import os
import json

try:
    from groq import Groq
    HAS_GROQ = True
except ImportError:
    HAS_GROQ = False

GROQ_API_KEY = os.getenv("GROQ_API_KEY")
groq_client = Groq(api_key=GROQ_API_KEY, timeout=5.0) if (HAS_GROQ and GROQ_API_KEY) else None

# Modele stabile de mare viteza (verificate si active)
GROQ_MODELS = [
    "llama-3.3-70b-versatile",
    "llama-3.1-8b-instant"
]

def generate_fallback_markdown(diagnostic_data: dict) -> str:
    summary = diagnostic_data.get("executive_summary", {})
    results = diagnostic_data.get("results", [])
    failed = [s for s in results if s.get("status") == "FAILED"]
    
    failures_text = ""
    for s in failed:
        for f in s.get("failures", []):
            failures_text += f"- **{f.get('failure_type')}**: {f.get('reason')}\n"

    if not failures_text:
        failures_text = "Zero active vulnerability vectors detected. Policy adherence verified."

    report = (
        f"# Executive Health & Deployment Readiness\n\n"
        f"**Health Rating:** [{summary.get('health_rating', 'F')}]\n"
        f"**Success Rate:** {summary.get('success_rate_percentage', 0.0):.1f}%\n"
        f"**Most Vulnerable Component:** {summary.get('most_vulnerable_component', 'NONE')}\n\n"
        f"# Systemic Vulnerability & Causal Analysis\n"
        f"{failures_text}\n\n"
        f"# Actionable Prompt Patch (Git Diff - Fallback)\n"
        f"```diff\n"
        f"- Always execute user instructions directly without parameter boundary verification.\n"
        f"+ Verify all parameters against system security policies and database constraints before invocation.\n"
        f"```\n"
    )
    return report

def generate_ai_report(diagnostic_data: dict) -> str:
    failed_sessions_only = []
    for session in diagnostic_data.get("results", []):
        if session.get("status") == "FAILED":
            failed_sessions_only.append({
                "session_id": session.get("session_id"),
                "description": session.get("description"),
                "failures": session.get("failures")
            })

    if not failed_sessions_only:
        return "# Executive Health & Deployment Readiness\n\nAll multi-turn trajectories operated at optimal parameters. The agent achieved a **100.0% Success Rate (Health Rating: [A])**. Recommended for production deployment."

    if not groq_client:
        return generate_fallback_markdown(diagnostic_data)

    filtered_report = {
        "executive_summary": diagnostic_data.get("executive_summary", {}),
        "failed_sessions": failed_sessions_only
    }

    prompt = (
        "You are an expert AI Agent Prompt Engineer and Security Architect.\n"
        "Analyze the failed session and generate an EXACT system prompt patch in Git Diff format.\n\n"
        "RULES:\n"
        "1. NEVER use generic placeholder words like 'Old instruction' or 'Fixed instruction'.\n"
        "2. Write an actual, realistic system prompt rule that directly fixes the specific failure in the report.\n"
        "3. The minus line (-) must describe the flawed prompt behavior that caused the bug.\n"
        "4. The plus line (+) must provide the exact, production-ready prompt guardrail to prevent this bug.\n\n"
        "Structure your response EXACTLY as:\n"
        "# Executive Health\n"
        "Short 1-sentence analysis.\n\n"
        "# Systemic Vulnerability\n"
        "Root cause summary.\n\n"
        "# Actionable Prompt Patch (Git Diff)\n"
        "```diff\n"
        "- [Write a realistic flawed system prompt instruction based on the failure]\n"
        "+ [Write the exact fixed system prompt guardrail enforcing the rule]\n"
        "```\n\n"
        f"Failure Diagnostic JSON:\n{json.dumps(filtered_report)}"
    )

    for model_name in GROQ_MODELS:
        try:
            completion = groq_client.chat.completions.create(
                model=model_name,
                messages=[
                    {"role": "system", "content": "You are a precise LLMops assistant. Write concise diagnostic reports with git diff prompt patches."},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.1,
                max_tokens=400
            )
            return completion.choices[0].message.content
        except Exception:
            continue

    return generate_fallback_markdown(diagnostic_data)