services/screen_service.py

import threading
import queue
import time
import base64
import io
import logging
from typing import Callable, Optional, List, Dict

import mss
import numpy as np
from PIL import Image

from openai import OpenAI
from config.settings import Settings

logger = logging.getLogger(__name__)

class ScreenService:
    def __init__(
        self,
        prompt: str,
        model: str,
        fps: float = 0.5,
        queue_size: int = 2,
        monitor: int = 1,
        max_width: int = 3440,
        max_height: int = 1440,
        compression_quality: int = 100,
        image_format: str = "PNG",
    ):
        """
        :param prompt: Vision model instruction
        :param model: Nebius model name
        :param fps: Capture frames per second
        :param queue_size: Internal buffer size
        :param monitor: MSS monitor index
        :param max_width/max_height: Max resolution for resizing
        :param compression_quality: JPEG quality (1-100)
        :param image_format: "JPEG" or "PNG" (PNG is lossless)
        """
        self.prompt = prompt
        self.model = model
        self.fps = fps
        self.queue: queue.Queue = queue.Queue(maxsize=queue_size)
        self.monitor = monitor
        self.max_width = max_width
        self.max_height = max_height
        self.compression_quality = compression_quality
        self.image_format = image_format.upper()

        self._stop_event = threading.Event()
        self._producer: Optional[threading.Thread] = None
        self._consumer: Optional[threading.Thread] = None

        # Nebius client
        self.client = OpenAI(
            base_url=Settings.NEBIUS_BASE_URL,
            api_key=Settings.NEBIUS_API_KEY
        )

    def _process_image(self, img: Image.Image) -> Image.Image:
        # Convert to RGB if needed
        if img.mode != "RGB":
            img = img.convert("RGB")
        w, h = img.size
        ar = w / h
        # Resize maintaining aspect ratio if above max
        if w > self.max_width or h > self.max_height:
            if ar > 1:
                new_w = min(w, self.max_width)
                new_h = int(new_w / ar)
            else:
                new_h = min(h, self.max_height)
                new_w = int(new_h * ar)
            img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
        return img

    def _image_to_base64(self, img: Image.Image) -> str:
        buf = io.BytesIO()
        if self.image_format == "PNG":
            img.save(buf, format="PNG")
        else:
            img.save(
                buf,
                format="JPEG",
                quality=self.compression_quality,
                optimize=True
            )
        data = buf.getvalue()
        return base64.b64encode(data).decode("utf-8")

    def _capture_loop(self):
        with mss.mss() as sct:
            mon = sct.monitors[self.monitor]
            interval = 1.0 / self.fps if self.fps > 0 else 0
            while not self._stop_event.is_set():
                t0 = time.time()
                frame = np.array(sct.grab(mon))
                pil = Image.fromarray(frame)
                pil = self._process_image(pil)
                b64 = self._image_to_base64(pil)
                try:
                    self.queue.put_nowait((t0, b64))
                except queue.Full:
                    self.queue.get_nowait()
                    self.queue.put_nowait((t0, b64))
                if interval:
                    time.sleep(interval)

    def _flatten_conversation_history(self, history: List[Dict[str, str]]) -> str:
        """Flatten conversation history into a readable format for the vision model"""
        if not history:
            return "No previous conversation."
        
        # Filter out system messages and vision outputs to avoid confusion
        filtered_history = []
        for msg in history:
            role = msg.get('role', '')
            content = msg.get('content', '')
            
            # Skip system messages and previous vision outputs
            if role == 'system':
                continue
            if content.startswith('VISION MODEL OUTPUT:'):
                continue
            if 'screen' in content.lower() and 'sharing' in content.lower():
                continue
                
            filtered_history.append(msg)
        
        # Take only the last 10 exchanges to keep context manageable
        if len(filtered_history) > 20:  # 10 user + 10 assistant messages
            filtered_history = filtered_history[-20:]
        
        # Format the conversation
        formatted_lines = []
        for msg in filtered_history:
            role = msg.get('role', 'unknown')
            content = msg.get('content', '')
            
            # Truncate very long messages
            if len(content) > 200:
                content = content[:200] + "..."
            
            if role == 'user':
                formatted_lines.append(f"User: {content}")
            elif role == 'assistant':
                formatted_lines.append(f"Assistant: {content}")
        
        return "\n".join(formatted_lines) if formatted_lines else "No relevant conversation history."

    def _inference_loop(
        self,
        callback: Callable[[Dict, float, str], None],
        history_getter: Callable[[], List[Dict[str, str]]]
    ):
        while not self._stop_event.is_set():
            try:
                t0, frame_b64 = self.queue.get(timeout=1)
            except queue.Empty:
                continue

            # Get and flatten the conversation history
            history = history_getter()
            flattened_history = self._flatten_conversation_history(history)
            
            # Create the full prompt with system instructions and conversation context
            full_prompt = f"{self.prompt}\n\nCONVERSATION CONTEXT:\n{flattened_history}"

            for i, msg in enumerate(history):
                content_preview = msg.get('content', '')[:100] + "..." if len(msg.get('content', '')) > 100 else msg.get('content', '')

            user_message = {
                "role": "user",
                "content": [
                    {"type": "text", "text": full_prompt},
                    {"type": "image_url", "image_url": {"url": f"data:image/{self.image_format.lower()};base64,{frame_b64}"}}
                ]
            }
            
            try:
                resp = self.client.chat.completions.create(
                    model=self.model,
                    messages=[user_message]
                )
                latency = time.time() - t0
                callback(resp, latency, frame_b64)
            except Exception as e:
                logger.error(f"Nebius inference error: {e}")

    def start(
        self,
        callback: Callable[[Dict, float, str], None],
        history_getter: Callable[[], List[Dict[str, str]]]
    ) -> None:
        if self._producer and self._producer.is_alive():
            return
        self._stop_event.clear()
        self._producer = threading.Thread(target=self._capture_loop, daemon=True)
        self._consumer = threading.Thread(
            target=self._inference_loop,
            args=(callback, history_getter),
            daemon=True
        )
        self._producer.start()
        self._consumer.start()
        logger.info("ScreenService started.")

    def stop(self) -> None:
        self._stop_event.set()
        if self._producer:
            self._producer.join(timeout=1.0)
        if self._consumer:
            self._consumer.join(timeout=1.0)
        logger.info("ScreenService stopped.")