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Update app.py
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app.py
CHANGED
@@ -17,7 +17,8 @@ import tiktoken
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import verovio
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model_name = "ucaslcl/GOT-OCR2_0"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModel.from_pretrained(model_name, trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True).eval().cuda()
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UPLOAD_FOLDER = "./uploads"
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RESULTS_FOLDER = "./results"
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@@ -32,7 +33,8 @@ def image_to_base64(image):
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return base64.b64encode(buffered.getvalue()).decode()
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q_model_name = "Qwen/Qwen2-VL-2B-Instruct"
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q_model = Qwen2VLForConditionalGeneration.from_pretrained(q_model_name, torch_dtype="auto").cuda().eval()
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q_processor = AutoProcessor.from_pretrained(q_model_name, trust_remote_code=True)
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def get_qwen_op(image_file, model, processor):
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@@ -53,7 +55,8 @@ def get_qwen_op(image_file, model, processor):
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}
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]
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text_prompt = q_processor.apply_chat_template(conversation, add_generation_prompt=True)
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inputs = q_processor(text=[text_prompt], images=[image], padding=True, return_tensors="pt").to("cuda")
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inputs = {k: v.to(torch.float32) if torch.is_floating_point(v) else v for k, v in inputs.items()}
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generation_config = {
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import verovio
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model_name = "ucaslcl/GOT-OCR2_0"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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#model = AutoModel.from_pretrained(model_name, trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True).eval().cuda()
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model = AutoModel.from_pretrained(model_name, trust_remote_code=True, low_cpu_mem_usage=True, device_map='cpu', use_safetensors=True).eval()
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UPLOAD_FOLDER = "./uploads"
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RESULTS_FOLDER = "./results"
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return base64.b64encode(buffered.getvalue()).decode()
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q_model_name = "Qwen/Qwen2-VL-2B-Instruct"
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#q_model = Qwen2VLForConditionalGeneration.from_pretrained(q_model_name, torch_dtype="auto").cuda().eval()
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q_model = Qwen2VLForConditionalGeneration.from_pretrained(q_model_name, torch_dtype="cpu").eval()
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q_processor = AutoProcessor.from_pretrained(q_model_name, trust_remote_code=True)
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def get_qwen_op(image_file, model, processor):
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}
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]
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text_prompt = q_processor.apply_chat_template(conversation, add_generation_prompt=True)
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#inputs = q_processor(text=[text_prompt], images=[image], padding=True, return_tensors="pt").to("cuda")
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inputs = q_processor(text=[text_prompt], images=[image], padding=True, return_tensors="pt")
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inputs = {k: v.to(torch.float32) if torch.is_floating_point(v) else v for k, v in inputs.items()}
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generation_config = {
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