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| # import gradio as gr | |
| # import spaces | |
| # from transformers import Qwen2VLForConditionalGeneration, AutoProcessor | |
| # from qwen_vl_utils import process_vision_info | |
| # from PIL import Image | |
| # from datetime import datetime | |
| # import os | |
| # # subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) | |
| # DESCRIPTION = "[Sparrow Qwen2-VL-7B Backend](https://github.com/katanaml/sparrow)" | |
| # def array_to_image_path(image_filepath, max_width=1250, max_height=1750): | |
| # if image_filepath is None: | |
| # raise ValueError("No image provided. Please upload an image before submitting.") | |
| # # Open the uploaded image using its filepath | |
| # img = Image.open(image_filepath) | |
| # # Extract the file extension from the uploaded file | |
| # input_image_extension = image_filepath.split('.')[-1].lower() # Extract extension from filepath | |
| # # Set file extension based on the original file, otherwise default to PNG | |
| # if input_image_extension in ['jpg', 'jpeg', 'png']: | |
| # file_extension = input_image_extension | |
| # else: | |
| # file_extension = 'png' # Default to PNG if extension is unavailable or invalid | |
| # # Get the current dimensions of the image | |
| # width, height = img.size | |
| # # Initialize new dimensions to current size | |
| # new_width, new_height = width, height | |
| # # Check if the image exceeds the maximum dimensions | |
| # if width > max_width or height > max_height: | |
| # # Calculate the new size, maintaining the aspect ratio | |
| # aspect_ratio = width / height | |
| # if width > max_width: | |
| # new_width = max_width | |
| # new_height = int(new_width / aspect_ratio) | |
| # if new_height > max_height: | |
| # new_height = max_height | |
| # new_width = int(new_height * aspect_ratio) | |
| # # Generate a unique filename using timestamp | |
| # timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") | |
| # filename = f"image_{timestamp}.{file_extension}" | |
| # # Save the image | |
| # img.save(filename) | |
| # # Get the full path of the saved image | |
| # full_path = os.path.abspath(filename) | |
| # return full_path, new_width, new_height | |
| # # Initialize the model and processor globally to optimize performance | |
| # model = Qwen2VLForConditionalGeneration.from_pretrained( | |
| # "Qwen/Qwen2-VL-7B-Instruct", | |
| # torch_dtype="auto", | |
| # device_map="auto" | |
| # ) | |
| # processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct") | |
| # @spaces.GPU | |
| # def run_inference(input_imgs, text_input): | |
| # results = [] | |
| # for image in input_imgs: | |
| # # Convert each image to the required format | |
| # image_path, width, height = array_to_image_path(image) | |
| # try: | |
| # # Prepare messages for each image | |
| # messages = [ | |
| # { | |
| # "role": "user", | |
| # "content": [ | |
| # { | |
| # "type": "image", | |
| # "image": image_path, | |
| # "resized_height": height, | |
| # "resized_width": width | |
| # }, | |
| # { | |
| # "type": "text", | |
| # "text": text_input | |
| # } | |
| # ] | |
| # } | |
| # ] | |
| # # Prepare inputs for the model | |
| # 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") | |
| # # Generate inference output | |
| # generated_ids = model.generate(**inputs, max_new_tokens=4096) | |
| # generated_ids_trimmed = [ | |
| # out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) | |
| # ] | |
| # raw_output = processor.batch_decode( | |
| # generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=True | |
| # ) | |
| # results.append(raw_output[0]) | |
| # print("Processed: " + image) | |
| # finally: | |
| # # Clean up the temporary image file | |
| # os.remove(image_path) | |
| # return results | |
| # 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_imgs = gr.Files(file_types=["image"], label="Upload Document Images") | |
| # text_input = gr.Textbox(label="Query") | |
| # submit_btn = gr.Button(value="Submit", variant="primary") | |
| # with gr.Column(): | |
| # output_text = gr.Textbox(label="Response") | |
| # submit_btn.click(run_inference, [input_imgs, text_input], [output_text]) | |
| # demo.queue(api_open=True) | |
| # demo.launch(debug=True) | |
| import gradio as gr | |
| import spaces | |
| from transformers import Qwen2VLForConditionalGeneration, AutoProcessor | |
| from qwen_vl_utils import process_vision_info | |
| from PIL import Image | |
| from datetime import datetime | |
| import os | |
| DESCRIPTION = "[Sparrow Qwen2-VL-7B Backend](https://github.com/katanaml/sparrow)" | |
| # ============================================================================ | |
| # IMPORTANT: NE PAS charger le modèle ici (scope global) | |
| # Le modèle doit être chargé UNIQUEMENT dans la fonction @spaces.GPU | |
| # ============================================================================ | |
| # Variables globales pour le cache (sans charger le modèle) | |
| _model = None | |
| _processor = None | |
| def array_to_image_path(image_filepath, max_width=1250, max_height=1750): | |
| if image_filepath is None: | |
| raise ValueError("No image provided. Please upload an image before submitting.") | |
| img = Image.open(image_filepath) | |
| input_image_extension = image_filepath.split('.')[-1].lower() | |
| if input_image_extension in ['jpg', 'jpeg', 'png']: | |
| file_extension = input_image_extension | |
| else: | |
| file_extension = 'png' | |
| width, height = img.size | |
| new_width, new_height = width, height | |
| if width > max_width or height > max_height: | |
| aspect_ratio = width / height | |
| if width > max_width: | |
| new_width = max_width | |
| new_height = int(new_width / aspect_ratio) | |
| if new_height > max_height: | |
| new_height = max_height | |
| new_width = int(new_height * aspect_ratio) | |
| timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") | |
| filename = f"image_{timestamp}.{file_extension}" | |
| img.save(filename) | |
| full_path = os.path.abspath(filename) | |
| return full_path, new_width, new_height | |
| def run_inference(input_imgs, text_input): | |
| """ | |
| CORRECTION CRITIQUE: Le modèle est chargé ICI, pas dans le scope global | |
| """ | |
| global _model, _processor | |
| # Charger le modèle uniquement la première fois (lazy loading) | |
| if _model is None or _processor is None: | |
| print("🔄 Chargement du modèle Qwen2-VL-7B-Instruct...") | |
| _model = Qwen2VLForConditionalGeneration.from_pretrained( | |
| "Qwen/Qwen2-VL-7B-Instruct", | |
| torch_dtype="auto", | |
| device_map="auto" | |
| ) | |
| _processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct") | |
| print("✅ Modèle chargé avec succès!") | |
| results = [] | |
| for image in input_imgs: | |
| image_path, width, height = array_to_image_path(image) | |
| try: | |
| messages = [ | |
| { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "type": "image", | |
| "image": image_path, | |
| "resized_height": height, | |
| "resized_width": width | |
| }, | |
| { | |
| "type": "text", | |
| "text": text_input | |
| } | |
| ] | |
| } | |
| ] | |
| 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") | |
| generated_ids = _model.generate(**inputs, max_new_tokens=4096) | |
| generated_ids_trimmed = [ | |
| out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) | |
| ] | |
| raw_output = _processor.batch_decode( | |
| generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=True | |
| ) | |
| results.append(raw_output[0]) | |
| print(f"✅ Processed: {image}") | |
| except Exception as e: | |
| print(f"❌ Error processing {image}: {str(e)}") | |
| results.append(f"Error: {str(e)}") | |
| finally: | |
| if os.path.exists(image_path): | |
| os.remove(image_path) | |
| return "\n\n---\n\n".join(results) | |
| 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_imgs = gr.Files(file_types=["image"], label="Upload Document Images") | |
| text_input = gr.Textbox(label="Query", placeholder="Enter your query here...") | |
| submit_btn = gr.Button(value="Submit", variant="primary") | |
| with gr.Column(): | |
| output_text = gr.Textbox(label="Response", elem_id="output") | |
| submit_btn.click(run_inference, [input_imgs, text_input], [output_text]) | |
| demo.queue(api_open=True) | |
| demo.launch(debug=True) |