app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
from sklearn.ensemble import IsolationForest
import io
from fpdf import FPDF
import requests
import PyPDF2
import tempfile
import os

# -------------------------------
# Page Configuration and Header
# -------------------------------
st.set_page_config(page_title="🚀 WiFi Anomaly Detection", layout="wide")
st.title("🚀 WiFi Anomaly Detection System")
st.markdown("""
> "Innovation distinguishes between a leader and a follower." – *Steve Jobs*

> "The future depends on what you do today." – *Mahatma Gandhi*
""")
st.markdown("""
Welcome to the WiFi Anomaly Detection System. This application uses AI to proactively detect abnormal behavior in Public Wi-Fi systems, identifying suspicious spikes that may indicate hacking attempts. Let’s build a more secure network, one anomaly at a time!
""")

# -------------------------------
# Define Helper Functions
# -------------------------------
def load_data(uploaded_file):
    file_type = uploaded_file.name.split('.')[-1].lower()
    if file_type == 'csv':
        try:
            df = pd.read_csv(uploaded_file)
            return df, "csv"
        except Exception as e:
            st.error("Error reading CSV file.")
            return None, None
    elif file_type == 'txt':
        try:
            try:
                df = pd.read_csv(uploaded_file, sep=",")
            except:
                df = pd.read_csv(uploaded_file, sep="\s+")
            return df, "txt"
        except Exception as e:
            st.error("Error reading TXT file.")
            return None, None
    elif file_type == 'pdf':
        try:
            pdf_reader = PyPDF2.PdfReader(uploaded_file)
            text = ""
            for page in pdf_reader.pages:
                text += page.extract_text()
            df = pd.DataFrame({"text": [text]})
            return df, "pdf"
        except Exception as e:
            st.error("Error reading PDF file.")
            return None, None
    else:
        st.error("Unsupported file type.")
        return None, None

def run_local_anomaly_detection(df):
    # Use IsolationForest for numeric data anomaly detection.
    numeric_cols = df.select_dtypes(include=[np.number]).columns
    if len(numeric_cols) < 2:
        st.warning("Not enough numeric columns for anomaly detection. (Need at least 2 numeric columns)")
        return df
    X = df[numeric_cols].fillna(0)
    model = IsolationForest(contamination=0.1, random_state=42)
    model.fit(X)
    df['anomaly'] = model.predict(X)
    df['anomaly_flag'] = df['anomaly'].apply(lambda x: "🚨 Anomaly" if x == -1 else "✅ Normal")
    return df

def call_groq_api(df):
    # ----- Dummy Groq API integration -----
    # Replace this dummy call with an actual Groq API call as needed.
    df = run_local_anomaly_detection(df)
    return df

def generate_plots(df):
    # Generate 2D and 3D plots from the first numeric columns
    numeric_cols = df.select_dtypes(include=[np.number]).columns
    fig2d, fig3d = None, None
    if len(numeric_cols) >= 2:
        fig2d = px.scatter(df, x=numeric_cols[0], y=numeric_cols[1],
                           color='anomaly_flag',
                           title="📈 2D Anomaly Detection Plot")
    if len(numeric_cols) >= 3:
        fig3d = px.scatter_3d(df, x=numeric_cols[0], y=numeric_cols[1], z=numeric_cols[2],
                              color='anomaly_flag',
                              title="📊 3D Anomaly Detection Plot")
    return fig2d, fig3d

def generate_pdf_report(summary_text, fig2d, fig3d):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", 'B', 16)
    pdf.cell(0, 10, "WiFi Anomaly Detection Report", ln=True)
    pdf.ln(10)
    pdf.set_font("Arial", size=12)
    pdf.multi_cell(0, 10, summary_text)
    pdf.ln(10)
    
    # Save figures as temporary image files using Kaleido (Plotly's image export engine)
    image_files = []
    if fig2d is not None:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmpfile:
            fig2d.write_image(tmpfile.name)
            image_files.append(tmpfile.name)
    if fig3d is not None:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmpfile:
            fig3d.write_image(tmpfile.name)
            image_files.append(tmpfile.name)
    
    # Add each image to the PDF
    for image in image_files:
        pdf.image(image, w=pdf.w - 40)
        pdf.ln(10)
    
    # Clean up temporary image files
    for image in image_files:
        os.remove(image)
    
    pdf_data = pdf.output(dest="S").encode("latin1")
    return pdf_data

# -------------------------------
# Initialize Session State Variables
# -------------------------------
if "step" not in st.session_state:
    st.session_state.step = "upload"
if "df" not in st.session_state:
    st.session_state.df = None
if "df_processed" not in st.session_state:
    st.session_state.df_processed = None
if "fig2d" not in st.session_state:
    st.session_state.fig2d = None
if "fig3d" not in st.session_state:
    st.session_state.fig3d = None
if "summary_text" not in st.session_state:
    st.session_state.summary_text = ""

# -------------------------------
# Sidebar: Step Buttons
# -------------------------------
st.sidebar.title("🔧 Application Steps")
if st.sidebar.button("📁 Upload File"):
    st.session_state.step = "upload"
if st.sidebar.button("📊 Data Visualization"):
    st.session_state.step = "viz"
if st.sidebar.button("📈 Statistic Analysis"):
    st.session_state.step = "stats"
if st.sidebar.button("⬇️ Download Report"):
    st.session_state.step = "download"

# -------------------------------
# Main Workflow Based on Step
# -------------------------------
if st.session_state.step == "upload":
    st.subheader("Step 1: Upload Your Data File")
    st.markdown("Please upload a CSV, TXT, or PDF file with network data. The expected columns for CSV/TXT files are:")
    st.code("['traffic', 'latency', 'packet_loss']", language="python")
    uploaded_file = st.file_uploader("Choose a file", type=["csv", "txt", "pdf"])
    if uploaded_file is not None:
        df, file_type = load_data(uploaded_file)
        if df is not None:
            st.session_state.df = df
            st.success("File uploaded and processed successfully!")
            if file_type == "pdf":
                st.subheader("Extracted Text from PDF:")
                st.text_area("PDF Content", df["text"][0], height=300)
            else:
                st.subheader("Data Preview:")
                st.dataframe(df.head())
    else:
        st.info("Awaiting file upload. 😊")

elif st.session_state.step == "viz":
    st.subheader("Step 2: Data Visualization")
    if st.session_state.df is None:
        st.error("Please upload a file first in the 'Upload File' step.")
    else:
        # Process the data if not already done
        if st.session_state.df_processed is None:
            # Here, you can choose between the local model or Groq API; we use the local model for demo.
            st.session_state.df_processed = run_local_anomaly_detection(st.session_state.df)
        fig2d, fig3d = generate_plots(st.session_state.df_processed)
        st.session_state.fig2d = fig2d
        st.session_state.fig3d = fig3d
        if fig2d:
            st.plotly_chart(fig2d, use_container_width=True)
        if fig3d:
            st.plotly_chart(fig3d, use_container_width=True)

elif st.session_state.step == "stats":
    st.subheader("Step 3: Statistic Analysis")
    if st.session_state.df_processed is None:
        st.error("Data has not been processed yet. Please complete the Data Visualization step first.")
    else:
        df_result = st.session_state.df_processed
        anomaly_count = (df_result['anomaly'] == -1).sum()
        total_count = df_result.shape[0]
        st.session_state.summary_text = f"Total records: {total_count}\nDetected anomalies: {anomaly_count}"
        st.markdown("**Anomaly Detection Summary:**")
        st.text(st.session_state.summary_text)
        st.markdown("**Detailed Data:**")
        st.dataframe(df_result.head())
        st.markdown("**Descriptive Statistics:**")
        st.dataframe(df_result.describe())

elif st.session_state.step == "download":
    st.subheader("Step 4: Download PDF Report")
    if st.session_state.df_processed is None or (st.session_state.fig2d is None and st.session_state.fig3d is None):
        st.error("Please complete the previous steps (Upload, Visualization, Statistic Analysis) before downloading the report.")
    else:
        pdf_data = generate_pdf_report(st.session_state.summary_text, st.session_state.fig2d, st.session_state.fig3d)
        st.download_button("⬇️ Download PDF Report", data=pdf_data,
                           file_name="wifi_anomaly_report.pdf",
                           mime="application/pdf")