"""
agent_workflow.py
Agentic AI workflow using Groq (LLaMA-3).
Falls back to rule-based mock if the key is missing or the call fails.
"""

import json
import os


def run_groq_agent(
    detection_summary: dict,
    video_meta: dict,
    ai_summary: str,
) -> dict:
    """
    Run a Groq-powered analysis and return a JSON report.
    Falls back to mock if Groq is unavailable.
    """
    try:
        from groq import Groq

        client = Groq(api_key=os.getenv("GROQ_API_KEY"))

        payload = {
            "detection_summary": detection_summary,
            "video_meta": video_meta,
            "ai_summary": ai_summary,
        }

        prompt = f"""
You are a professional video-analysis assistant.

Analyze the following structured data and return ONLY valid JSON.

Required JSON schema:
{{
  "insights": ["...", "...", "..."],
  "risk_flags": ["..."],
  "recommended_actions": ["...", "...", "..."]
}}

Rules:
- Return ONLY JSON.
- "insights" should contain exactly 3 concise observations.
- "risk_flags" should be an empty list [] if there are no meaningful risks.
- "recommended_actions" should contain 2 or 3 useful next steps.
- Be factual and do not invent details beyond the provided data.

DATA:
{json.dumps(payload, indent=2)}
""".strip()

        response = client.chat.completions.create(
            model="llama3-8b-8192",
            messages=[{"role": "user", "content": prompt}],
            temperature=0.2,
            max_tokens=400,
        )

        content = response.choices[0].message.content.strip()
        clean = content.removeprefix("```json").removeprefix("```").removesuffix("```").strip()
        result = json.loads(clean)

        if not isinstance(result, dict):
            raise ValueError("LLM output is not a dictionary.")

        result.setdefault("insights", [])
        result.setdefault("risk_flags", [])
        result.setdefault("recommended_actions", [])

        if not isinstance(result["insights"], list):
            result["insights"] = []
        if not isinstance(result["risk_flags"], list):
            result["risk_flags"] = []
        if not isinstance(result["recommended_actions"], list):
            result["recommended_actions"] = []

        if len(result["insights"]) < 3:
            result["insights"] += [
                "Detected objects were analyzed across extracted frames.",
                "The summary is based on object frequency and video metadata.",
                "Manual review of annotated frames can provide additional context.",
            ]
            result["insights"] = result["insights"][:3]

        if len(result["recommended_actions"]) < 2:
            result["recommended_actions"] += [
                "Review the annotated frames to validate detections.",
                "Export the report for further analysis or archiving.",
            ]
            result["recommended_actions"] = result["recommended_actions"][:3]

        return result

    except Exception as e:
        return run_mock_agent(detection_summary, video_meta, ai_summary, error=str(e))


def run_mock_agent(
    detection_summary: dict,
    video_meta: dict,
    ai_summary: str,
    error: str = "",
) -> dict:
    """
    Rule-based fallback report.
    """
    total = sum(detection_summary.values())
    categories = len(detection_summary)
    top_class = max(detection_summary, key=detection_summary.get) if detection_summary else "none"
    top_count = detection_summary.get(top_class, 0)
    fps = max(video_meta.get("fps", 25), 1)
    duration = round(video_meta.get("total_frames", 0) / fps, 1)
    w = video_meta.get("width", "N/A")
    h = video_meta.get("height", "N/A")

    insights = [
        f"Dominant object: '{top_class}' detected {top_count} times across analyzed frames.",
        f"Total: {total} detections across {categories} categories in a {duration}s video.",
        f"Video resolution {w}x{h} @ {fps:.0f} fps — suitable for detailed object analysis.",
    ]

    risk_flags = []
    if detection_summary.get("person", 0) > 20:
        risk_flags.append("High person density — possible crowd or public-safety scenario.")
    if detection_summary.get("knife", 0) > 0 or detection_summary.get("gun", 0) > 0:
        risk_flags.append("Potentially dangerous object detected — manual review recommended.")
    if total == 0:
        risk_flags.append("Zero detections — video may be blank, very dark, or corrupted.")
    if categories == 1 and total > 15:
        risk_flags.append(
            f"Unusually high concentration of a single class ('{top_class}') — verify context."
        )
    if error:
        risk_flags.append(f"Groq agent unavailable, fallback mock analysis used: {error[:120]}")

    recommended_actions = [
        f"Review annotated frames focusing on '{top_class}' detections for context validation.",
        "Lower the confidence threshold if important objects appear to be missed.",
        "Export the JSON report for downstream analytics or alerting integration.",
    ]

    return {
        "insights": insights,
        "risk_flags": risk_flags,
        "recommended_actions": recommended_actions,
    }


def run_agent(
    detection_summary: dict,
    video_meta: dict,
    ai_summary: str,
    mode: str = "mock",
) -> dict:
    if mode == "groq":
        return run_groq_agent(detection_summary, video_meta, ai_summary)
    return run_mock_agent(detection_summary, video_meta, ai_summary)