#!/usr/bin/env python3
"""
Upgrade Repo Script
Reads market research and suggests code upgrades using Gemini and Jules.
"""

import os
import logging
import requests
import google.generativeai as genai
from pathlib import Path
from datetime import datetime
from dotenv import load_dotenv

# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

REPO_ROOT = Path(__file__).resolve().parents[1]
DOCS_DIR = REPO_ROOT / "docs"

# Load env vars
load_dotenv()

def ask_jules(prompt):
    api_key = os.environ.get("JULES_API_KEY")
    api_url = os.environ.get("JULES_API_URL")
    model = os.environ.get("JULES_MODEL", "jules-v1")

    if not api_key or not api_url:
        logger.warning("Skipping Jules (Key/URL missing)")
        return None

    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {api_key}"
    }
    payload = {
        "model": model,
        "prompt": prompt
    }

    try:
        response = requests.post(api_url, json=payload, headers=headers, timeout=60)
        response.raise_for_status()
        try:
            resp_json = response.json()
            if "response" in resp_json:
                return resp_json["response"]
            elif "choices" in resp_json and len(resp_json["choices"]) > 0:
                return resp_json["choices"][0].get("text", str(resp_json))
            else:
                return str(resp_json)
        except ValueError:
            return response.text
    except Exception as e:
        logger.error(f"Jules request failed: {e}")
        return None

def ask_gemini(prompt):
    api_key = os.environ.get("GEMINI_API_KEY") or os.environ.get("GOOGLE_API_KEY")
    if not api_key:
        logger.warning("Skipping Gemini (Key missing)")
        return None

    try:
        genai.configure(api_key=api_key)
        model_name = os.environ.get("GEMINI_MODEL", 'gemini-1.5-flash')
        model = genai.GenerativeModel(model_name)
        response = model.generate_content(prompt)
        return response.text
    except Exception as e:
        logger.error(f"Gemini request failed: {e}")
        return None

def main():
    logger.info("Starting Code Upgrade Analysis...")

    report_path = DOCS_DIR / "market_research_report.md"
    if not report_path.exists():
        logger.warning("No market research report found. Skipping upgrade analysis.")
        return

    with open(report_path, 'r') as f:
        research_content = f.read()

    # Get EA code context
    ea_path = REPO_ROOT / "mt5/MQL5/Experts/SMC_TrendBreakout_MTF_EA.mq5"
    ea_code = ""
    if ea_path.exists():
        with open(ea_path, 'r') as f:
            ea_code = f.read()[:5000]

    prompt = f"""
    Based on the following market research, suggest 3 specific code upgrades or parameter adjustments for the trading bot.

    Market Research:
    {research_content}

    Current EA Code Snippet (Top 5000 chars):
    {ea_code}

    Output format:
    1. [File Name]: [Suggestion] - [Reasoning]
    """

    gemini_suggestions = ask_gemini(prompt)
    jules_suggestions = ask_jules(prompt)

    if not gemini_suggestions and not jules_suggestions:
        logger.warning("Both AI providers failed or keys missing.")
        return

    suggestion_path = DOCS_DIR / "upgrade_suggestions.md"
    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

    content = f"# Upgrade Suggestions\n\nGenerated: {timestamp}\n\n"

    if gemini_suggestions:
        content += f"## Gemini Suggestions\n\n{gemini_suggestions}\n\n"

    if jules_suggestions:
        content += f"## Jules Suggestions\n\n{jules_suggestions}\n\n"

    with open(suggestion_path, 'w') as f:
        f.write(content)

    logger.info(f"Suggestions saved to {suggestion_path}")

if __name__ == "__main__":
    main()