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import gradio as gr
import torch
from PIL import Image
from transformers import AutoModelForImageTextToText, MllamaForConditionalGeneration, AutoProcessor
from transformers import TextStreamer
from torchvision.transforms import Resize
from unsloth import FastVisionModel
# Define the model and processor
model_id = "0llheaven/Llama-3.2-11B-Vision-Radiology-mini"
# device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForImageTextToText.from_pretrained(
model_id,
# load_in_4bit=True,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.gradient_checkpointing_enable()
processor = AutoProcessor.from_pretrained(model_id)
FastVisionModel.for_inference(model)
print("กำลังโหลด tokenizer...")
base_model, tokenizer = FastVisionModel.from_pretrained(
"unsloth/Llama-3.2-11B-Vision-Instruct",
# load_in_4bit = True,
use_gradient_checkpointing = "unsloth",
)
# Function to process the image and generate the description
def generate_description(image: Image.Image, instruction: str):
image = image.convert("RGB")
# image = Resize((224, 224))(image)
# Create the message to pass to the model
instruction = "You are an expert radiographer. Describe accurately what you see in this image."
messages = [
{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": instruction}
]}
]
input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
# input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = tokenizer(
image,
input_text,
add_special_tokens=False,
return_tensors="pt"
).to(model.device)
# Generate the output from the model
# output = model.generate(**inputs, max_new_tokens=256)
text_streamer = TextStreamer(tokenizer, skip_prompt=True)
outputs = model.generate(
**inputs,
streamer=text_streamer,
max_new_tokens=256,
use_cache=True,
temperature=1.5,
min_p=0.1
)
return tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
# Define Gradio interface
interface = gr.Interface(
fn=generate_description,
inputs=gr.Image(type="pil", label="Upload an Image"),
outputs=gr.Textbox(label="Generated Description"),
# live=True,
title="Radiology Image Description Generator",
description="Upload an image and provide an instruction to generate a description using a vision-language model."
)
# Launch the interface
interface.launch()
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