import gradio as gr import subprocess import torch from PIL import Image from transformers import AutoProcessor, AutoModelForCausalLM # Attempt to install flash-attn try: subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, check=True, shell=True) except subprocess.CalledProcessError as e: print(f"Error installing flash-attn: {e}") print("Continuing without flash-attn.") # Determine the device to use device = "cuda" if torch.cuda.is_available() else "cpu" # Load the base model and processor try: vision_language_model_base = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval() vision_language_processor_base = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True) except Exception as e: print(f"Error loading base model: {e}") vision_language_model_base = None vision_language_processor_base = None # Load the large model and processor try: vision_language_model_large = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True).to(device).eval() vision_language_processor_large = AutoProcessor.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True) except Exception as e: print(f"Error loading large model: {e}") vision_language_model_large = None vision_language_processor_large = None def describe_image(uploaded_image, model_choice): """ Generates a detailed description of the input image using the selected model. Args: uploaded_image (PIL.Image.Image): The image to describe. model_choice (str): The model to use, either "Base" or "Large". Returns: str: A detailed textual description of the image or an error message. """ if uploaded_image is None: return "Please upload an image." if model_choice == "Base": if vision_language_model_base is None: return "Base model failed to load." model = vision_language_model_base processor = vision_language_processor_base elif model_choice == "Large": if vision_language_model_large is None: return "Large model failed to load." model = vision_language_model_large processor = vision_language_processor_large else: return "Invalid model choice." if not isinstance(uploaded_image, Image.Image): uploaded_image = Image.fromarray(uploaded_image) inputs = processor(text="", images=uploaded_image, return_tensors="pt").to(device) with torch.no_grad(): 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] processed_description = processor.post_process_generation( generated_text, task="", image_size=(uploaded_image.width, uploaded_image.height) ) image_description = processed_description[""] print("\nImage description generated!:", image_description) return image_description # Description for the interface description = "Select the model to use for generating the image description. 'Base' is smaller and faster, while 'Large' is more accurate but slower." if device == "cpu": description += " Note: Running on CPU, which may be slow for large models." # Create the Gradio interface image_description_interface = gr.Interface( fn=describe_image, inputs=[ gr.Image(label="Upload Image", type="pil"), gr.Radio(["Base", "Large"], label="Model Choice", value="Base") ], outputs=gr.Textbox(label="Generated Caption", lines=4, show_copy_button=True), live=False, title="# **[Florence-2 Models Image Captions](https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0)**", theme=="bethecloud/storj_theme", description=description ) # Launch the interface image_description_interface.launch(debug=True, ssr_mode=False)