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| import gradio as gr | |
| from transformers import AutoProcessor, AutoModelForCausalLM | |
| # import peft | |
| import spaces | |
| import requests | |
| import copy | |
| from PIL import Image, ImageDraw, ImageFont | |
| import io | |
| import matplotlib.pyplot as plt | |
| import matplotlib.patches as patches | |
| import random | |
| import numpy as np | |
| import subprocess | |
| subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) | |
| models = { | |
| 'microsoft/Florence-2-large-ft': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large-ft', trust_remote_code=True).to("cuda").eval(), | |
| 'dwb2023/florence2-large-bccd-base-ft': AutoModelForCausalLM.from_pretrained('dwb2023/florence2-large-bccd-base-ft', trust_remote_code=True).to("cuda").eval(), | |
| } | |
| processors = { | |
| 'microsoft/Florence-2-large-ft': AutoProcessor.from_pretrained('microsoft/Florence-2-large-ft', trust_remote_code=True), | |
| 'dwb2023/florence2-large-bccd-base-ft': AutoProcessor.from_pretrained('microsoft/Florence-2-large-ft', trust_remote_code=True), | |
| } | |
| colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red', | |
| 'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue'] | |
| def fig_to_pil(fig): | |
| buf = io.BytesIO() | |
| fig.savefig(buf, format='png') | |
| buf.seek(0) | |
| return Image.open(buf) | |
| # spaces.GPU | |
| def run_example(task_prompt, image, text_input=None, model_id='microsoft/Florence-2-large'): | |
| model = models[model_id] | |
| processor = processors[model_id] | |
| if text_input is None: | |
| prompt = task_prompt | |
| else: | |
| prompt = task_prompt + text_input | |
| inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda") | |
| 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 parsed_answer | |
| def plot_bbox(image, data): | |
| fig, ax = plt.subplots() | |
| ax.imshow(image) | |
| for bbox, label in zip(data['bboxes'], data['labels']): | |
| x1, y1, x2, y2 = bbox | |
| rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none') | |
| ax.add_patch(rect) | |
| plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5)) | |
| ax.axis('off') | |
| return fig | |
| def draw_polygons(image, prediction, fill_mask=False): | |
| draw = ImageDraw.Draw(image) | |
| scale = 1 | |
| for polygons, label in zip(prediction['polygons'], prediction['labels']): | |
| color = random.choice(colormap) | |
| fill_color = random.choice(colormap) if fill_mask else None | |
| for _polygon in polygons: | |
| _polygon = np.array(_polygon).reshape(-1, 2) | |
| if len(_polygon) < 3: | |
| print('Invalid polygon:', _polygon) | |
| continue | |
| _polygon = (_polygon * scale).reshape(-1).tolist() | |
| if fill_mask: | |
| draw.polygon(_polygon, outline=color, fill=fill_color) | |
| else: | |
| draw.polygon(_polygon, outline=color) | |
| draw.text((_polygon[0] + 8, _polygon[1] + 2), label, fill=color) | |
| return image | |
| def convert_to_od_format(data): | |
| bboxes = data.get('bboxes', []) | |
| labels = data.get('bboxes_labels', []) | |
| od_results = { | |
| 'bboxes': bboxes, | |
| 'labels': labels | |
| } | |
| return od_results | |
| def draw_ocr_bboxes(image, prediction): | |
| scale = 1 | |
| draw = ImageDraw.Draw(image) | |
| bboxes, labels = prediction['quad_boxes'], prediction['labels'] | |
| for box, label in zip(bboxes, labels): | |
| color = random.choice(colormap) | |
| new_box = (np.array(box) * scale).tolist() | |
| draw.polygon(new_box, width=3, outline=color) | |
| draw.text((new_box[0]+8, new_box[1]+2), | |
| "{}".format(label), | |
| align="right", | |
| fill=color) | |
| return image | |
| def process_image(image, task_prompt, text_input=None, model_id='dwb2023/florence2-large-bccd-base-ft'): | |
| image = Image.fromarray(image) # Convert NumPy array to PIL Image | |
| if task_prompt == 'Object Detection': | |
| task_prompt = '<OD>' | |
| results = run_example(task_prompt, image, model_id=model_id) | |
| fig = plot_bbox(image, results['<OD>']) | |
| return results, fig_to_pil(fig) | |
| else: | |
| return "", None # Return empty string and None for unknown task prompts | |
| single_task_list =[ | |
| 'Object Detection' | |
| ] | |
| with gr.Blocks(theme="sudeepshouche/minimalist") as demo: | |
| gr.Markdown("## 🧬OmniScience - fine tuned VLM models for use in function calling 🔧") | |
| gr.Markdown("- 🔬Florence-2 Model Proof of Concept, focusing on Object Detection <OD> tasks.") | |
| gr.Markdown("- Fine-tuned for 🩸Blood Cell Detection using the [Roboflow BCCD dataset](https://universe.roboflow.com/roboflow-100/bccd-ouzjz/dataset/2), this model can detect blood cells and types in images.") | |
| gr.Markdown("") | |
| gr.Markdown("BCCD Datasets on Hugging Face:") | |
| gr.Markdown("- [🌺Florence 2](https://huggingface.co/datasets/dwb2023/roboflow100-bccd-florence2/viewer/default/test?q=BloodImage_00038_jpg.rf.1b0ce1635e11b3b49302de527c86bb02.jpg), [💎PaliGemma](https://huggingface.co/datasets/dwb2023/roboflow-bccd-paligemma/viewer/default/test?q=BloodImage_00038_jpg.rf.1b0ce1635e11b3b49302de527c86bb02.jpg)") | |
| with gr.Tab(label="Florence-2 Object Detection"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_img = gr.Image(label="Input Picture") | |
| model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value='microsoft/Florence-2-large-ft') | |
| task_prompt = gr.Dropdown(choices=single_task_list, label="Task Prompt", value="Object Detection") | |
| text_input = gr.Textbox(label="Text Input", placeholder="Not used for Florence-2 Object Detection") | |
| submit_btn = gr.Button(value="Submit") | |
| with gr.Column(): | |
| output_text = gr.Textbox(label="Output Text") | |
| output_img = gr.Image(label="Output Image") | |
| gr.Examples( | |
| examples=[ | |
| ["examples/bccd-test/BloodImage_00038_jpg.rf.1b0ce1635e11b3b49302de527c86bb02.jpg", 'Object Detection'], | |
| ["examples/bccd-test/BloodImage_00044_jpg.rf.1c44102fcdf64fd178f1f16bb988d5cf.jpg", 'Object Detection'], | |
| ["examples/bccd-test/BloodImage_00062_jpg.rf.fbed5373cd2e0e732092ed5c7b28aa19.jpg", 'Object Detection'], | |
| ["examples/bccd-test/BloodImage_00090_jpg.rf.7e3d419774b20ef93d4ec6c4be8f64df.jpg", 'Object Detection'], | |
| ["examples/bccd-test/BloodImage_00099_jpg.rf.0a65e56401cdd71253e7bc04917c3558.jpg", 'Object Detection'], | |
| ["examples/bccd-test/BloodImage_00112_jpg.rf.6b8d185de08e65c6d765c824bb76ec68.jpg", 'Object Detection'], | |
| ["examples/bccd-test/BloodImage_00113_jpg.rf.ab69dfaa52c1b3249cf44fa66afbb619.jpg", 'Object Detection'], | |
| ["examples/bccd-test/BloodImage_00120_jpg.rf.4a2f84ca3564ef453b12ceb9c852e32e.jpg", 'Object Detection'], | |
| ], | |
| inputs=[input_img, task_prompt], | |
| outputs=[output_text, output_img], | |
| fn=process_image, | |
| cache_examples=True, | |
| label='Try examples' | |
| ) | |
| submit_btn.click(process_image, [input_img, task_prompt, model_selector], [output_text, output_img]) | |
| gr.Markdown("## 🚀Other Cool Stuff:") | |
| gr.Markdown("- [Florence 2 Whitepaper](https://arxiv.org/pdf/2311.06242) - how I found out about the Roboflow 100 and the BCCD dataset.") | |
| gr.Markdown("- [Roboflow YouTube Video on Florence 2 fine-tuning](https://youtu.be/i3KjYgxNH6w?si=x1ZMg9hsNe25Y19-&t=1296) - bookmarked an 🧠insightful trade-off analysis of various VLMs.") | |
| gr.Markdown("- [Landing AI - Vision Agent](https://va.landing.ai/) - 🌟just pure WOW. bringing agentic planning into solutions architecture.") | |
| gr.Markdown("- [OmniScience fork of Landing AI repo](https://huggingface.co/spaces/dwb2023/omniscience) - I had a lot of fun with this one... some great 🔍reverse engineering enabled by W&B's Weave📊.") | |
| gr.Markdown("- [Scooby Snacks🐕 - microservice based function calling with style](https://huggingface.co/spaces/dwb2023/blackbird-app) - Leveraging 🤖Claude Sonnet 3.5 to orchestrate Microservice-Based Function Calling.") | |
| demo.launch(debug=True) | |