"""
routes/ai_summary.py — AI document analysis routes.
"""

import logging
import json
import io
import os
import re
import requests as http_requests
from flask import (Blueprint, render_template, request, redirect, url_for,
                   flash, session, jsonify)
from models import (
    get_all_criteria, get_active_criteria, create_criterion, update_criterion,
    delete_criterion, save_ai_analysis, get_ai_analysis_history,
    get_ai_analysis_detail, delete_ai_analysis, log_action,
)
from config import get_setting
from utils.decorators import login_required, admin_required

logger = logging.getLogger("routes.ai_summary")
ai_summary_bp = Blueprint("ai_summary", __name__, url_prefix="/ai-summary")

GROQ_MODELS = [
    "llama-3.3-70b-versatile",
    "llama-3.1-8b-instant",
    "gemma2-9b-it",
]

SUPPORTED_EXT = {".txt", ".md", ".csv", ".pdf", ".doc", ".docx", ".xlsx", ".xls"}


@ai_summary_bp.route("/")
@login_required
def ai_summary():
    user       = session["user"]
    criteria   = get_all_criteria()
    history    = get_ai_analysis_history(user_id=None)
    groq_key   = get_setting("groq.api_key", "")
    groq_model = get_setting("groq.model", GROQ_MODELS[0])
    return render_template("ai_summary.html",
                           criteria=criteria, history=history,
                           groq_key_set=bool(groq_key),
                           groq_model=groq_model,
                           groq_models=GROQ_MODELS,
                           is_admin=user["role"] == "admin")


@ai_summary_bp.route("/analyze", methods=["POST"])
@login_required
def analyze():
    user    = session["user"]
    files   = request.files.getlist("documents[]")
    model   = request.form.get("model", get_setting("groq.model", GROQ_MODELS[0]))
    # Read API key: env var takes priority, then DB setting
    api_key = (os.environ.get("GROQ_API_KEY") or "").strip() or get_setting("groq.api_key", "").strip()

    if not api_key:
        return jsonify({"error": "Groq API key is not configured. Set GROQ_API_KEY in .env or via Admin → Settings."}), 400
    if not files or all(f.filename == "" for f in files):
        return jsonify({"error": "No files uploaded."}), 400

    texts      = []
    file_names = []
    errors     = []

    for f in files:
        if not f.filename:
            continue
        ext = ("." + f.filename.rsplit(".", 1)[-1].lower()) if "." in f.filename else ""
        if ext not in SUPPORTED_EXT:
            errors.append(f"{f.filename}: unsupported file type")
            continue
        file_names.append(f.filename)
        try:
            content = _extract_text(f, ext)
            if content and content.strip():
                texts.append(f"=== {f.filename} ===\n{content.strip()}")
            else:
                errors.append(f"{f.filename}: no text could be extracted")
        except Exception as e:
            logger.warning(f"Could not extract text from {f.filename}: {e}")
            errors.append(f"{f.filename}: {e}")

    if not texts:
        detail = "; ".join(errors) if errors else "Check the file format and try again."
        return jsonify({"error": f"Could not extract text from any uploaded file. {detail}"}), 400

    criteria     = get_active_criteria()
    combined     = "\n\n".join(texts)

    try:
        result = _call_groq(api_key, model, combined, criteria)
    except Exception as e:
        logger.error(f"Groq API error: {e}")
        return jsonify({"error": f"AI analysis failed: {e}"}), 500

    criteria_snap = json.dumps([
        {"title": c["title"], "description": c["description"]} for c in criteria
    ])
    analysis_id = save_ai_analysis(
        user["id"], ", ".join(file_names), model,
        result.get("verdict"), criteria_snap, result.get("summary", "")
    )
    log_action(user["id"], "AI_ANALYSIS", "ai_analysis_log", analysis_id,
               f"AI analysis on {len(file_names)} file(s). Verdict: {result.get('verdict')}.")

    return jsonify({"analysis_id": analysis_id, "model": model,
                   "file_count": len(file_names), "file_errors": errors, **result})


# Magic-byte signatures for each supported binary format.
# Office Open XML (docx/xlsx) and legacy OLE (doc/xls) share these headers.
_FILE_MAGIC: dict = {
    ".pdf":  b"%PDF",
    ".docx": b"PK\x03\x04",
    ".xlsx": b"PK\x03\x04",
    ".doc":  b"\xd0\xcf\x11\xe0\xa1\xb1\x1a\xe1",
    ".xls":  b"\xd0\xcf\x11\xe0\xa1\xb1\x1a\xe1",
}


def _validate_magic(data: bytes, ext: str) -> None:
    """Raise ValueError if the file's actual bytes don't match its extension."""
    magic = _FILE_MAGIC.get(ext)
    if magic:
        if data[: len(magic)] != magic:
            raise ValueError(
                f"File content does not match the declared {ext} format "
                "(possible disguised upload)."
            )
    elif ext in (".txt", ".csv", ".md"):
        # Text files must be decodable; binary data masquerading as text is rejected.
        try:
            data[:512].decode("utf-8")
        except UnicodeDecodeError:
            raise ValueError(
                "Text file does not appear to be valid UTF-8. "
                "Ensure the file is a plain text document."
            )


def _extract_text(file_obj, ext: str) -> str:
    """Extract plain text from an uploaded file object."""
    data = file_obj.read()
    _validate_magic(data, ext)

    if ext in (".txt", ".md", ".csv"):
        return data.decode("utf-8", errors="replace")

    if ext == ".pdf":
        import pypdf
        reader = pypdf.PdfReader(io.BytesIO(data))
        pages  = []
        for page in reader.pages:
            t = page.extract_text()
            if t:
                pages.append(t)
        return "\n".join(pages)

    if ext in (".docx", ".doc"):
        # Use python-docx (installed as 'docx') — do NOT use docx2txt
        from docx import Document
        doc   = Document(io.BytesIO(data))
        lines = [para.text for para in doc.paragraphs if para.text.strip()]
        return "\n".join(lines)

    if ext in (".xlsx", ".xls"):
        import openpyxl
        wb    = openpyxl.load_workbook(io.BytesIO(data), read_only=True, data_only=True)
        lines = []
        for ws in wb.worksheets:
            for row in ws.iter_rows(values_only=True):
                line = "\t".join(str(c) if c is not None else "" for c in row)
                if line.strip():
                    lines.append(line)
        return "\n".join(lines)

    return ""


# Office address used as origin for distance/travel-time estimates.
_OFFICE_ADDRESS = "2815 Hartland Road, Falls Church, VA 22043, USA"

# Text character limit sent to the API. llama-3.3-70b supports 128K tokens (~500K chars);
# 60K chars is a safe ceiling that leaves room for the prompt and response.
_TEXT_LIMIT = 60_000

# Analyst persona injected as the system message in every API call.
_SYSTEM_PROMPT = (
    "You are a senior government procurement analyst supporting a small business BD team. "
    "Your role is to extract, organize, and evaluate information from solicitation documents "
    "with precision and clarity. Format all output in well-structured Markdown using tables "
    "and headers. Never invent or assume information not present in the source documents — "
    "write \"Not specified\" for any missing field."
)

# Stage 1 — extraction prompt (always sent)
_EXTRACTION_PROMPT = """\
Analyze the {n} attached government solicitation document(s) as one combined package.
Complete every section below exactly as structured. Write **"Not specified"** for any field
not found in the documents. Do not guess or invent details.

Our office is located at: **{office}** — use this as the origin for all travel estimates.

---

## Solicitation Overview

| Field | Value |
|---|---|
| **Solicitation Number** | |
| **Solicitation Type** | (RFP / RFQ / IFB / IDIQ / BPA / etc.) |
| **Issuing Agency / Office** | |
| **Set-Aside** | (Small Business / 8(a) / SDVOSB / HUBZone / WOSB / Unrestricted / etc.) |
| **Contract Type** | (Firm-Fixed-Price / T&M / Cost-Plus / IDIQ / etc.) |
| **Contract Period** | |
| **NAICS Code** | |
| **Estimated Value** | |

---

## Scope of Work

*Describe what is required in full detail: services, deliverables, performance standards, and any technical requirements. Use bullet points.*

---

## Performance Location(s)

*List all work sites. Note whether remote or on-site work is permitted.*

---

## Key Dates & Deadlines

| Milestone | Date & Time (with timezone) |
|---|---|
| Pre-Proposal Conference / Site Visit | |
| Questions Due | |
| **Proposal Due** | |
| Award Date (if stated) | |
| Period of Performance Start | |
| Other Deadlines | |

---

## Pre-Proposal Conference / Site Visit

| | |
|---|---|
| **Date & Time** | |
| **Full Address** | |
| **Attendance** | (Mandatory / Optional / Not applicable) |
| **RSVP Required** | (Yes — deadline & method / No) |

---

## Point of Contact (POC)

| | |
|---|---|
| **Name / Title** | |
| **Phone** | |
| **Email** | |
| **Questions Submission Method** | |

---

## Proposal Requirements

*Summarize format, page limits, required sections/volumes, submission method (portal/email/mail), and number of copies. Use bullet points.*

---

## Evaluation Factors

*List evaluation criteria and their weights or order of priority as stated in the solicitation.*

---

## Travel & Logistics

| | |
|---|---|
| **Origin** | {office} |
| **Destination** | (Pre-Proposal / Primary Work Site address) |
| **Estimated Driving Distance** | |
| **Estimated Drive Time** | (normal traffic conditions) |

*(AI estimate — verify with a mapping service before scheduling travel.)*

---

## Notable Requirements & Red Flags

*List anything that affects bid/no-bid decisions: unusual insurance or bonding levels, required security clearances, certifications, teaming or subcontracting restrictions, incumbent advantage indicators, aggressive timelines, or any other risk factors.*

---

## Total Square Footage

*(If applicable to the scope of work; write "N/A" if not.)*

---

## Overall Summary

Provide a concise but thorough summary covering:

- **What is Being Procured** — Scope and key deliverables.
- **Contract Period & Estimated Value** — Duration and any stated ceiling or estimate.
- **Key Constraints & Requirements** — Timeline, location, special certifications, etc.
- **Immediate Action Items** — What the team must do and by when.

---

DOCUMENTS:
{documents}"""

# Stage 2 — criteria evaluation (sent as a separate API call when active criteria exist).
# Receives the clean Stage 1 extraction output as {summary}, not the raw documents,
# so the model can focus entirely on the evaluation without re-parsing document noise.
_CRITERIA_PROMPT = """\
Below is an extracted and summarized government solicitation opportunity.
Evaluate whether our company should pursue it based on our evaluation criteria.

---

## OPPORTUNITY SUMMARY

{summary}

---

## ALIGNMENT EVALUATION

**Our evaluation criteria:**

{criteria_list}

For **each criterion** listed above, provide:
- A status: ✅ **MEETS** / ⚠️ **PARTIALLY MEETS** / ❌ **DOES NOT MEET** / ❓ **CANNOT DETERMINE**
- A 1–2 sentence explanation citing specific details from the summary above.

---

## Overall Recommendation

State your recommendation on its own line in **exactly** this format (required for system parsing — do not alter the label):

```
RECOMMENDATION: PURSUE
```
or
```
RECOMMENDATION: PASS
```
or
```
RECOMMENDATION: UNCLEAR
```

**PURSUE** — opportunity clearly aligns with most criteria and is competitive.
**PASS** — fails one or more critical criteria or presents unacceptable risk.
**UNCLEAR** — insufficient information to make a confident determination.

---

## Executive Summary

Write 3–4 sentences in plain business language explaining your recommendation:
the strongest reasons to pursue or pass, and the single biggest risk or opportunity."""


def _call_groq_api(api_key: str, model: str, user_message: str) -> str:
    """Single Groq chat completions call. Raises on HTTP error."""
    response = http_requests.post(
        "https://api.groq.com/openai/v1/chat/completions",
        headers={
            "Authorization": f"Bearer {api_key}",
            "Content-Type":  "application/json",
        },
        json={
            "model":       model,
            "messages":    [
                {"role": "system", "content": _SYSTEM_PROMPT},
                {"role": "user",   "content": user_message},
            ],
            "max_tokens":  8192,
            "temperature": 0.1,
        },
        timeout=120,
    )
    if not response.ok:
        logger.error(f"Groq API {response.status_code}: {response.text[:300]}")
        response.raise_for_status()
    return response.json()["choices"][0]["message"]["content"] or ""


def _call_groq(api_key: str, model: str, text: str, criteria: list) -> dict:
    """
    Two-stage Groq analysis:
      Stage 1 (always):   extract structured fields + overall summary from documents.
      Stage 2 (optional): evaluate criteria against the Stage 1 output (not raw docs),
                          producing a focused RECOMMENDATION with per-criterion scoring.
    """
    truncated = len(text) > _TEXT_LIMIT
    doc_text  = text[:_TEXT_LIMIT]
    n         = doc_text.count("=== ") or 1

    # ── Stage 1: extraction ────────────────────────────────────────────────────
    stage1_prompt = _EXTRACTION_PROMPT.format(
        n=n, office=_OFFICE_ADDRESS, documents=doc_text
    )
    summary = _call_groq_api(api_key, model, stage1_prompt)

    if not criteria:
        return {"verdict": None, "summary": summary, "truncated": truncated}

    # ── Stage 2: criteria evaluation (uses clean summary, not raw docs) ────────
    criteria_list = "\n".join(
        f"{i+1}. **{c['title']}**: {c['description']}"
        for i, c in enumerate(criteria)
    )
    stage2_prompt = _CRITERIA_PROMPT.format(
        summary=summary, criteria_list=criteria_list
    )
    evaluation = _call_groq_api(api_key, model, stage2_prompt)

    verdict = None
    match = re.search(
        r"RECOMMENDATION\s*:\s*(PURSUE|PASS|UNCLEAR)",
        evaluation, re.IGNORECASE,
    )
    if match:
        verdict = match.group(1).upper()

    combined = summary + "\n\n---\n\n" + evaluation
    return {"verdict": verdict, "summary": combined, "truncated": truncated}


# ─── Criteria Management (admin only) ─────────────────────────────────────────

@ai_summary_bp.route("/criteria/create", methods=["POST"])
@admin_required
def create_criterion_view():
    admin      = session["user"]
    title      = request.form.get("title", "").strip()
    desc       = request.form.get("description", "").strip()
    is_active  = request.form.get("is_active", "1") == "1"
    try:
        sort_order = int(request.form.get("sort_order", 0) or 0)
    except (ValueError, TypeError):
        sort_order = 0
    try:
        create_criterion(admin["id"], title, desc, is_active, sort_order)
        flash(f"Criterion '{title}' created.", "success")
    except Exception as e:
        flash(f"Error: {e}", "danger")
    return redirect(url_for("ai_summary.ai_summary"))


@ai_summary_bp.route("/criteria/<int:criterion_id>/edit", methods=["POST"])
@admin_required
def edit_criterion_view(criterion_id):
    admin      = session["user"]
    title      = request.form.get("title", "").strip()
    desc       = request.form.get("description", "").strip()
    is_active  = request.form.get("is_active", "1") == "1"
    try:
        sort_order = int(request.form.get("sort_order", 0) or 0)
    except (ValueError, TypeError):
        sort_order = 0
    try:
        update_criterion(admin["id"], criterion_id, title, desc, is_active, sort_order)
        flash(f"Criterion '{title}' updated.", "success")
    except Exception as e:
        flash(f"Error: {e}", "danger")
    return redirect(url_for("ai_summary.ai_summary"))


@ai_summary_bp.route("/criteria/<int:criterion_id>/delete", methods=["POST"])
@admin_required
def delete_criterion_view(criterion_id):
    admin = session["user"]
    try:
        delete_criterion(admin["id"], criterion_id)
        flash("Criterion deleted.", "success")
    except Exception as e:
        flash(f"Error: {e}", "danger")
    return redirect(url_for("ai_summary.ai_summary"))


@ai_summary_bp.route("/history/<int:analysis_id>")
@login_required
def analysis_detail(analysis_id):
    record = get_ai_analysis_detail(analysis_id)
    if not record:
        return jsonify({"error": "Not found"}), 404
    return jsonify({
        "id":                record["id"],
        "username":          record.get("username"),
        "file_names":        record["file_names"],
        "model":             record["model"],
        "verdict":           record["verdict"],
        "analyzed_at":       str(record["analyzed_at"]),
        "summary_text":      record["summary_text"],
        "criteria_snapshot": record.get("criteria_snapshot") or "",
    })


@ai_summary_bp.route("/history/<int:analysis_id>/delete", methods=["POST"])
@login_required
def delete_analysis(analysis_id):
    user = session["user"]
    if user["role"] != "admin":
        return jsonify({"error": "Access denied"}), 403
    record = get_ai_analysis_detail(analysis_id)
    if not record:
        return jsonify({"error": "Not found"}), 404
    delete_ai_analysis(analysis_id)
    log_action(user["id"], "DELETE_AI_ANALYSIS", "ai_analysis_log", analysis_id,
               f"Deleted AI analysis id={analysis_id}.")
    return jsonify({"ok": True})