File size: 2,249 Bytes
b5ad8d3 48ad800 6e5f522 b5ad8d3 |
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 |
# #Inference using gradio
from peft import PeftModel
from transformers import Qwen2VLForConditionalGeneration
from transformers import AutoProcessor
import gradio as gr
from transformers import Qwen2VLProcessor
from qwen_vl_utils import process_vision_info
#load the base model and finetuned adapter
base_model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
model = PeftModel.from_pretrained(base_model, "vignesha7/qwen2-2b-instruct-Brain-MRI-Description")
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
#inference function
def generate_description(sample):
system_message = "You are an expert MRI radiographer. you can describe what you see in the mri image"
prompt = "Describe accurately what you see in this radiology image."
messages = [
{ "role": "system",
"content": [{"type": "text", "text": system_message}]
},
{ "role": "user",
"content" : [
{"type" : "text", "text" : prompt},
{"type" : "image", "image" : sample}]
},
]
# 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(model.device)
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=256, top_p=1.0, do_sample=True, temperature=0.8)
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]
### Gradio app ###
title = "BrainMRI Radiology Expert"
description = "An Qwen2-VL-2B-Instruct model fine tuned on brain mri images.Describes the brain image"
demo = gr.Interface(
fn=generate_description,
inputs=gr.Image(type='pil'),
outputs='text',
title=title,
description=description,
)
demo.launch()
|