change main.py to qwenvl.py

app/main.py

@@ -1,101 +0,0 @@
-import torch
-from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
-from qwen_vl_utils import process_vision_info
-from appPublic.worker import awaitify
-from appPublic.jsonConfig import getConfig
-from ahserver.serverenv import ServerEnv
-from ahserver.webapp import webapp
-
-class Qwen2VLClass:
-	def __init__(self, modelname):
-		# default: Load the model on the available device(s)
-		self.model = Qwen2VLForConditionalGeneration.from_pretrained(
-			modelname,
-			torch_dtype=torch.bfloat16,
-			# attn_implementation="flash_attention_2",
-			device_map="auto"
-		)
-
-		# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
-		# model = Qwen2VLForConditionalGeneration.from_pretrained(
-		#     "Qwen/Qwen2-VL-7B-Instruct",
-		#     torch_dtype=torch.bfloat16,
-		#     attn_implementation="flash_attention_2",
-		#     device_map="auto",
-		# )
-
-		# default processer
-		self.processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
-
-		# The default range for the number of visual tokens per image in the model is 4-16384.
-		# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
-		# min_pixels = 256*28*28
-		# max_pixels = 1280*28*28
-		# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
-
-	def inference(self, prompt, image=None, videofile=None):
-		content = [
-			{
-				"type":"text",
-				"text":prompt
-			}
-		]
-		if image:
-			if not image.startswith('file:///') \
-						and not image.startswith('http://') \
-						and not image.startswith('https://'):
-				image = f'data:image;base64,{image}'
-			content.append({
-				"type":"image",
-				"image":image
-			})
-		if videofile:
-			if not videofile.startswith('file:///'):
-				return 'only local video file support'
-
-			content.append({
-				"type":"video",
-				"video":videofile
-			})
-		
-		messages = [
-			{
-				"role": "user",
-				"content": content
-			}
-		]
-
-		# Preparation for inference
-		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")
-
-		# Inference: Generation of the output
-		generated_ids = self.model.generate(**inputs, max_new_tokens=128)
-		generated_ids_trimmed = [
-			out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-		]
-		output_text = processor.batch_decode(
-			generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-		)
-		return output_text
-
-def main():
-	config = getConfig()
-	modelname = config.modelname
-	m = Qwen2VLClass(modelname)
-	g = ServerEnv()
-	g.inference = awaitify(m.inference)
-
-
-if __name__ == '__main__':
-	webapp(main)