Spaces:
Running
Running
File size: 3,016 Bytes
d971da0 6ce9885 d971da0 6ce9885 582ebd3 |
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 |
import gradio as gr
import torch
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
from PIL import Image
from datetime import datetime
import numpy as np
import os
# Function to save image array as a file and return the path
def array_to_image_path(image_array):
img = Image.fromarray(np.uint8(image_array))
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"image_{timestamp}.png"
img.save(filename)
return os.path.abspath(filename)
# Load model and processor
model = Qwen2VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-VL-2B-Instruct",
torch_dtype=torch.float32,
device_map="cpu"
).eval()
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
DESCRIPTION = "[Qwen2-VL-2B Demo (CPU Version)](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct)"
def run_example(image, text_input):
image_path = array_to_image_path(image)
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",
)
# Inference: Generation of the output
with torch.no_grad():
generated_ids = model.generate(**inputs, max_new_tokens=128)
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-2B Input (CPU)"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Picture")
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], [output_text])
commandline_args = os.getenv("COMMANDLINE_ARGS", "")
# Enable or disable queue based on commandline_args
if "--no-gradio-queue" not in commandline_args:
demo.queue(api_open=False)
demo.launch(inline=False, server_name="0.0.0.0", server_port=int(os.getenv("PORT", 7860)), debug=True)
|