import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from django.shortcuts import render
from .forms import CSVUploadForm
import io
import base64

def get_plot_image():
    buffer = io.BytesIO()
    plt.savefig(buffer, format='png', bbox_inches='tight')
    buffer.seek(0)
    image_png = buffer.getvalue()
    buffer.close()
    return base64.b64encode(image_png).decode('utf-8')

def upload_csv(request):
    charts = []
    if request.method == "POST" and request.FILES.get("csv_file"):
        form = CSVUploadForm(request.POST, request.FILES)
        if form.is_valid():
            csv_file = request.FILES["csv_file"]
            df = pd.read_csv(csv_file)
            df.columns = df.columns.str.strip()  # Remove any spaces in headers

            # Validate required columns
            required_columns = ['Semester', 'Geschlecht', 'Fakultät', 'Wie hast du von diesem Kurs erfahren?']
            missing = [col for col in required_columns if col not in df.columns]
            if missing:
                print('test')
                return render(request, 'reports/upload.html', {
                    'form': form,
                    'error': f'Missing required columns: {", ".join(missing)}'
                })

            sns.set_theme(style="whitegrid")

            # 1. Semester Distribution
            semester_counts = df['Semester'].value_counts().sort_index()
            plt.figure(figsize=(8, 4))
            sns.lineplot(x=semester_counts.index, y=semester_counts.values, marker="o")
            plt.title("Semester Distribution")
            plt.xlabel("Semester")
            plt.ylabel("Number of Students")
            charts.append(get_plot_image())
            plt.close()

            # 2. Gender Distribution
            gender_counts = df['Geschlecht'].value_counts()
            plt.figure(figsize=(6, 4))
            ax = sns.barplot(x=gender_counts.index, y=gender_counts.values, palette="pastel", hue=gender_counts.index, legend=False)
            for i, v in enumerate(gender_counts.values):
                ax.text(i, v + 0.2, str(v), color='black', ha='center', fontweight='bold')
            plt.title("Gender Distribution")
            plt.xlabel("Gender")
            plt.ylabel("Number of Students")
            charts.append(get_plot_image())
            plt.close()

            # 3. Faculty Distribution
            df['Fakultät'] = df['Fakultät'].replace({
                'B (Biotechnologie)': 'B',
                'N (Informationstechnik)': 'N',
                'M (Maschinenbau)': 'M',
                'I (Informatik)': 'I',
                'E (Elektrotechnik)': 'E',
            })
            faculty_counts = df['Fakultät'].value_counts()
            plt.figure(figsize=(6, 4))
            ax = sns.barplot(x=faculty_counts.index, y=faculty_counts.values, palette="muted", hue=faculty_counts.index, legend=False)
            for i, v in enumerate(faculty_counts.values):
                ax.text(i, v + 0.2, str(v), color='black', ha='center', fontweight='bold')
            plt.title("Faculty Distribution")
            plt.xlabel("Faculty")
            plt.ylabel("Number of Students")
            charts.append(get_plot_image())
            plt.close()
            print('test2')

            # 4. Course Discovery (Pie Chart)
            course_source_counts = df['Wie hast du von diesem Kurs erfahren?'].value_counts()
            plt.figure(figsize=(6, 6))
            plt.pie(course_source_counts, labels=course_source_counts.index, autopct='%1.1f%%', startangle=140)
            plt.title("How Did Students Hear About the Course?")
            charts.append(get_plot_image())
            plt.close()

            return render(request, 'reports/upload.html', {
                'form': form,
                'charts': charts
            })

    else:
        form = CSVUploadForm()

    return render(request, 'reports/upload.html', {'form': form})