Update handler.py

handler.py

@@ -1,7 +1,4 @@
 from typing import Dict, Any
-from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
-from modelscope import snapshot_download
-from qwen_vl_utils import process_vision_info
 import torch
 import os
 import base64
@@ -9,36 +6,26 @@ import io
 from PIL import Image
 import logging
 import requests
-import subprocess
-from moviepy.editor import VideoFileClip
 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. It includes a runtime workaround to
-    install FFmpeg if it's not available in the environment.
+    for multimodal understanding and generation.
     """
 
     def __init__(self, path=""):
         """
-        Initializes the handler, installs FFmpeg, and loads the Qwen2-VL model.
-
+        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
 
-        # Install FFmpeg at runtime (this will run once during container initialization)
-        try:
-            subprocess.run(["apt-get", "update"], check=True)
-            subprocess.run(["apt-get", "install", "-y", "ffmpeg"], check=True)
-            logging.info("FFmpeg installed successfully.")
-        except subprocess.CalledProcessError as e:
-            logging.error(f"Error installing FFmpeg: {e}")
-
         # Load the Qwen2-VL model
         self.model = Qwen2VLForConditionalGeneration.from_pretrained(
             self.model_dir, torch_dtype="auto", device_map="auto"
@@ -48,12 +35,10 @@ class EndpointHandler():
     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.
         """
@@ -69,9 +54,6 @@ class EndpointHandler():
         )
         image_inputs, video_inputs = process_vision_info(messages)
 
-        logging.debug(f"Image inputs: {image_inputs}")
-        logging.debug(f"Video inputs: {video_inputs}")
-
         inputs = self.processor(
             text=[text],
             images=image_inputs,
@@ -95,10 +77,8 @@ class EndpointHandler():
     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.
         """
@@ -110,9 +90,7 @@ class EndpointHandler():
             else:  # Image/video part
                 if part.lower().startswith("video:"):
                     video_path = part.split("video:")[1].strip()
-                    print(f"Video path: {video_path}")
                     video_frames = self._extract_video_frames(video_path)
-                    print(f"Number of frames extracted: {len(video_frames) if video_frames else 0}")
                     if video_frames:
                         content.append({"type": "video", "video": video_frames, "fps": 1})
                 else:
@@ -124,59 +102,42 @@ class EndpointHandler():
     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.
         """
-        if image_data.startswith("http"):
-            try:
-                image = Image.open(requests.get(image_data, stream=True).raw)
-            except Exception as e:
-                logging.error(f"Error loading image from URL: {e}")
-                return None
-        elif image_data.startswith("data:image"):
-            try:
-                image_data = image_data.split(",")[1]
-                image_bytes = base64.b64decode(image_data)
-                image = Image.open(io.BytesIO(image_bytes))
-            except Exception as e:
-                logging.error(f"Error loading image from base64: {e}")
-                return None
-        else:
-            logging.error("Invalid image data format. Must be URL or base64 encoded.")
-            return None
-        return image
+        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, 
+            list or None: A list of PIL Images representing the extracted frames,
                           or None if extraction fails.
         """
         try:
-            print(f"Attempting to load video from: {video_path}")
-            video = VideoFileClip(video_path)
-            print(f"Video loaded: {video}")
-
-            frames = [
-                Image.fromarray(frame.astype('uint8'), 'RGB')
-                for frame in video.iter_frames(fps=fps)
-            ]
-            print(f"Number of frames: {len(frames)}")
-            print(f"Frame type: {type(frames[0]) if frames else None}")
-            print(f"Frame size: {frames[0].size if frames else None}")
-            video.close()
-            return frames
+            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:
-            error_message = f"Error extracting video frames: {e}\n{traceback.format_exc()}"
-            logging.error(error_message)  # Log the formatted error message
-            return None
+            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')