# FrameProcessor/graph/steps/evaluate_importance.py
import json
import re
from langchain_core.messages import HumanMessage, SystemMessage
from llm.model import model
from langgraph.graph import END
from types_.state import GraphState

def evaluate_importance(state: GraphState) -> GraphState:
    """Use LLM to determine whether the frame is important."""

    if state["frame_features"].get("dark_ratio", 0) > 0.9:
        state["importance"] = "not_important"
        state["reason"] = "Frame is mostly black (over 90%)"
        state["next_step"] = END
        return state

    if "error" in state["frame_features"]:
        state["importance"] = "not_important"
        state["reason"] = f"Could not properly analyze frame: {state['frame_features']['error']}"
        state["next_step"] = END
        return state

    try:
        messages = [
            SystemMessage(content="""You are an expert in video summarization. Your task is to evaluate the importance of a video frame for inclusion in a video summary.

                                    Evaluate the frame and classify it as either "important" or "not_important" based on the following criteria:

                                    Important frames:
                                    - Contain essential information for the video
                                    - Show important events or scene changes
                                    - Contain important text or visual information
                                    - Represent key moments in the video

                                    Unimportant frames:
                                    - Black or single-color frames
                                    - Regular portrait shots unrelated to video content
                                    - Transitional or blurry frames
                                    - Frames very similar to previous ones

                                    Return a JSON containing:
                                    {
                                    "importance": "important" or "not_important",
                                    "reason": "reason for your classification"
                                    }
                                    """),
            HumanMessage(
                content=[
                    {"type": "text", "text": "Evaluate the importance of this video frame."},
                    {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{state['frame_data']['base64_image']}"}}
                ]
            )
        ]

        print("🔍 🔍 🔍🔍 🔍 🔍Calling Gemini: evaluate_importance")
        response = model.invoke(messages)
        print("✅ ✅ ✅✅ ✅ ✅ Gemini Done: evaluate_importance")

        try:
            json_match = re.search(r'({.*})', response.content.replace('\n', ' '))
            if json_match:
                result = json.loads(json_match.group(1))
                state["importance"] = result.get("importance", "not_important")
                state["reason"] = result.get("reason", "No reason provided")
            else:
                state["importance"] = "important" if "important" in response.content.lower() else "not_important"
                state["reason"] = response.content

        except Exception as e:
            print(f"Error parsing importance response: {str(e)}")
            state["importance"] = "not_important"
            state["reason"] = f"Error processing response: {str(e)}"

    except Exception as e:
        print(f"Error evaluating importance: {str(e)}")
        state["importance"] = "not_important"
        state["reason"] = f"Failed to evaluate: {str(e)}"

    state["next_step"] = "describe_frame" if state["importance"] == "important" else END
    return state