import matplotlib.pyplot as plt
from fpdf import FPDF
from datetime import datetime
from huggingfaceModel import generate_dropout_insights
#rom ollamaModel import generate_dropout_insights
import re
import os

def format_key(key):
    """Format keys to be more readable by capitalizing and removing underscores."""
    return key.replace("_", " ").title()

def remove_unsupported_characters(text):
    """Removes emojis and unsupported Unicode characters for PDF compatibility."""
    return text.encode("ascii", "ignore").decode()

def generate_pdf(input_data, dropout_risk, summary):
    """Generates a structured, professional PDF report for dropout prediction results."""
    
    pdf = FPDF()
    pdf.add_page()

    # 🔹 Check if DejaVuSans font files exist
    if os.path.exists("DejaVuSans.ttf") and os.path.exists("DejaVuSans-Bold.ttf") and os.path.exists("DejaVuSans-Oblique.ttf"):
        font_name = "DejaVu"
        pdf.add_font("DejaVu", "", "Dejavu/DejaVuSans.ttf", uni=True)
        pdf.add_font("DejaVu", "B", "Dejavu/DejaVuSans-Bold.ttf", uni=True)
        pdf.add_font("DejaVu", "I", "Dejavu/DejaVuSans-Oblique.ttf", uni=True)
    else:
        font_name = "Arial"  # Use default Arial if DejaVu fonts are missing
    
    # 🔹 Cover Page
    pdf.set_font(font_name, "B", 20)
    pdf.cell(200, 15, "AI-DRIVEN DROPOUT PREDICTION AND PREVENTION TOOL", ln=True, align="C")

    pdf.set_font(font_name, "I", 12)
    pdf.cell(200, 10, "Generated by: AI Dropout Predictor System", ln=True, align="C")
    pdf.cell(200, 10, f"Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", ln=True, align="C")

    pdf.ln(20)
    pdf.set_font(font_name, "B", 16)
    pdf.cell(200, 10, "Confidential Report", ln=True, align="C")

    pdf.ln(10)
    pdf.set_font(font_name, "I", 12)
    pdf.cell(200, 10, "This document contains confidential student information and predictive analysis.", ln=True, align="C")

    pdf.add_page()

    # 🔹 Student Information Section
    pdf.set_font(font_name, "B", 14)
    pdf.cell(200, 10, "Student Information", ln=True, align="L")
    pdf.ln(5)

    pdf.set_font(font_name, "", 12)
    for key, value in input_data.items():
        formatted_key = key.replace("_", " ").title()
        pdf.cell(90, 8, f"{formatted_key}:", border=1, align="L")
        pdf.cell(100, 8, str(value), border=1, align="L")
        pdf.ln(8)

    pdf.ln(10)
    pdf.add_page()

    # 🔹 Dropout Risk Score Section
    pdf.set_font(font_name, "B", 14)
    pdf.cell(200, 10, "Predicted Dropout Risk Score", ln=True, align="L")
    pdf.ln(5)

    pdf.set_font(font_name, "B", 16)
    pdf.set_fill_color(240, 240, 240)
    pdf.cell(200, 15, f" Dropout Risk Score: {dropout_risk}/10 ", ln=True, align="L", border=1, fill=True)

    pdf.ln(10)

    # 🔹 Dropout Prediction Graph
    pdf.set_font(font_name, "B", 14)
    pdf.cell(200, 10, "Dropout Risk Analysis Graph", ln=True, align="C")
    pdf.ln(5)

    if os.path.exists("dropout_plot.png"):
        pdf.image("dropout_plot.png", x=50, w=100)
    else:
        pdf.cell(200, 10, "Graph Unavailable", ln=True, align="C")

    pdf.ln(20)
    pdf.add_page()

    # 🔹 Summary Section
    pdf.set_font(font_name, "B", 14)
    pdf.cell(200, 10, "Summary of Student Data", ln=True, align="C")
    pdf.ln(5)

    pdf.set_font(font_name, "", 12)
    cleaned_summary = remove_unsupported_characters(summary)
    pdf.multi_cell(0, 7, cleaned_summary, align="L")

    # 🔹 Disclaimer
    pdf.set_y(-30)
    pdf.set_font(font_name, "I", 10)
    pdf.cell(200, 10, "Disclaimer:", ln=True, align="L")
    pdf.multi_cell(0, 7, "This report is generated using predictive analysis. It does not guarantee actual dropout behavior but serves as a guideline for early intervention.", align="L")

    # 🔹 Save PDF
    pdf_filename = "Dropout_Report.pdf"
    pdf.output(pdf_filename)

    return pdf_filename

def calculate_risk(value, thresholds, weights):
    """Assigns a risk weight based on threshold conditions."""
    for threshold, weight in zip(thresholds, weights):
        if value <= threshold:
            return weight
    return weights[-1]

def predict_dropout(**kwargs):
    """Predicts dropout risk based on multiple risk factors."""
    risk_factors = [
        calculate_risk(1 if kwargs.get("special_lab") == "Non-Active" else 0, [0, 1], [-0.1, 0.5]),
        calculate_risk(kwargs.get("attendance_percentage", 0), [50, 65, 75, 85], [1.0, 0.7, 0.2, -0.2]),
        calculate_risk(kwargs.get("formative_assessment", 0), [30, 50, 70, 85], [1.0, 0.7, 0.4, -0.2]),
        calculate_risk(kwargs.get("cgpa", 0), [5.0, 6.0, 7.0, 8.5], [1.0, 0.7, 0.4, -0.2]),
        calculate_risk(kwargs.get("current_sgpa", 0), [5.0, 6.0, 7.0, 8.5], [1.0, 0.7, 0.4, -0.2]),
        calculate_risk(kwargs.get("arrear_count", 0), [1, 3, 6, 10], [0.6, 0.8, 1.0, 1.3]),
        calculate_risk(kwargs.get("full_stack_rank", 0), [300, 800, 1200, 1600], [-0.3, -0.1, 0.3, 0.6]),
        calculate_risk(kwargs.get("ps_rank", 0), [1, 2, 4, 6], [0.8, 0.6, -0.1, -0.2]),
        calculate_risk(kwargs.get("Overall_Skills_Acquired", 0), [1, 4, 8, 12], [0.8, 0.5, -0.1, -0.2]),
        calculate_risk(kwargs.get("placement_fa", 0), [40, 60, 80, 95], [1.0, 0.7, -0.1, -0.2]),
        calculate_risk(kwargs.get("interim_assessment_status", 0), [50, 70, 85, 95], [1.0, 0.7, -0.1, -0.2]),
        calculate_risk(kwargs.get("training_assessment_status", 0), [50, 70, 85, 95], [1.0, 0.7, -0.1, -0.2]),
        calculate_risk(kwargs.get("mock_assessment_status", 0), [30, 50, 70, 85], [1.0, 0.7, 0.3, -0.2]),
        calculate_risk(kwargs.get("placement_cumulative", 0), [50, 70, 85, 95], [1.0, 0.7, -0.1, -0.2]),
        calculate_risk(kwargs.get("placement_Attendence", 0), [50, 70, 85, 95], [1.0, 0.7, -0.1, -0.2]),
        *(calculate_risk(kwargs.get(activity, 0), [0, 1, 3], [0.1, -0.2, -0.4]) for activity in [
            "Technical_Competition", "Paper_Presentation", "Project_Competition", 
            "Product_Development", "Patent", "Internship", "Online_Course"
        ])
    ]
    print(risk_factors)

    total_risk = min(10, max(0, sum(risk_factors)))
    return total_risk, risk_factors

def generate_plot(dropout_risk):
    """Creates a bar plot for dropout risk."""
    fig, ax = plt.subplots()
    colors = ['green', 'lightgreen', 'yellow', 'orange', 'red']
    labels = ['Very Low Risk', 'Low Risk', 'Moderate Risk', 'High Risk', 'Very High Risk']
    index = min(int(dropout_risk // 2), 4)

    ax.bar([labels[index]], [dropout_risk], color=colors[index])
    ax.set_ylim(0, 10)
    ax.set_ylabel('Dropout Risk Score')
    ax.set_title('Dropout Risk Prediction')

    plt.savefig("dropout_plot.png")  # Save before using in PDF
    return fig ,labels[index]

def remove_emojis(text):
    """Removes all emojis and unsupported Unicode characters from text."""
    emoji_pattern = re.compile("[\U00010000-\U0010ffff]", flags=re.UNICODE)
    return emoji_pattern.sub("", text)

def process_and_generate_report(**input_values):

    """Processes input values and generates risk prediction along with a report."""
    
    dropout_risk,risk_factor = predict_dropout(**input_values)
    fig, label = generate_plot(dropout_risk)
    insights = generate_dropout_insights(input_values,risk_factor,label)
    cleaned_insights = remove_emojis(insights)
    pdf_filename = generate_pdf(input_values, dropout_risk, cleaned_insights)
    
    return fig, insights, pdf_filename