import streamlit as st
from parse_logs import parse_log_file
from detect import load_model, detect_anomaly
import matplotlib.pyplot as plt

st.set_page_config(page_title="LogShield - AI-Powered Log Anomaly Detector")

st.title("🔒 LogShield - AI-Powered Log Anomaly Detector")

uploaded_file = st.file_uploader("📁 Upload your log file (.txt):", type="txt")

if uploaded_file:
    st.success("✅ File Uploaded Successfully!")
    
    logs = parse_log_file(uploaded_file.read())
    st.write(f"Total Log Lines: {len(logs)}")

    with st.spinner("Analyzing Logs..."):
        model = load_model()
        results = detect_anomaly(model, logs)

    # Display results
    anomaly_count = results.count("Anomaly")
    normal_count = results.count("Normal")

    st.subheader("🚨 Anomaly Detection Results:")
    for log, result in zip(logs, results):
        color = "red" if result == "Anomaly" else "green"
        st.markdown(f"<span style='color:{color}'><strong>{result}</strong>: {log}</span>", unsafe_allow_html=True)

    # Display Summary
    st.subheader("🔍 Summary Chart")
    fig, ax = plt.subplots()
    ax.bar(["Normal", "Anomaly"], [normal_count, anomaly_count], color=["green", "red"])
    ax.set_ylabel("Count")
    ax.set_title("Log Summary")
    st.pyplot(fig)