#!/usr/bin/env python3
from dotenv import load_dotenv
load_dotenv()

import os
import json
import argparse
import sys
from typing import Dict, Any
from openai import OpenAI


def resolve_api_key() -> str:
    # Canonical env var is API_KEY, aliases are supported for compatibility.
    return (
        (os.environ.get("API_KEY") or "").strip()
        or (os.environ.get("HF_TOKEN") or "").strip()
        or (os.environ.get("OPENAI_API_KEY") or "").strip()
    )


API_BASE_URL = os.environ.get("API_BASE_URL", "")
MODEL_NAME = os.environ.get("MODEL_NAME", "")
API_KEY = resolve_api_key()
TEMPERATURE = float(os.environ.get("TEMPERATURE", "0.7"))
MAX_TOKENS = int(os.environ.get("MAX_TOKENS", "2000"))
REQUEST_TIMEOUT = int(os.environ.get("REQUEST_TIMEOUT", "60"))

if not API_BASE_URL:
    print("=" * 60)
    print("API Configuration Required")
    print("=" * 60)
    print("\nPlease set the following environment variables:\n")
    print("  API_BASE_URL  - OpenAI-compatible API endpoint")
    print("  MODEL_NAME    - Model identifier")
    print("  API_KEY       - API key (canonical)\n")
    print("Supported auth aliases (backward compatibility):")
    print("  HF_TOKEN")
    print("  OPENAI_API_KEY\n")
    print("Examples:\n")
    print("  OpenAI:")
    print("    export API_BASE_URL=https://api.openai.com/v1")
    print("    export MODEL_NAME=gpt-4o-mini")
    print("    export API_KEY=sk-xxxxx\n")
    print("  Groq:")
    print("    export API_BASE_URL=https://api.groq.com/openai/v1")
    print("    export MODEL_NAME=llama-3.3-70b-versatile")
    print("    export API_KEY=gsk_xxxxx\n")
    print("  Local Ollama:")
    print("    export API_BASE_URL=http://localhost:11434/v1")
    print("    export MODEL_NAME=llama3")
    print("    export API_KEY=not-needed\n")
    print("=" * 60)
    sys.exit(1)

if not MODEL_NAME:
    print("ERROR: MODEL_NAME environment variable is required")
    sys.exit(1)

if not API_KEY:
    print("ERROR: Missing auth token. Set API_KEY (preferred), or HF_TOKEN/OPENAI_API_KEY.")
    sys.exit(1)

FALLBACK_ACTION = json.dumps({
    "action_type": "request_changes",
    "comments": [],
    "suggestions": [],
    "final_decision": "changes_requested"
})


def add_line_numbers(code: str) -> str:
    lines = code.split("\n")
    return "\n".join(f"{i+1}: {line}" for i, line in enumerate(lines))


class LLMClient:

    def __init__(self, base_url: str, api_key: str, model: str):
        self.base_url = base_url.rstrip("/")
        self.api_key = api_key
        self.model = model
        self.client = OpenAI(
            base_url=self.base_url,
            api_key=self.api_key,
            timeout=REQUEST_TIMEOUT
        )
        print("Connected using OpenAI client")
        print(f"Endpoint: {self.base_url}")
        print(f"Model: {self.model}\n")

    def chat_completion(self, messages: list, temperature: float = 0.7, max_tokens: int = 2000) -> str:
        completion = self.client.chat.completions.create(
            model=self.model,
            messages=messages,
            temperature=temperature,
            max_tokens=max_tokens,
            stream=False,
        )
        return completion.choices[0].message.content or ""


class CodeReviewAgent:

    def __init__(self):
        self.client = LLMClient(API_BASE_URL, API_KEY, MODEL_NAME)
        self.history = []
        self.phase = 1

    def get_action(self, observation: Dict[str, Any]) -> str:

        system_prompt = """You are an expert code reviewer. You MUST follow this exact sequence:

PHASE 1 - Add Comments: Use action_type "add_comment" to identify ALL bugs with exact line numbers
PHASE 2 - Suggest Fixes: Use action_type "suggest_fix" to provide fixes for every bug found
PHASE 3 - Final Decision: Use action_type "request_changes" with final_decision "changes_requested"

RULES:
- NEVER skip straight to approve or request_changes without first adding comments and suggestions
- NEVER combine phases - each action should do ONE thing
- ALWAYS use the exact line numbers shown in the code diff
- ALWAYS set severity for comments: "critical", "high", "medium", or "low"
- If no bugs found in Phase 1, skip to Phase 3 with "approved"

Respond ONLY with a valid JSON object, no extra text:
{
    "action_type": "add_comment" | "suggest_fix" | "approve" | "request_changes",
    "comments": [
        {
            "line_number": <exact line number>,
            "content": "Detailed explanation of the bug",
            "is_issue": true,
            "severity": "critical" | "high" | "medium" | "low"
        }
    ],
    "suggestions": [
        {
            "original_line": <exact line number>,
            "suggested_code": "corrected code here",
            "explanation": "why this fix works"
        }
    ],
    "final_decision": "approved" | "changes_requested"
}"""

        prev_comments = observation.get('previous_comments', [])
        prev_suggestions = observation.get('previous_suggestions', [])

        comments_text = "\n".join([
            f"  Line {c.get('line_number') if isinstance(c, dict) else c.line_number}: "
            f"{c.get('content') if isinstance(c, dict) else c.content}"
            for c in prev_comments
        ]) or "None yet"

        suggestions_text = "\n".join([
            f"  Line {s.get('original_line') if isinstance(s, dict) else s.original_line}: "
            f"{s.get('suggested_code') if isinstance(s, dict) else s.suggested_code}"
            for s in prev_suggestions
        ]) or "None yet"

        if self.phase == 1:
            phase_instruction = """
YOUR TASK NOW (Phase 1 - Add Comments):
- action_type MUST be "add_comment"
- Carefully read the code diff line by line
- Find ALL bugs, vulnerabilities, or issues
- Comment on each one with the EXACT line number shown
- Do NOT make a final decision yet
- Do NOT suggest fixes yet
"""
        elif self.phase == 2:
            phase_instruction = """
YOUR TASK NOW (Phase 2 - Suggest Fixes):
- action_type MUST be "suggest_fix"
- For every bug you commented on, provide a concrete code fix
- Use the same line numbers as your comments
- Do NOT make a final decision yet
"""
        else:
            phase_instruction = """
YOUR TASK NOW (Phase 3 - Final Decision):
- action_type MUST be "request_changes"
- Set final_decision to "changes_requested"
- No new comments or suggestions needed
"""

        user_prompt = f"""
Code Review Task:
{observation.get('task_description', 'Review the following code changes')}

Code Diff (USE THESE EXACT LINE NUMBERS in your response):
{add_line_numbers(observation.get('code_diff', ''))}

File Context:
{observation.get('file_context', '')}

Current Step: {observation.get('current_step', 0)}/{observation.get('max_steps', 50)}

Comments already made:
{comments_text}

Suggestions already made:
{suggestions_text}

{phase_instruction}

Respond with JSON only.
"""

        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt}
        ]

        try:
            response = self.client.chat_completion(messages, TEMPERATURE, MAX_TOKENS)
            response = response.strip()

            if "```json" in response:
                response = response.split("```json")[1].split("```")[0]
            elif "```" in response:
                response = response.split("```")[1].split("```")[0]

            action_data = json.loads(response.strip())

            if "action_type" not in action_data:
                action_data["action_type"] = "request_changes"
            if "comments" not in action_data:
                action_data["comments"] = []
            if "suggestions" not in action_data:
                action_data["suggestions"] = []

            self.phase += 1
            return json.dumps(action_data)

        except json.JSONDecodeError as e:
            print(f"Failed to parse JSON response: {e}")
            print(f"Raw response: {response[:200]}...")
            self.phase += 1
            return FALLBACK_ACTION
        except Exception as e:
            print(f"Error getting action from LLM: {e}")
            return FALLBACK_ACTION

    def validate_action(self, action: Dict, observation: Dict) -> Dict:
        line_count = observation.get('line_count', 999)

        for comment in action.get("comments", []):
            comment["line_number"] = max(1, min(comment.get("line_number", 1), line_count))
            if not comment.get("severity"):
                comment["severity"] = "medium"
            if "is_issue" not in comment:
                comment["is_issue"] = True

        for suggestion in action.get("suggestions", []):
            suggestion["original_line"] = max(1, min(suggestion.get("original_line", 1), line_count))

        return action

    def parse_action(self, action_str: str) -> Dict[str, Any]:
        try:
            return json.loads(action_str)
        except json.JSONDecodeError:
            return {"action_type": "request_changes", "comments": [], "suggestions": []}


def main():
    sys.path.append('.')

    try:
        from environment.env import CodeReviewEnv
    except ImportError as e:
        print(f"Failed to import environment: {e}")
        print("Make sure you're in the correct directory and environment is installed.")
        sys.exit(1)

    parser = argparse.ArgumentParser(description="Run code review agent")
    parser.add_argument("--task-id", type=str, default="bug_detection_easy_1")
    parser.add_argument("--max-steps", type=int, default=50)
    parser.add_argument("--output", type=str, default="baseline_results.json")
    args = parser.parse_args()

    print("=" * 60)
    print("Code Review Agent")
    print("=" * 60)

    env = CodeReviewEnv()
    env.max_steps = args.max_steps
    agent = CodeReviewAgent()

    obs = env.reset(task_id=args.task_id)
    done = False
    step = 0
    total_reward = 0.0

    print(f"\nTask    : {args.task_id}")
    print(f"Desc    : {obs.get('task_description', 'N/A')}")
    print(f"Model   : {MODEL_NAME}")
    print("-" * 60)

    while not done and step < args.max_steps:
        action_str = agent.get_action(obs)
        action = agent.parse_action(action_str)
        action = agent.validate_action(action, obs)

        obs, reward, done, info = env.step(action)
        total_reward += reward
        step += 1

        print(f"\nStep {step}/{args.max_steps}:")
        print(f"  Phase       : {agent.phase - 1}")
        print(f"  Action      : {action.get('action_type')}")
        print(f"  Comments    : {len(action.get('comments', []))}")
        print(f"  Suggestions : {len(action.get('suggestions', []))}")
        print(f"  Reward      : {reward:.3f}")
        print(f"  Total       : {total_reward:.3f}")
        print(f"  Score       : {info.get('task_score', 0):.3f}")

        if info.get('last_action_valid') is False:
            print(f"  Warning     : {info.get('error', 'Invalid action')}")

    final_score = env.get_task_score()

    print("\n" + "=" * 60)
    print("Final Results:")
    print(f"  Task         : {args.task_id}")
    print(f"  Total Reward : {total_reward:.3f}")
    print(f"  Task Score   : {final_score:.3f}/1.0")
    print(f"  Steps        : {step}")
    print("=" * 60)

    env.close()

    results = {
        "task_id": args.task_id,
        "total_reward": round(total_reward, 4),
        "task_score": round(final_score, 4),
        "steps": step,
        "max_steps": args.max_steps,
        "provider": "openai-client",
        "model": MODEL_NAME,
        "api_base_url": API_BASE_URL
    }

    with open(args.output, "w") as f:
        json.dump(results, f, indent=2)

    print(f"\nResults saved to {args.output}")


if __name__ == "__main__":
    main()