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import gradio as gr
import torch
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
# Load the model and processor on available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-VL-72B-Instruct-AWQ",
torch_dtype=torch.float16,
#device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-72B-Instruct-AWQ")
@spaces.GPU(duration=60)
def generate_caption(image, prompt):
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image, # The uploaded image
},
{"type": "text", "text": prompt},
],
}
]
# Prepare the 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"
)
device = "cuda" if torch.cuda.is_available() else "cpu"
inputs = inputs.to(device)
# Generate the output
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]
# Launch the Gradio interface with the updated inference function and title
demo = gr.ChatInterface(fn=generate_caption, title="Qwen2-VL-72B-Instruct-OCR", multimodal=True, description="Upload your Image and get the best possible insights out of the Image")
demo.queue().launch()
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