Delete src/model.py

src/model.py

@@ -1,60 +0,0 @@
-# Importing necessary libraries
-import gradio as gr
-import subprocess
-import spaces
-from transformers import AutoProcessor, AutoModelForCausalLM
-
-
-# Install the required dependencies
-subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
-
-# Load model and processor from Hugging Face
-model_id = "microsoft/Florence-2-large-ft"
-model = (
-    AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda").eval()
-)
-processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
-
-
-@spaces.GPU(duration=120)
-def run_example(task_prompt, image, text_input=None):
-    """
-    Runs an example using the given task prompt and image.
-
-    Args:
-        - task_prompt (str): The task prompt for the example.
-        - image (PIL.Image.Image): The image to be processed.
-        - text_input (str, optional): Additional text input to be appended to the task prompt. Defaults to None.
-
-    Returns:
-        str: The parsed answer generated by the model.
-    """
-    # Check if the image is provided
-    if not image:
-        raise gr.Error("No image provided")
-        
-    # If there is no text input, use the task prompt as the prompt
-    if text_input is None:
-        prompt = task_prompt
-    else:
-        prompt = task_prompt + text_input
-
-    # Process the image and text input
-    inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
-
-    # Generate the answer using the model
-    generated_ids = model.generate(
-        input_ids=inputs["input_ids"],
-        pixel_values=inputs["pixel_values"],
-        max_new_tokens=1024,
-        early_stopping=False,
-        do_sample=False,
-        num_beams=3,
-    )
-    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
-    parsed_answer = processor.post_process_generation(
-        generated_text, task=task_prompt, image_size=(image.width, image.height)
-    )
-
-    # Return the parsed answer
-    return parsed_answer