| import gradio as gr |
| from ultralytics import YOLO |
| import cv2 |
| import PIL.Image as Image |
| import numpy as np |
| import os |
| from huggingface_hub import hf_hub_download |
| import spaces |
|
|
| token = os.getenv("ACE_TOKEN") |
| repo_id = "LexBwmn/ACE_LAB" |
|
|
| try: |
| model_path = hf_hub_download( |
| repo_id=repo_id, |
| filename="model.pt", |
| token=token |
| ) |
| model = YOLO(model_path) |
| except Exception as e: |
| print(f"Error loading private model: {e}") |
| model = None |
|
|
| @spaces.GPU |
| def predict(img): |
| global model |
| if model is None: |
| return None |
| |
| try: |
| img_array = np.array(img) |
| results = model(img_array, conf=0.466, imgsz=640) |
| res_plotted = results[0].plot() |
| res_rgb = cv2.cvtColor(res_plotted, cv2.COLOR_BGR2RGB) |
| return Image.fromarray(res_rgb) |
| except Exception as e: |
| print(f"CRITICAL ERROR DURING SUBMIT: {e}") |
| return None |
|
|
| demo = gr.Interface( |
| fn=predict, |
| inputs=gr.Image(type="pil", label="Upload Brain MRI"), |
| outputs=gr.Image(type="pil", label="Detection Results"), |
| title="ACE-V1.1 Lab Test", |
| description="Secure Inference Test for Brain Tumor Detection." |
| ) |
|
|
| if __name__ == "__main__": |
| demo.queue(default_concurrency_limit=1) |
| demo.launch(ssr_mode=False) |
