import sys import importlib.util from unittest.mock import MagicMock # Create a fake spec object class FakeFlashAttnSpec: name = 'flash_attn' loader = None origin = None submodule_search_locations = [] fake_spec = FakeFlashAttnSpec() # Create mock modules with proper __spec__ attributes flash_attn_mock = MagicMock() flash_attn_mock.__spec__ = fake_spec flash_attn_mock.__version__ = "0.0.0" # Force version check to fail sys.modules['flash_attn'] = flash_attn_mock sys.modules['flash_attn.flash_attn_interface'] = MagicMock() sys.modules['flash_attn.bert_padding'] = MagicMock() # Patch find_spec to return our fake spec _original_find_spec = importlib.util.find_spec def _patched_find_spec(name, package=None): if name == 'flash_attn' or name.startswith('flash_attn.'): return fake_spec return _original_find_spec(name, package) importlib.util.find_spec = _patched_find_spec # NOW import everything else import gradio as gr from transformers import AutoProcessor, AutoModelForCausalLM 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 models = { 'microsoft/Florence-2-large-ft': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large-ft', trust_remote_code=True, attn_implementation="sdpa", revision="3112cd2e25c969cfdcb600a01489c56737d943d3" ).to("cuda").eval(), 'microsoft/Florence-2-large': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True, attn_implementation="sdpa", revision="00d2f1570b00c6dea5df998f5635db96840436bc" ).to("cuda").eval(), 'microsoft/Florence-2-base-ft': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base-ft', trust_remote_code=True, attn_implementation="sdpa", revision="9803f52844ec1ae5df004e6089262e9a23e527fd" ).to("cuda").eval(), 'microsoft/Florence-2-base': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True, attn_implementation="sdpa", revision="ceaf371f01ef66192264811b390bccad475a4f02" ).to("cuda").eval(), } processors = { 'microsoft/Florence-2-large-ft': AutoProcessor.from_pretrained('microsoft/Florence-2-large-ft', attn_implementation="sdpa", revision="3112cd2e25c969cfdcb600a01489c56737d943d3", trust_remote_code=True), 'microsoft/Florence-2-large': AutoProcessor.from_pretrained('microsoft/Florence-2-large', attn_implementation="sdpa", revision="00d2f1570b00c6dea5df998f5635db96840436bc", trust_remote_code=True), 'microsoft/Florence-2-base-ft': AutoProcessor.from_pretrained('microsoft/Florence-2-base-ft', attn_implementation="sdpa", revision="9803f52844ec1ae5df004e6089262e9a23e527fd", trust_remote_code=True), 'microsoft/Florence-2-base': AutoProcessor.from_pretrained('microsoft/Florence-2-base', attn_implementation="sdpa", revision="ceaf371f01ef66192264811b390bccad475a4f02", trust_remote_code=True), } DESCRIPTION = "# [Florence-2 Demo](https://huggingface.co/microsoft/Florence-2-large) | [Compare Outputs](https://dualview.ai)" 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='microsoft/Florence-2-large'): image = Image.fromarray(image) # Convert NumPy array to PIL Image if task_prompt == 'Caption': task_prompt = '' results = run_example(task_prompt, image, model_id=model_id) return results, None elif task_prompt == 'Detailed Caption': task_prompt = '' results = run_example(task_prompt, image, model_id=model_id) return results, None elif task_prompt == 'More Detailed Caption': task_prompt = '' results = run_example(task_prompt, image, model_id=model_id) return results, None elif task_prompt == 'Caption + Grounding': task_prompt = '' results = run_example(task_prompt, image, model_id=model_id) text_input = results[task_prompt] task_prompt = '' results = run_example(task_prompt, image, text_input, model_id) results[''] = text_input fig = plot_bbox(image, results['']) return results, fig_to_pil(fig) elif task_prompt == 'Detailed Caption + Grounding': task_prompt = '' results = run_example(task_prompt, image, model_id=model_id) text_input = results[task_prompt] task_prompt = '' results = run_example(task_prompt, image, text_input, model_id) results[''] = text_input fig = plot_bbox(image, results['']) return results, fig_to_pil(fig) elif task_prompt == 'More Detailed Caption + Grounding': task_prompt = '' results = run_example(task_prompt, image, model_id=model_id) text_input = results[task_prompt] task_prompt = '' results = run_example(task_prompt, image, text_input, model_id) results[''] = text_input fig = plot_bbox(image, results['']) return results, fig_to_pil(fig) elif task_prompt == 'Object Detection': task_prompt = '' results = run_example(task_prompt, image, model_id=model_id) fig = plot_bbox(image, results['']) return results, fig_to_pil(fig) elif task_prompt == 'Dense Region Caption': task_prompt = '' results = run_example(task_prompt, image, model_id=model_id) fig = plot_bbox(image, results['']) return results, fig_to_pil(fig) elif task_prompt == 'Region Proposal': task_prompt = '' results = run_example(task_prompt, image, model_id=model_id) fig = plot_bbox(image, results['']) return results, fig_to_pil(fig) elif task_prompt == 'Caption to Phrase Grounding': task_prompt = '' results = run_example(task_prompt, image, text_input, model_id) fig = plot_bbox(image, results['']) return results, fig_to_pil(fig) elif task_prompt == 'Referring Expression Segmentation': task_prompt = '' results = run_example(task_prompt, image, text_input, model_id) output_image = copy.deepcopy(image) output_image = draw_polygons(output_image, results[''], fill_mask=True) return results, output_image elif task_prompt == 'Region to Segmentation': task_prompt = '' results = run_example(task_prompt, image, text_input, model_id) output_image = copy.deepcopy(image) output_image = draw_polygons(output_image, results[''], fill_mask=True) return results, output_image elif task_prompt == 'Open Vocabulary Detection': task_prompt = '' results = run_example(task_prompt, image, text_input, model_id) bbox_results = convert_to_od_format(results['']) fig = plot_bbox(image, bbox_results) return results, fig_to_pil(fig) elif task_prompt == 'Region to Category': task_prompt = '' results = run_example(task_prompt, image, text_input, model_id) return results, None elif task_prompt == 'Region to Description': task_prompt = '' results = run_example(task_prompt, image, text_input, model_id) return results, None elif task_prompt == 'OCR': task_prompt = '' results = run_example(task_prompt, image, model_id=model_id) return results, None elif task_prompt == 'OCR with Region': task_prompt = '' results = run_example(task_prompt, image, model_id=model_id) output_image = copy.deepcopy(image) output_image = draw_ocr_bboxes(output_image, results['']) return results, output_image else: return "", None # Return empty string and None for unknown task prompts css = """ #output { height: 500px; overflow: auto; border: 1px solid #ccc; } """ single_task_list =[ 'Caption', 'Detailed Caption', 'More Detailed Caption', 'Object Detection', 'Dense Region Caption', 'Region Proposal', 'Caption to Phrase Grounding', 'Referring Expression Segmentation', 'Region to Segmentation', 'Open Vocabulary Detection', 'Region to Category', 'Region to Description', 'OCR', 'OCR with Region' ] cascased_task_list =[ 'Caption + Grounding', 'Detailed Caption + Grounding', 'More Detailed Caption + Grounding' ] def update_task_dropdown(choice): if choice == 'Cascased task': return gr.Dropdown(choices=cascased_task_list, value='Caption + Grounding') else: return gr.Dropdown(choices=single_task_list, value='Caption') with gr.Blocks(css=css) as demo: gr.Markdown(DESCRIPTION) with gr.Tab(label="Florence-2 Image Captioning"): 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') task_type = gr.Radio(choices=['Single task', 'Cascased task'], label='Task type selector', value='Single task') task_prompt = gr.Dropdown(choices=single_task_list, label="Task Prompt", value="Caption") task_type.change(fn=update_task_dropdown, inputs=task_type, outputs=task_prompt) text_input = gr.Textbox(label="Text Input (optional)") 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=[ ["image1.jpg", 'Object Detection'], ["image2.jpg", 'OCR with Region'] ], 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, text_input, model_selector], [output_text, output_img]) demo.launch(debug=True)