import gradio as gr import spaces from transformers import Qwen2VLForConditionalGeneration, AutoProcessor from qwen_vl_utils import process_vision_info import torch from PIL import Image import subprocess from datetime import datetime import numpy as np import os # subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) # models = { # "Qwen/Qwen2-VL-7B-Instruct": AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", trust_remote_code=True, torch_dtype="auto", _attn_implementation="flash_attention_2").cuda().eval() # } def array_to_image_path(image_array): if image_array is None: raise ValueError("No image provided. Please upload an image before submitting.") # Convert numpy array to PIL Image img = Image.fromarray(np.uint8(image_array)) # Generate a unique filename using timestamp timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") filename = f"image_{timestamp}.png" # Save the image img.save(filename) # Get the full path of the saved image full_path = os.path.abspath(filename) return full_path models = { "Qwen/Qwen2-VL-7B-Instruct": Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", trust_remote_code=True, torch_dtype="auto").cuda().eval() } processors = { "Qwen/Qwen2-VL-7B-Instruct": AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", trust_remote_code=True) } DESCRIPTION = "[Qwen2-VL-7B Demo](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)" kwargs = {} kwargs['torch_dtype'] = torch.bfloat16 user_prompt = '<|user|>\n' assistant_prompt = '<|assistant|>\n' prompt_suffix = "<|end|>\n" @spaces.GPU def run_example(image, text_input=None, model_id="Qwen/Qwen2-VL-7B-Instruct"): image_path = array_to_image_path(image) print(image_path) model = models[model_id] processor = processors[model_id] prompt = f"{user_prompt}<|image_1|>\n{text_input}{prompt_suffix}{assistant_prompt}" image = Image.fromarray(image).convert("RGB") messages = [ { "role": "user", "content": [ { "type": "image", "image": image_path, }, {"type": "text", "text": text_input}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Inference: Generation of the output generated_ids = model.generate(**inputs, max_new_tokens=1024) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) return output_text[0] css = """ #output { height: 500px; overflow: auto; border: 1px solid #ccc; } """ with gr.Blocks(css=css) as demo: gr.Markdown(DESCRIPTION) with gr.Tab(label="Qwen2-VL-7B Input"): with gr.Row(): with gr.Column(): input_img = gr.Image(label="Input Picture") model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="Qwen/Qwen2-VL-7B-Instruct") text_input = gr.Textbox(label="Question") submit_btn = gr.Button(value="Submit") with gr.Column(): output_text = gr.Textbox(label="Output Text") submit_btn.click(run_example, [input_img, text_input, model_selector], [output_text]) demo.queue(api_open=False) demo.launch(debug=True)