import sys, os

BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
UTILS_DIR = os.path.join(BASE_DIR, "utils")

if UTILS_DIR not in sys.path:
    sys.path.insert(0, UTILS_DIR)

import streamlit as st
import os, sys

# ─── Ensure omniscientframework package is importable ────────────────
ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
PACKAGE_PATH = os.path.abspath(os.path.join(ROOT_PATH, ".."))
if PACKAGE_PATH not in sys.path:
    sys.path.insert(0, PACKAGE_PATH)

# ─── Import project utilities ────────────────────────────────────────
from omniscientframework.utils.backend import run_llm

# ─── Page Setup ─────────────────────────────────────────────────────
st.title("🧪 Example Page with Chatbot")
st.write("This demo chatbot also ingests Omnieye + Omnilog outputs.")

# ─── Initialize Session State ───────────────────────────────────────
if "example_chat" not in st.session_state:
    st.session_state.example_chat = []

# ─── Collect context from Omnieye + Omnilog ─────────────────────────
system_context = []
if "omnieye_output" in st.session_state:
    preview = st.session_state.omnieye_output.get("file_preview", "")
    matches = st.session_state.omnieye_output.get("matches", [])
    if preview:
        system_context.append(f"Omnieye preview:\n{preview}")
    if matches:
        system_context.append("Keyword matches:\n" + "\n".join(matches))

if "omnilog_output" in st.session_state:
    normalized = st.session_state.omnilog_output.get("normalized_preview", "")
    matches = st.session_state.omnilog_output.get("matches", [])
    if normalized:
        system_context.append(f"Omnilog preview:\n{normalized}")
    if matches:
        system_context.append("Log matches:\n" + "\n".join(matches))

# ─── Display Chat History ───────────────────────────────────────────
for msg in st.session_state.example_chat:
    with st.chat_message(msg["role"]):
        st.markdown(msg["content"])

# ─── Chat Input ─────────────────────────────────────────────────────
if prompt := st.chat_input("Ask the Example Chatbot about files or logs..."):
    st.session_state.example_chat.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)

    # Build context-aware input
    ai_input = "\n\n".join(system_context + [prompt])

    # Generate AI response
    try:
        ai_reply = run_llm(ai_input)
    except Exception as e:
        ai_reply = f"⚠️ Error running LLM: {e}"

    with st.chat_message("assistant"):
        st.markdown(ai_reply)
    st.session_state.example_chat.append({"role": "assistant", "content": ai_reply})