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Dockerfile

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+FROM python:3.10
+
+# 1. Install system dependencies
+RUN apt-get update && apt-get install -y \
+    git \
+    libgl1 \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# 2. Create user
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+# 3. Install Torch dependencies FIRST
+RUN pip install --no-cache-dir pip --upgrade && \
+    pip install --no-cache-dir \
+    torch==2.0.1+cpu \
+    torchvision==0.15.2+cpu \
+    --extra-index-url https://download.pytorch.org/whl/cpu
+
+# 4. Install Detectron2 with --no-build-isolation
+# This flag fixes the "No module named torch" error
+RUN pip install --no-cache-dir --no-build-isolation git+https://github.com/facebookresearch/detectron2.git
+
+# 5. Install the requirements.txt 
+WORKDIR /app
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 6. Copy app files and run
+COPY --chown=user . /app
+CMD ["python", "app.py"]

README.md

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+---
+title: Capstone
+emoji: 🌖
+colorFrom: blue
+colorTo: purple
+sdk: docker
+app_port: 7860
+---
+
+# Dental X-Ray Segmentation Capstone
+
+This Space hosts a deep learning model for the automatic segmentation of dental panoramic X-rays.
+
+## How to use
+1. Upload a panoramic dental X-ray (JPEG/PNG).
+2. Click "Run Segmentation".
+3. View the overlay and detection data.
+
+## Citations and References
+
+This project utilizes the following research and datasets. Please cite them if you use this work:
+
+> Brahmi, W., & Jdey, I. (2024). Automatic tooth instance segmentation and identification from panoramic X-Ray images using deep CNN. *Multimedia Tools and Applications, 83*(18), 55565–55585.
+> Brahmi, W., Jdey, I., & Drira, F. (2024). Exploring the role of Convolutional Neural Networks (CNN) in dental radiography segmentation: A comprehensive Systematic Literature Review. *Engineering Applications of Artificial Intelligence, 133*, 108510.
+> Abderrahim, H. (2020). *Panoramic Dental X-rays* [Data set]. Mendeley Data, V3.  
+> Available at: [https://data.mendeley.com/datasets/73n3kz2k4k/3](https://data.mendeley.com/datasets/73n3kz2k4k/3)

app.py

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+import os, json, time
+import numpy as np, cv2, torch, gradio as gr
+from detectron2.config import get_cfg
+from detectron2.engine import DefaultPredictor
+from detectron2.data import MetadataCatalog
+from detectron2.utils.visualizer import Visualizer, ColorMode
+
+# --- 1. CONFIGURATION & MODEL LOADING ---
+LOAD_DIR = "./artifacts"
+WEIGHTS = os.path.join(LOAD_DIR, "model_final.pth")
+CFG_PATH = os.path.join(LOAD_DIR, "config.yaml")
+CLASSES_PATH = os.path.join(LOAD_DIR, "classes.json")
+
+cfg = get_cfg()
+cfg.merge_from_file(CFG_PATH)
+cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+cfg.MODEL.WEIGHTS = WEIGHTS
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
+
+classes = None
+if os.path.exists(CLASSES_PATH):
+    with open(CLASSES_PATH) as f:
+        classes = json.load(f)
+    cfg.MODEL.ROI_HEADS.NUM_CLASSES = len(classes)
+
+predictor = DefaultPredictor(cfg)
+meta = MetadataCatalog.get("inference_only")
+meta.thing_classes = classes if classes else [f"class_{i}" for i in range(cfg.MODEL.ROI_HEADS.NUM_CLASSES)]
+
+MAX_SIDE = 1600
+
+# --- 2. INFERENCE FUNCTION ---
+def segment(rgb: np.ndarray):
+    t0 = time.time()
+    # Handle potential None input if user clicks run without image
+    if rgb is None:
+        return None, {"error": "No image uploaded"}
+        
+    h0, w0 = rgb.shape[:2]
+    scale = 1.0
+    if max(h0, w0) > MAX_SIDE:
+        scale = MAX_SIDE / max(h0, w0)
+        rgb_small = cv2.resize(rgb, (int(w0*scale), int(h0*scale)), interpolation=cv2.INTER_AREA)
+    else:
+        rgb_small = rgb
+
+    outputs = predictor(rgb_small[:, :, ::-1])  # predictor expects BGR
+    inst = outputs["instances"].to("cpu")
+
+    vis = Visualizer(rgb_small, metadata=meta, scale=1.0, instance_mode=ColorMode.IMAGE_BW)
+    overlay_rgb = vis.draw_instance_predictions(inst).get_image()
+
+    dets = []
+    if inst.has("pred_boxes"):
+        boxes = inst.pred_boxes.tensor.numpy().tolist()
+        scores = inst.scores.numpy().tolist() if inst.has("scores") else [None]*len(boxes)
+        classes_idx = inst.pred_classes.numpy().tolist() if inst.has("pred_classes") else [0]*len(boxes)
+        inv = (1.0/scale) if scale != 1.0 else 1.0
+        for b, s, c in zip(boxes, scores, classes_idx):
+            b = [float(x*inv) for x in b]
+            label = meta.thing_classes[c] if 0 <= c < len(meta.thing_classes) else str(c)
+            dets.append({"box": b, "class": label, "score": float(s)})
+
+    return overlay_rgb, {
+        "instances": dets,
+        "original_size": [int(h0), int(w0)],
+        "latency_ms": int((time.time()-t0)*1000),
+    }
+
+# --- 3. GRADIO INTERFACE ---
+
+# Define the paths to your example images
+example_files = [
+    ["examples/1.jpg"],
+    ["examples/2.jpg"],
+    ["examples/3.jpg"],
+    ["examples/4.jpg"],
+    ["examples/5.jpg"]
+]
+
+with gr.Blocks(title="Panoramic Radiograph Segmentation") as demo:
+    gr.Markdown("## Dental X-Ray Segmentation App")
+    gr.Markdown("Upload a panoramic radiograph (or click an example below) to detect teeth.")
+
+    with gr.Row():
+        # --- Left Column: Input ---
+        with gr.Column():
+            img_in = gr.Image(type="numpy", label="Input Radiograph")
+            
+            # This adds the thumbnails row
+            gr.Examples(
+                examples=example_files,
+                inputs=img_in,
+                label="Click an example to load it:"
+            )
+            
+            submit_btn = gr.Button("Run Segmentation", variant="primary")
+
+        # --- Right Column: Output ---
+        with gr.Column():
+            img_out = gr.Image(label="Overlay Result")
+            json_out = gr.JSON(label="Detections Data")
+
+    # Link the button to the function
+    submit_btn.click(fn=segment, inputs=img_in, outputs=[img_out, json_out], api_name="/predict")
+
+    # --- CITATIONS SECTION ---
+    gr.Markdown("---") 
+    gr.Markdown(
+        """
+        ### Credits & Citations
+        Credits & Citations:
+        * **Brahmi, W., & Jdey, I. (2024). Automatic tooth instance segmentation and identification from panoramic X-Ray images using deep CNN. *Multimedia Tools and Applications, 83*(18), 55565–55585.
+        * **Brahmi, W., Jdey, I., & Drira, F. (2024). Exploring the role of Convolutional Neural Networks (CNN) in dental radiography segmentation: A comprehensive Systematic Literature Review. *Engineering Applications of Artificial Intelligence, 133*, 108510.
+        * **[Panoramic Dental X-rays (Mendeley Data)](https://data.mendeley.com/datasets/73n3kz2k4k/3)
+        """
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

gitattributes

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requirements.txt

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+gradio
+opencv-python
+numpy<2
+Pillow

runtime.txt

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+python-3.10