handler.py

from typing import Dict, Any
import torch
import os
import base64
import io
from PIL import Image
import logging
import requests
import traceback  # For formatting exception tracebacks
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
from moviepy.editor import VideoFileClip

class EndpointHandler():
    """
    Handler class for the Qwen2-VL-7B-Instruct model on Hugging Face Inference Endpoints.
    This handler processes text, image, and video inputs, leveraging the Qwen2-VL model
    for multimodal understanding and generation.
    """

    def __init__(self, path=""):
        """
        Initializes the handler and loads the Qwen2-VL model.
        Args:
            path (str, optional): The path to the Qwen2-VL model directory. Defaults to "".
        """
        self.model_dir = path

        # Load the Qwen2-VL model
        self.model = Qwen2VLForConditionalGeneration.from_pretrained(
            self.model_dir, torch_dtype="auto", device_map="auto"
        )
        self.processor = AutoProcessor.from_pretrained(self.model_dir)

    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Processes the input data and returns the Qwen2-VL model's output.
        Args:
            data (Dict[str, Any]): A dictionary containing the input data.
                - "inputs" (str): The input text, including image/video references.
                - "max_new_tokens" (int, optional): Max tokens to generate (default: 128).
        Returns:
            Dict[str, Any]: A dictionary containing the generated text.
        """
        inputs = data.get("inputs")
        max_new_tokens = data.get("max_new_tokens", 128)

        # Construct the messages list from the input string
        messages = [{"role": "user", "content": self._parse_input(inputs)}]

        # Prepare for inference (using qwen_vl_utils)
        text = self.processor.apply_chat_template(
            messages, tokenize=False, add_generation_prompt=True
        )
        image_inputs, video_inputs = process_vision_info(messages)

        inputs = self.processor(
            text=[text],
            images=image_inputs,
            videos=video_inputs,
            padding=True,
            return_tensors="pt",
        )
        inputs = inputs.to("cuda" if torch.cuda.is_available() else "cpu")

        # Inference
        generated_ids = self.model.generate(**inputs, max_new_tokens=max_new_tokens)
        generated_ids_trimmed = [
            out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
        ]
        output_text = self.processor.batch_decode(
            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
        )[0]

        return {"generated_text": output_text}

    def _parse_input(self, input_string):
        """
        Parses the input string to identify image/video references and text.
        Args:
            input_string (str): The input string containing text, image, and video references.
        Returns:
            list: A list of dictionaries representing the parsed content.
        """
        content = []
        parts = input_string.split("<image>")
        for i, part in enumerate(parts):
            if i % 2 == 0:  # Text part
                content.append({"type": "text", "text": part.strip()})
            else:  # Image/video part
                if part.lower().startswith("video:"):
                    video_path = part.split("video:")[1].strip()
                    video_frames = self._extract_video_frames(video_path)
                    if video_frames:
                        content.append({"type": "video", "video": video_frames, "fps": 1})
                else:
                    image = self._load_image(part.strip())
                    if image:
                        content.append({"type": "image", "image": image})
        return content

    def _load_image(self, image_data):
        """
        Loads an image from a URL or base64 encoded string.
        Args:
            image_data (str): The image data, either a URL or a base64 encoded string.
        Returns:
            PIL.Image.Image or None: The loaded image, or None if loading fails.
        """
        try:
            if image_data.startswith("http"):
                response = requests.get(image_data, stream=True)
                response.raise_for_status()  # Check for HTTP errors
                return Image.open(response.raw)
            elif image_data.startswith("data:image"):
                base64_data = image_data.split(",")[1]
                image_bytes = base64.b64decode(base64_data)
                return Image.open(io.BytesIO(image_bytes))
        except requests.exceptions.RequestException as e:
            logging.error(f"HTTP error occurred while loading image: {e}")
        except IOError as e:
            logging.error(f"Error opening image: {e}")
        return None

    def _extract_video_frames(self, video_path, fps=1):
        """
        Extracts frames from a video at the specified FPS using MoviePy.
        Args:
            video_path (str): The path or URL of the video file.
            fps (int, optional): The desired frames per second. Defaults to 1.
        Returns:
            list or None: A list of PIL Images representing the extracted frames,
                          or None if extraction fails.
        """
        try:
            with VideoFileClip(video_path) as video:
                return [Image.fromarray(frame.astype('uint8'), 'RGB') for frame in video.iter_frames(fps=fps)]
        except Exception as e:
            logging.error(f"Error extracting video frames: {e}")
            return None

# Additional configurations for logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')