Create app.py

app.py

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+import platform
+import pathlib
+import requests
+import gradio as gr 
+from tensorflow.keras.models import load_model
+import numpy as np 
+import cv2
+from huggingface_hub import hf_hub_download 
+
+# حل مشكلة المسارات في Windows
+plt = platform.system()
+pathlib.WindowsPath = pathlib.PosixPath
+
+# تحميل النموذج من Hugging Face
+model_path = hf_hub_download(repo_id="SalmanAboAraj/Tooth1", filename="unet_model.h5") 
+model = load_model(model_path) 
+
+def predict(image): 
+    original_height, original_width, _ = image.shape 
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
+    image = cv2.resize(image, (128, 128)) 
+    image = np.expand_dims(image, axis=0) 
+    image = np.expand_dims(image, axis=-1) 
+    image = image / 255.0 
+    mask = model.predict(image) 
+    mask = (mask[0] > 0.5).astype(np.uint8) * 255 
+    mask = cv2.resize(mask, (original_width, original_height)) 
+    return mask 
+
+# إنشاء واجهة Gradio باستخدام الإصدار 3.35.2
+image = gr.inputs.Image()
+iface = gr.Interface(
+    fn=predict,
+    inputs=image,
+    outputs=gr.outputs.Image(type="numpy", label="Annotation Mask"),
+    title="Tooth Segmentation Model",
+    description="Upload a dental X-ray image to generate the annotation mask."
+)
+
+if __name__ == "__main__": 
+    iface.launch(inline=False)