pdf_module.py

from fpdf import FPDF
from datetime import datetime
import tempfile
import os

class EcoVerifyReport(FPDF):
    def header(self):
        self.set_fill_color(45, 122, 79)
        self.rect(0, 0, 210, 28, 'F')
        self.set_font("Helvetica", "B", 16)
        self.set_text_color(255, 255, 255)
        self.set_xy(0, 8)
        self.cell(0, 10, "EcoVerify AI - Official Site Report", align="C")
        self.set_font("Helvetica", "", 9)
        self.set_xy(0, 18)
        self.cell(0, 6, "AI-powered Waste Management Verification for ASEAN Ecotourism", align="C")
        self.set_text_color(0, 0, 0)
        self.ln(18)

    def footer(self):
        self.set_y(-15)
        self.set_font("Helvetica", "I", 8)
        self.set_text_color(128, 128, 128)
        self.cell(0, 10, f"Generated by EcoVerify AI | {datetime.now().strftime('%d %b %Y %H:%M')} | Page {self.page_no()}", align="C")

    def section_title(self, title):
        self.set_fill_color(240, 250, 244)
        self.set_draw_color(45, 122, 79)
        self.set_font("Helvetica", "B", 11)
        self.set_text_color(30, 80, 50)
        self.cell(0, 9, f"  {title}", ln=True, fill=True, border="LB")
        self.set_text_color(0, 0, 0)
        self.ln(2)

    def key_value(self, key, value, color=None):
        self.set_font("Helvetica", "B", 10)
        self.cell(70, 7, f"  {key}:", ln=False)
        self.set_font("Helvetica", "", 10)
        if color:
            self.set_text_color(*color)
        self.cell(0, 7, str(value), ln=True)
        self.set_text_color(0, 0, 0)

    def body_text(self, text):
        self.set_font("Helvetica", "", 10)
        self.multi_cell(0, 6, f"  {text}")
        self.ln(1)

    def risk_bar(self, label, score):
        self.set_font("Helvetica", "", 9)
        self.cell(55, 6, f"  {label}", ln=False)
        # Bar background
        bar_x = self.get_x()
        bar_y = self.get_y() + 1
        self.set_fill_color(220, 220, 220)
        self.rect(bar_x, bar_y, 80, 4, 'F')
        # Bar fill
        filled = score * 80
        if score > 0.6:
            self.set_fill_color(231, 76, 60)
        elif score > 0.3:
            self.set_fill_color(243, 156, 18)
        else:
            self.set_fill_color(46, 204, 113)
        self.rect(bar_x, bar_y, filled, 4, 'F')
        self.set_x(bar_x + 85)
        self.set_font("Helvetica", "B", 9)
        self.cell(20, 6, f"{score:.2f}", ln=True)


def generate_pdf(
    site_name,
    site_type,
    site_size,
    visitor_count,
    nlp_risk,
    img_risk,
    combined_risk,
    nlp_output,
    img_output,
    rec_output,
    impact_output,
    warn_output,
    language="English"
) -> str:

    pdf = EcoVerifyReport()
    pdf.set_auto_page_break(auto=True, margin=20)
    pdf.add_page()

    # ── SITE INFO ─────────────────────────────────────────
    pdf.section_title("Site Information")
    pdf.key_value("Site Name", site_name)
    pdf.key_value("Site Type", site_type)
    pdf.key_value("Site Size", site_size)
    pdf.key_value("Avg Visitors/day", str(int(visitor_count)))
    pdf.key_value("Report Date", datetime.now().strftime("%d %B %Y"))
    pdf.ln(4)

    # ── RISK SUMMARY ──────────────────────────────────────
    pdf.section_title("Risk Summary")

    if combined_risk > 0.6:
        verdict = "HIGH RISK - Greenwashing Suspected"
        v_color = (231, 76, 60)
    elif combined_risk > 0.3:
        verdict = "MEDIUM RISK - Partial Inconsistency"
        v_color = (243, 156, 18)
    else:
        verdict = "LOW RISK - Claims Appear Consistent"
        v_color = (46, 204, 113)

    pdf.key_value("Overall Verdict", verdict, color=v_color)
    pdf.ln(2)
    pdf.risk_bar("NLP Risk Score", nlp_risk)
    pdf.risk_bar("Image Risk Score", img_risk)
    pdf.risk_bar("Combined Risk Score", combined_risk)
    pdf.ln(4)

    # ── EARLY WARNING ─────────────────────────────────────
    pdf.section_title("Early Warning Analysis")
    clean_warn = _clean_text(warn_output)
    pdf.body_text(clean_warn[:800])
    pdf.ln(4)

    # ── RECOMMENDATION ────────────────────────────────────
    pdf.section_title("Waste Management Recommendation")
    clean_rec = _clean_text(rec_output)
    pdf.body_text(clean_rec[:1000])
    pdf.ln(4)

    # ── IMPACT ────────────────────────────────────────────
    pdf.section_title("Environmental & Economic Impact")
    waste_total = visitor_count * 0.5
    unmanaged   = waste_total * combined_risk
    water_bod   = round(unmanaged * 0.15, 2)
    co2         = round(unmanaged * 2.5, 2) if combined_risk > 0.5 else 0.0
    bad_cost    = round(unmanaged * 1.5, 1)
    good_cost   = round(waste_total * 0.4, 1)
    daily_save  = round(max(bad_cost - good_cost, 0), 1)
    annual_save = int(daily_save * 365)

    pdf.key_value("Unmanaged Waste",     f"{unmanaged:.1f} kg/day")
    pdf.key_value("Water Pollution",     f"{water_bod} kg BOD/day")
    pdf.key_value("CO2 Emission",        f"{co2} kg/day")
    pdf.key_value("Current Cost",        f"${bad_cost}/day")
    pdf.key_value("Optimized Cost",      f"${good_cost}/day")
    pdf.key_value("Potential Savings",   f"${annual_save:,}/year", color=(46, 204, 113))
    pdf.ln(4)

    # ── CERTIFICATION READINESS ───────────────────────────
    pdf.section_title("ASEAN Certification Readiness")
    waste_ready   = round((1 - combined_risk) * 100)
    water_ready   = round((1 - combined_risk * 0.8) * 100)
    energy_ready  = round((1 - combined_risk * 0.6) * 100)
    overall_ready = round((waste_ready + water_ready + energy_ready) / 3)
    months_needed = max(1, round((100 - overall_ready) / 8))

    pdf.risk_bar("Waste Management",  1 - waste_ready/100)
    pdf.risk_bar("Wastewater",        1 - water_ready/100)
    pdf.risk_bar("Energy Efficiency", 1 - energy_ready/100)
    pdf.ln(2)
    pdf.key_value("Overall Readiness",    f"{overall_ready}%")
    pdf.key_value("Est. Time to Certify", f"{months_needed} months")
    pdf.ln(6)

    # ── FOOTER NOTE ───────────────────────────────────────
    pdf.set_font("Helvetica", "I", 8)
    pdf.set_text_color(128, 128, 128)
    pdf.multi_cell(0, 5,
        "This report is generated by EcoVerify AI for informational purposes. "
        "It supports post-certification monitoring and does not replace official inspection. "
        "Built for AI ASEAN Ready Competition."
    )

    # ── SAVE ──────────────────────────────────────────────
    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf",
                                      prefix=f"EcoVerify_{site_name.replace(' ','_')}_")
    pdf.output(tmp.name)
    return tmp.name


def _clean_text(text: str) -> str:
    """Strip markdown symbols cho PDF."""
    import re
    text = re.sub(r"#{1,6}\s?", "", text)
    text = re.sub(r"\*{1,3}(.*?)\*{1,3}", r"\1", text)
    text = re.sub(r"\|.*?\|", "", text)
    text = re.sub(r"-{3,}", "", text)
    text = re.sub(r"\n{3,}", "\n\n", text)
    return text.strip()