Spaces:
Sleeping
Sleeping
File size: 10,414 Bytes
fc672ff 725766b ab64317 725766b ab64317 725766b ab64317 725766b ab64317 725766b fc672ff 725766b fc672ff 725766b fc672ff 725766b fc672ff ab64317 725766b ab64317 725766b ab64317 725766b ab64317 725766b ab64317 725766b ab64317 fc672ff ab64317 725766b ab64317 725766b ab64317 725766b ab64317 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 | # 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
@spaces.GPU
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) |