app.py

import gradio as gr
import requests
from detectron2.config import get_cfg
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog
import numpy as np
import cv2
from PIL import Image
import os

# Dropbox model link
MODEL_URL = "https://www.dropbox.com/scl/fi/m8e7tr4vy887rrmedvpok/model_final-1.pth?rlkey=bf5ov8r1m89u9qp88alpuvmse&st=htkj8ux1&dl=1"
MODEL_PATH = "model_final.pth"

# Download model if not exists
if not os.path.exists(MODEL_PATH):
    print("Downloading model...")
    response = requests.get(MODEL_URL, stream=True)
    with open(MODEL_PATH, 'wb') as f:
        for chunk in response.iter_content(chunk_size=8192):
            f.write(chunk)
    print("Model downloaded.")

# Configure Detectron2
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 8
cfg.MODEL.WEIGHTS = MODEL_PATH
cfg.MODEL.DEVICE = "cpu"  # Set to CPU for Hugging Face Spaces

predictor = DefaultPredictor(cfg)

# Metadata
MetadataCatalog.get("car_parts").set(thing_classes=[
    "Dent", "Scratch", "Broken part", "Paint chip",
    "Missing part", "Flaking", "Corrosion", "Cracked"
])
metadata = MetadataCatalog.get("car_parts")

def detect_damage(input_image):
    # Convert PIL image to OpenCV format
    image_cv2 = cv2.cvtColor(np.array(input_image), cv2.COLOR_RGB2BGR)
    
    # Run predictions
    outputs = predictor(image_cv2)

    # Visualize predictions
    v = Visualizer(image_cv2[:, :, ::-1], metadata=metadata, scale=1.0)
    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
    visualized_image = out.get_image()[:, :, ::-1]

    # Convert back to PIL for display in Gradio
    return Image.fromarray(visualized_image)

# Gradio Interface
demo = gr.Interface(
    fn=detect_damage,
    inputs=gr.Image(type="pil"),
    outputs="image",
    title="Car Parts Damage Detection",
    description="Upload an image of a car to detect damage such as dents, scratches, and broken parts."
)

if __name__ == "__main__":
    demo.launch()