_students', 0),
            graduation_rate=session.get('graduation_rate', 0),
            application_success_rate=session.get('application_success_rate', 0),
            international_students=session.get('international_students', 0),
            student_faculty_ratio=session.get('student_faculty_ratio', 0),
            research_active_faculty=session.get('research_active_faculty', 0),
            first_gen_students=session.get('first_gen_students', 0),
            students_with_disabilities=session.get('students_with_disabilities', 0),
            scholarship_swot=session.get('scholarship_swot', {'strengths': [], 'weaknesses': [], 'opportunities': [], 'threats': []}),
            diversity_swot=session.get('diversity_swot', {'strengths': [], 'weaknesses': [], 'opportunities': [], 'threats': []})
        )

        # Create a temporary file to store the PDF
        with tempfile.NamedTemporaryFile(suffix='.pdf', delete=False) as tmp:
            # Generate PDF from HTML
            HTML(string=html_content).write_pdf(tmp.name)
            
            # Send the PDF file
            return send_file(
                tmp.name,
                mimetype='application/pdf',
                as_attachment=True,
                download_name='educational_data_analysis_report.pdf'
            )

    except Exception as e:
        return render_template('index.html', error=f'Error generating PDF: {str(e)}')

def calculate_scholarship_score(data: pd.DataFrame, metrics: Dict) -> Tuple[int, List[str]]:
    """
    Calculate scholarship score out of 100 based on various metrics.
    Returns tuple of (score, explanations)
    """
    score = 0
    explanations = []
    
    # Coverage and Utilization (30 points)
    coverage_rate = (data['Number of Beneficiaries'].sum() / data['Total Students Eligible'].sum()) * 100
    if coverage_rate >= 75:
        score += 30
        explanations.append("Excellent coverage rate (30/30)")
    elif coverage_rate >= 50:
        score += 20
        explanations.append("Good coverage rate (20/30)")
    elif coverage_rate >= 25:
        score += 10
        explanations.append("Fair coverage rate (10/30)")
    else:
        explanations.append("Poor coverage rate (0/30)")

    # Program Success (25 points)
    grad_rate = float(metrics.get('graduation_rate', 0))
    app_success = float(metrics.get('application_success_rate', 0))
    
    # Graduation rate (15 points)
    if grad_rate >= 90:
        score += 15
        explanations.append("Outstanding graduation rate (15/15)")
    elif grad_rate >= 80:
        score += 10
        explanations.append("Good graduation rate (10/15)")
    elif grad_rate >= 70:
        score += 5
        explanations.append("Fair graduation rate (5/15)")
    else:
        explanations.append("Needs improvement in graduation rate (0/15)")
    
    # Application success (10 points)
    if app_success >= 80:
        score += 10
        explanations.append("High application success rate (10/10)")
    elif app_success >= 60:
        score += 5
        explanations.append("Moderate application success rate (5/10)")
    else:
        explanations.append("Low application success rate (0/10)")

    # Support Services (25 points)
    support_score = 0
    if metrics.get('mentorship_programs') == 'yes':
        support_score += 8
    if metrics.get('career_guidance') == 'yes':
        support_score += 8
    if metrics.get('academic_support') == 'yes':
        support_score += 9
    score += support_score
    explanations.append(f"Support services score ({support_score}/25)")

    # Sustainability (20 points)
    funding_years = int(metrics.get('funding_sustainability', 0))
    if funding_years >= 5:
        score += 20
        explanations.append("Strong funding sustainability (20/20)")
    elif funding_years >= 3:
        score += 15
        explanations.append("Good funding sustainability (15/20)")
    elif funding_years >= 1:
        score += 10
        explanations.append("Limited funding sustainability (10/20)")
    else:
        explanations.append("Funding sustainability concerns (0/20)")

    return score, explanations

def calculate_diversity_score(data: pd.DataFrame, metrics: Dict) -> Tuple[int, List[str]]:
    """
    Calculate diversity score out of 100 based on various metrics.
    Returns tuple of (score, explanations)
    """
    score = 0
    explanations = []
    
    total_students = float(metrics.get('total_students', 0))
    if total_students == 0:
        total_students = len(data)

    # Gender Diversity (25 points)
    if 'Gender' in data.columns:
        gender_dist = data['Gender'].value_counts(normalize=True)
        min_gender_ratio = gender_dist.min()
        if min_gender_ratio >= 0.4:
            score += 25
            explanations.append("Excellent gender balance (25/25)")
        elif min_gender_ratio >= 0.3:
            score += 20
            explanations.append("Good gender balance (20/25)")
        elif min_gender_ratio >= 0.2:
            score += 15
            explanations.append("Fair gender balance (15/25)")
        else:
            explanations.append("Gender balance needs improvement (0/25)")

    # Inclusive Access (25 points)
    disabilities_pct = (float(metrics.get('students_with_disabilities', 0)) / total_students) * 100
    if disabilities_pct >= 5:
        score += 15
        explanations.append("Strong accessibility support (15/15)")
    elif disabilities_pct >= 3:
        score += 10
        explanations.append("Good accessibility support (10/15)")
    else:
        explanations.append("Accessibility support needs improvement (0/15)")

    first_gen_pct = (float(metrics.get('first_gen_students', 0)) / total_students) * 100
    if first_gen_pct >= 30:
        score += 10
        explanations.append("Strong first-generation representation (10/10)")
    elif first_gen_pct >= 20:
        score += 5
        explanations.append("Moderate first-generation representation (5/10)")
    else:
        explanations.append("First-generation representation needs improvement (0/10)")

    # Academic Environment (25 points)
    student_faculty = float(metrics.get('student_faculty_ratio', 0))
    if student_faculty > 0 and student_faculty <= 15:
        score += 15
        explanations.append("Excellent student-faculty ratio (15/15)")
    elif student_faculty <= 20:
        score += 10
        explanations.append("Good student-faculty ratio (10/15)")
    elif student_faculty <= 25:
        score += 5
        explanations.append("Fair student-faculty ratio (5/15)")
    else:
        explanations.append("Student-faculty ratio needs improvement (0/15)")

    research_faculty = float(metrics.get('research_active_faculty', 0))
    if research_faculty >= 70:
        score += 10
        explanations.append("Strong research activity (10/10)")
    elif research_faculty >= 50:
        score += 5
        explanations.append("Moderate research activity (5/10)")
    else:
        explanations.append("Research activity needs improvement (0/10)")

    # International Diversity (25 points)
    intl_pct = (float(metrics.get('international_students', 0)) / total_students) * 100
    if intl_pct >= 15:
        score += 25
        explanations.append("Excellent international diversity (25/25)")
    elif intl_pct >= 10:
        score += 20
        explanations.append("Good international diversity (20/25)")
    elif intl_pct >= 5:
        score += 15
        explanations.append("Fair international diversity (15/25)")
    else:
        explanations.append("International diversity needs improvement (0/25)")

    return score, explanations

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860)
    #create a new tab as questineier for scholorship and for diversity(diversity) in which we can do swot analysis