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from fpdf import FPDF
import pandas as pd, os
from utils.logger import log_path
class NIDSReportPDF(FPDF):
def header(self):
self.set_font("Helvetica", "B", 18)
self.cell(0, 10, "NIDS - Network Intrusion Detection Report", ln=True, align="C")
self.ln(5)
def footer(self):
self.set_y(-15)
self.set_font("Helvetica", "I", 9)
self.cell(0, 10, f"Page {self.page_no()}/{{nb}}", align="C")
def generate_pdf_bytes(n=300):
"""Generate PDF summary of recent events."""
df = pd.read_csv(log_path) if os.path.exists(log_path) else pd.DataFrame()
pdf = NIDSReportPDF()
pdf.alias_nb_pages()
pdf.add_page()
pdf.set_font("Helvetica", "", 12)
pdf.cell(0, 10, f"Last {n} Events Summary", ln=True)
pdf.ln(5)
if len(df) == 0:
pdf.cell(0, 10, "No data available.", ln=True)
else:
df = df.tail(n)
counts = df["prediction"].value_counts().to_dict() if "prediction" in df.columns else {}
pdf.cell(0, 10, "Prediction Distribution:", ln=True)
pdf.ln(4)
for label, count in counts.items():
pdf.cell(0, 10, f"{label}: {count}", ln=True)
pdf.ln(8)
pdf.cell(0, 10, "Sample Events:", ln=True)
pdf.ln(4)
# limit to 10 sample rows
cols = ["time", "src", "dst", "proto", "prediction"]
cols = [c for c in cols if c in df.columns]
for _, row in df.tail(10).iterrows():
line = " | ".join(str(row[c]) for c in cols)
if len(line) > 150:
line = line[:147] + "..."
pdf.multi_cell(0, 8, line)
# return as bytes
output = pdf.output(dest="S")
if isinstance(output, (bytes, bytearray)):
return bytes(output)
else:
return bytes(output.encode("latin1", "ignore"))
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