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The error you're encountering stems from two separate issues:
1. **`trust_remote_code` warning:**
This warning is triggered because `trust_remote_code` is used in the wrong context. It only affects Auto classes (like `AutoModel` or `AutoProcessor`) but has no effect when loading the model directly using `Qwen2VLForConditionalGeneration`. You can safely remove it when loading the model. Here's the corrected model loading line:
```python
model = Qwen2VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-VL-2B-Instruct",
torch_dtype=torch.float32,
device_map="cpu"
).eval()
```
2. **`enable_queue` argument in `launch`:**
The argument `enable_queue` has been replaced by `queue` in recent Gradio versions. Instead of using `enable_queue=False`, you should use `queue=False`. Here’s how to fix the `demo.launch()` call:
```python
demo.launch(inline=False, server_name="0.0.0.0", server_port=int(os.getenv("PORT", 7860)), debug=True, queue=False)
```
This should resolve the issues you're encountering. Here's the corrected code:
### Final Code Fix:
```python
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", "")
demo.queue(api_open=False)
demo.launch(inline=False, server_name="0.0.0.0", server_port=int(os.getenv("PORT", 7860)), debug=True, queue=("--no-gradio-queue" not in commandline_args))
```
This code should now work without the previous errors. |