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import gradio as gr
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
model = Qwen2VLForConditionalGeneration.from_pretrained(
"prithivMLmods/Radiology-Infer-Mini", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("prithivMLmods/Radiology-Infer-Mini")
def generate_report(image, text):
# Prepare the message
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": text},
],
}
]
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("cpu")
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]
interface = gr.Interface(
fn=generate_report,
inputs=[
gr.Image(type="pil", label="Upload Image"),
gr.Textbox(label="Enter Description/Query", placeholder="Enter your query here..."),
],
outputs=gr.Textbox(label="Generated Report"),
title="Pter.AI Report Generator",
description="Upload a medical image and provide a description/query to generate a radiology report.",
)
interface.launch()
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