import gradio as gr from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, VisionEncoderDecoderModel import torch torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg') torch.hub.download_url_to_file('https://huggingface.co/datasets/nielsr/textcaps-sample/resolve/main/stop_sign.png', 'stop_sign.png') torch.hub.download_url_to_file('https://cdn.openai.com/dall-e-2/demos/text2im/astronaut/horse/photo/0.jpg', 'astronaut.jpg') git_processor_base = AutoProcessor.from_pretrained("microsoft/git-base-coco") git_model_base = AutoModelForCausalLM.from_pretrained("microsoft/git-base-coco") git_processor_large = AutoProcessor.from_pretrained("microsoft/git-large-coco") git_model_large = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco") blip_processor_base = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") blip_model_base = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") blip_processor_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large") blip_model_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") vitgpt_processor = AutoImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning") vitgpt_model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") vitgpt_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") device = "cuda" if torch.cuda.is_available() else "cpu" git_model_base.to(device) blip_model_base.to(device) git_model_large.to(device) blip_model_large.to(device) vitgpt_model.to(device) def generate_caption(processor, model, image, tokenizer=None): inputs = processor(images=image, return_tensors="pt").to(device) generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50) if tokenizer is not None: generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] else: generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] return generated_caption def generate_captions(image): caption_git_base = generate_caption(git_processor_base, git_model_base, image) caption_git_large = generate_caption(git_processor_large, git_model_large, image) caption_blip_base = generate_caption(blip_processor_base, blip_model_base, image) caption_blip_large = generate_caption(blip_processor_large, blip_model_large, image) caption_vitgpt = generate_caption(vitgpt_processor, vitgpt_model, image, vitgpt_tokenizer) return caption_git_base, caption_git_large, caption_blip_base, caption_blip_large, caption_vitgpt #examples = [["cats.jpg"], ["stop_sign.png"], ["astronaut.jpg"]] outputs = [gr.outputs.Textbox(label="Caption generated by GIT-base"), gr.outputs.Textbox(label="Caption generated by GIT-large"), gr.outputs.Textbox(label="Caption generated by BLIP-base"), gr.outputs.Textbox(label="Caption generated by BLIP-large"), gr.outputs.Textbox(label="Caption generated by ViT+GPT-2")] title = "Interactive demo: comparing image captioning models" description = "Gradio Demo to compare GIT, BLIP and ViT+GPT2, 3 state-of-the-art vision+language models. To use it, simply upload your image and click 'submit', or click one of the examples to load them. Read more at the links below." article = "

BLIP docs | GIT docs

" iface = gr.Interface(fn=generate_captions, inputs=gr.inputs.Image(type="pil"), outputs=outputs, # examples=examples, title=title, description=description, article=article, enable_queue=True) iface.launch(server_name="0.0.0.0", server_port=7860) ''' import gradio as gr import numpy as np from PIL import Image def generate_ascii_art(image): try: # Convert the numpy array to a PIL Image img = Image.fromarray(np.uint8(image)) # Resize the image to a smaller size for faster processing img = img.resize((80, 60)) # Convert the image to grayscale img = img.convert("L") # Define ASCII characters to represent different intensity levels #ascii_chars = "@%#*+=-:. " ascii_chars = "$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/|()1{}[]?-_+~<>i!lI;:,\\^`'. " # Convert each pixel to ASCII character based on intensity ascii_image = "" for pixel_value in img.getdata(): ascii_image += ascii_chars[pixel_value // 25] # Reshape the ASCII string to match the resized image dimensions ascii_image = "\n".join([ascii_image[i:i + img.width] for i in range(0, len(ascii_image), img.width)]) return ascii_image except Exception as e: return f"Error: {e}" iface = gr.Interface( fn=generate_ascii_art, inputs="image", outputs="text", title="ASCII Art Generator", description="Upload an image, and this app will turn it into ASCII art! - Simple Gradio App from Docker", live=True ) iface.launch(server_name="0.0.0.0", server_port=7860) import gradio as gr import subprocess def run_command(command): try: result = subprocess.check_output(command, shell=True, text=True) return result except subprocess.CalledProcessError as e: return f"Error: {e}" iface = gr.Interface( fn=run_command, inputs="text", outputs="text", #live=True, title="Command Output Viewer", description="Enter a command and view its output.", examples=[ ["ls"], ["pwd"], ["echo 'Hello, Gradio!'"]] ) iface.launch(server_name="0.0.0.0", server_port=7860) '''