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| import gradio as gr | |
| import re | |
| import torch | |
| from PIL import Image | |
| from transformers import FuyuForCausalLM, FuyuProcessor | |
| model_id = "adept/fuyu-8b" | |
| dtype = torch.bfloat16 | |
| device_map = { | |
| "language_model.model.embed_tokens": "cpu", | |
| "language_model.model.layers": 0, | |
| "language_model.model.final_layernorm": 0, | |
| "language_model.lm_head": "cpu", | |
| "vision_embed_tokens": "cpu", | |
| } | |
| model = FuyuForCausalLM.from_pretrained(model_id, device_map=device_map, torch_dtype=dtype) | |
| processor = FuyuProcessor.from_pretrained(model_id) | |
| CAPTION_PROMPT = "Generate a coco-style caption.\n" | |
| DETAILED_CAPTION_PROMPT = "What is happening in this image?" | |
| def resize_to_max(image, max_width=1920, max_height=1080): | |
| width, height = image.size | |
| if width <= max_width and height <= max_height: | |
| return image | |
| scale = min(max_width/width, max_height/height) | |
| width = int(width*scale) | |
| height = int(height*scale) | |
| return image.resize((width, height), Image.LANCZOS) | |
| def pad_to_size(image, canvas_width=1920, canvas_height=1080): | |
| width, height = image.size | |
| if width >= canvas_width and height >= canvas_height: | |
| return image | |
| # Paste at (0, 0) | |
| canvas = Image.new("RGB", (canvas_width, canvas_height)) | |
| canvas.paste(image) | |
| return canvas | |
| def predict(image, prompt): | |
| # image = image.convert('RGB') | |
| model_inputs = processor(text=prompt, images=[image]) | |
| generation_output = model.generate(**model_inputs, max_new_tokens=50) | |
| prompt_len = model_inputs["input_ids"].shape[-1] | |
| return processor.decode(generation_output[0][prompt_len:], skip_special_tokens=True) | |
| def caption(image, detailed_captioning): | |
| if detailed_captioning: | |
| caption_prompt = DETAILED_CAPTION_PROMPT | |
| else: | |
| caption_prompt = CAPTION_PROMPT | |
| return predict(image, caption_prompt).lstrip() | |
| def set_example_image(example: list) -> dict: | |
| return gr.Image.update(value=example[0]) | |
| def coords_from_response(response): | |
| # y1, x1, y2, x2 | |
| pattern = r"<box>(\d+),\s*(\d+),\s*(\d+),\s*(\d+)</box>" | |
| match = re.search(pattern, response) | |
| if match: | |
| # Unpack and change order | |
| y1, x1, y2, x2 = [int(coord) for coord in match.groups()] | |
| return (x1, y1, x2, y2) | |
| else: | |
| gr.Error("The string is malformed or does not match the expected pattern.") | |
| def localize(image, query): | |
| prompt = f"When presented with a box, perform OCR to extract text contained within it. If provided with text, generate the corresponding bounding box.\n{query}" | |
| # Downscale and/or pad to 1920x1080 | |
| padded = resize_to_max(image) | |
| padded = pad_to_size(padded) | |
| model_inputs = processor(text=prompt, images=[padded]) | |
| outputs = model.generate(**model_inputs, max_new_tokens=40) | |
| post_processed_bbox_tokens = processor.post_process_box_coordinates(outputs)[0] | |
| decoded = processor.decode(post_processed_bbox_tokens, skip_special_tokens=True) | |
| decoded = decoded.split('\x04', 1)[1] if '\x04' in decoded else '' | |
| coords = coords_from_response(decoded) | |
| return image, [(coords, f"Location of \"{query}\"")] | |
| css = """ | |
| #mkd { | |
| height: 500px; | |
| overflow: auto; | |
| border: 1px solid #ccc; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| gr.HTML( | |
| """ | |
| <h1 id="title">Fuyu Multimodal Demo</h1> | |
| <h3><a href="https://hf.co/adept/fuyu-8b">Fuyu-8B</a> is a multimodal model that supports a variety of tasks combining text and image prompts.</h3> | |
| For example, you can use it for captioning by asking it to describe an image. You can also ask it questions about an image, a task known as Visual Question Answering, or VQA. This demo lets you explore captioning and VQA, with more tasks coming soon :) | |
| Learn more about the model in <a href="https://www.adept.ai/blog/fuyu-8b">our blog post</a>. | |
| <br> | |
| <br> | |
| <strong>Note: This is a raw model release. We have not added further instruction-tuning, postprocessing or sampling strategies to control for undesirable outputs. The model may hallucinate, and you should expect to have to fine-tune the model for your use-case!</strong> | |
| <h3>Play with Fuyu-8B in this demo! 💬</h3> | |
| """ | |
| ) | |
| with gr.Tab("Visual Question Answering"): | |
| gr.Markdown( | |
| """ | |
| You can use natural-language questions to ask about the image. However, since this is a base model not fine-tuned for \ | |
| chat instructions, you may get better results by following a prompt format similar to the one used during training. See the \ | |
| examples below for details! | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_input = gr.Image(label="Upload your Image", type="pil") | |
| text_input = gr.Textbox(label="Ask a Question") | |
| vqa_output = gr.Textbox(label="Output") | |
| vqa_btn = gr.Button("Answer Visual Question") | |
| gr.Examples( | |
| [ | |
| ["assets/vqa_example_1.png", "What's the name of this dessert, and how is it made?\n"], | |
| ["assets/vqa_example_2.png", "What is this flower and where is it's origin?"], | |
| ["assets/food.png", "Answer the following VQAv2 question based on the image.\nWhat type of foods are in the image?"], | |
| ["assets/jobs.png", "Answer the following DocVQA question based on the image.\nWhich is the metro in California that has a good job Outlook?"], | |
| ["assets/docvqa_example.png", "How many items are sold?"], | |
| ["assets/screen2words_ui_example.png", "What is this app about?"], | |
| ], | |
| inputs = [image_input, text_input], | |
| outputs = [vqa_output], | |
| fn=predict, | |
| cache_examples=True, | |
| label='Click on any Examples below to get VQA results quickly 👇' | |
| ) | |
| with gr.Tab("Image Captioning"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| captioning_input = gr.Image(label="Upload your Image", type="pil") | |
| detailed_captioning_checkbox = gr.Checkbox(label="Enable detailed captioning") | |
| captioning_output = gr.Textbox(label="Output") | |
| captioning_btn = gr.Button("Generate Caption") | |
| gr.Examples( | |
| [["assets/captioning_example_1.png", False], ["assets/girl_hat.png", True]], | |
| inputs = [captioning_input, detailed_captioning_checkbox], | |
| outputs = [captioning_output], | |
| fn=caption, | |
| cache_examples=True, | |
| label='Click on any Examples below to get captioning results quickly 👇' | |
| ) | |
| captioning_btn.click(fn=caption, inputs=[captioning_input, detailed_captioning_checkbox], outputs=captioning_output) | |
| vqa_btn.click(fn=predict, inputs=[image_input, text_input], outputs=vqa_output) | |
| with gr.Tab("Find Text in Screenshots"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| localization_input = gr.Image(label="Upload your Image", type="pil") | |
| query_input = gr.Textbox(label="Text to find") | |
| localization_btn = gr.Button("Locate Text") | |
| with gr.Column(): | |
| with gr.Row(height=800): | |
| localization_output = gr.AnnotatedImage(label="Text Position") | |
| gr.Examples( | |
| [["assets/localization_example_1.jpeg", "Share your repair"], | |
| ["assets/screen2words_ui_example.png", "statistics"]], | |
| inputs = [localization_input, query_input], | |
| outputs = [localization_output], | |
| fn=localize, | |
| cache_examples=True, | |
| label='Click on any Examples below to get localization results quickly 👇' | |
| ) | |
| localization_btn.click(fn=localize, inputs=[localization_input, query_input], outputs=localization_output) | |
| demo.launch(server_name="0.0.0.0") |