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, hashlib, re, 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.file_utils import normalize_log_line
from omniscientframework.utils.summarizer import summarize_text
from omniscientframework.utils.docgen import generate_doc

# ─── Page Setup ─────────────────────────────────────────────────────
st.title("📂 AI Bulk Digest")
st.write("Upload multiple files for batch digestion, summarization, and documentation.")

# ─── Initialize Session State ───────────────────────────────────────
for key in ("uploaded_files", "errors", "bulk_digests"):
    if key not in st.session_state:
        st.session_state[key] = []

# ─── File Upload ────────────────────────────────────────────────────
uploads = st.file_uploader(
    "Upload files (scripts, logs, text, PDFs)",
    type=["py", "sh", "txt", "log", "pdf"],
    accept_multiple_files=True
)

# ─── Bulk Processing Logic ─────────────────────────────────────────
if uploads:
    digests = []
    for f in uploads:
        try:
            # Read content
            content = f.read().decode("utf-8", errors="ignore")
            sha1 = hashlib.sha1(content.encode()).hexdigest()

            # File type detection
            is_log = f.name.endswith(".log")
            is_script = f.name.endswith((".py", ".sh"))

            # Normalize log lines
            if is_log:
                normalized = [normalize_log_line(line) for line in content.splitlines()]
                preview = "\n".join(normalized[:30])
            else:
                preview = "\n".join(content.splitlines()[:30])

            # Generate AI summary
            summary = summarize_text(content)

            # Auto-generate documentation for scripts
            doc = generate_doc(f.name, "uploaded", content) if is_script else None

            # Build result object
            result = {
                "name": f.name,
                "sha1": sha1,
                "preview": preview,
                "summary": summary,
                "doc": doc,
            }
            digests.append(result)

            # Track file name
            st.session_state.uploaded_files.append(f.name)

            # ─── Display Results ───────────────────────────────
            st.subheader(f"📄 {f.name}")
            st.code(preview)
            st.markdown(f"**SHA1:** `{sha1}`")
            st.write("🧠 **Summary:**", summary)
            if doc:
                st.write("📘 **Generated Documentation:**")
                st.markdown(doc)

        except Exception as e:
            st.error(f"⚠️ Error processing {f.name}: {e}")
            st.session_state.errors.append(str(e))

    # Save to session state
    st.session_state.bulk_digests = digests
    st.success(f"✅ Bulk digestion complete for {len(digests)} files.")