Delete model.py

model.py

@@ -1,107 +0,0 @@
-import torch
-from transformers import AutoModelForCausalLM, AutoProcessor, VisionEncoderDecoderModel
-from huggingface_hub import snapshot_download
-from qwen_vl_utils import process_vision_info
-
-def load_model(model_name):
-    """
-    Load the specified model and its processor based on the model name.
-    
-    Args:
-        model_name (str): Name of the model ("dots.ocr" or "Dolphin").
-    
-    Returns:
-        tuple: (model, processor) for the specified model.
-    """
-    if model_name == "dots.ocr":
-        model_id = "rednote-hilab/dots.ocr"
-        model_path = "./models/dots-ocr-local"
-        snapshot_download(
-            repo_id=model_id,
-            local_dir=model_path,
-            local_dir_use_symlinks=False,
-        )
-        model = AutoModelForCausalLM.from_pretrained(
-            model_path,
-            attn_implementation="flash_attention_2",
-            torch_dtype=torch.bfloat16,
-            device_map="auto",
-            trust_remote_code=True
-        )
-        processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
-    elif model_name == "Dolphin":
-        model_id = "ByteDance/Dolphin"
-        processor = AutoProcessor.from_pretrained(model_id)
-        model = VisionEncoderDecoderModel.from_pretrained(model_id)
-        model.eval()
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        model.to(device)
-        model = model.half()  # Use half precision
-    else:
-        raise ValueError(f"Unknown model: {model_name}")
-    return model, processor
-
-def inference_dots_ocr(model, processor, image, prompt, max_new_tokens):
-    """
-    Perform inference using the dots.ocr model.
-    
-    Args:
-        model: The loaded dots.ocr model.
-        processor: The corresponding processor.
-        image (PIL.Image): Input image.
-        prompt (str): Prompt for inference.
-        max_new_tokens (int): Maximum number of tokens to generate.
-    
-    Returns:
-        str: Generated text output.
-    """
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {"type": "image", "image": image},
-                {"type": "text", "text": prompt}
-            ]
-        }
-    ]
-    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    image_inputs, video_inputs = process_vision_info(messages)
-    inputs = processor(
-        text=[text],
-        images=image_inputs,
-        videos=video_inputs,
-        padding=True,
-        return_tensors="pt",
-    )
-    inputs = inputs.to(model.device)
-    with torch.no_grad():
-        generated_ids = model.generate(
-            **inputs,
-            max_new_tokens=max_new_tokens,
-            do_sample=False,
-            temperature=0.1
-        )
-    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[0] if output_text else ""
-
-def inference_dolphin(model, processor, image):
-    """
-    Perform inference using the Dolphin model.
-    
-    Args:
-        model: The loaded Dolphin model.
-        processor: The corresponding processor.
-        image (PIL.Image): Input image.
-    
-    Returns:
-        str: Generated text output.
-    """
-    pixel_values = processor(image, return_tensors="pt").pixel_values.to(model.device).half()
-    generated_ids = model.generate(pixel_values)
-    generated_text = processor.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-    return generated_text