Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,081 Bytes
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# Standard library imports
import os
from datetime import datetime
import subprocess
import time
# Third-party imports
import numpy as np
import torch
from PIL import Image
import accelerate
import gradio as gr
import spaces
from transformers import (
Qwen2_5_VLForConditionalGeneration,
AutoTokenizer,
AutoProcessor
)
# Local imports
from qwen_vl_utils import process_vision_info
# Set device agnostic code
if torch.cuda.is_available():
device = "cuda"
elif (torch.backends.mps.is_available()) and (torch.backends.mps.is_built()):
device = "mps"
else:
device = "cpu"
print(f"[INFO] Using device: {device}")
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/Qwen2.5-VL-7B-Instruct": Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct",
trust_remote_code=True,
torch_dtype="auto",
device_map="auto").eval(),
"Qwen/Qwen2.5-VL-3B-Instruct": Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct",
trust_remote_code=True,
torch_dtype="auto",
device_map="auto").eval()
}
processors = {
"Qwen/Qwen2.5-VL-7B-Instruct": AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=True),
"Qwen/Qwen2.5-VL-3B-Instruct": AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct", trust_remote_code=True)
}
DESCRIPTION = "[Qwen2.5-VL Demo](https://huggingface.co/collections/Qwen/qwen25-vl-6795ffac22b334a837c0f9a5)"
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=None):
start_time = time.time()
image_path = array_to_image_path(image)
print(image_path)
model = models[model_id]
processor = processors[model_id]
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(device)
# 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
)
end_time = time.time()
total_time = round(end_time - start_time, 2)
return output_text[0], total_time
css = """
#output {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
with gr.Blocks(css=css) as demo:
gr.Markdown(DESCRIPTION)
with gr.Tab(label="Qwen2.5-VL 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/Qwen2.5-VL-7B-Instruct")
text_input = gr.Textbox(label="Text Prompt")
submit_btn = gr.Button(value="Submit")
with gr.Column():
output_text = gr.Textbox(label="Output Text")
time_taken = gr.Textbox(label="Time taken for processing + inference")
submit_btn.click(run_example, [input_img, text_input, model_selector], [output_text, time_taken])
demo.queue(api_open=False)
demo.launch(debug=True) |