Update app.py

app.py

@@ -1,42 +1,41 @@
 import platform
 import pathlib
-plt = platform.system()
-pathlib.WindowsPath = pathlib.PosixPath
-
-
 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
 
-# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
-
-# %% auto 0
-__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'interface', 'classify_image']
-
-# %% app.ipynb 1
-from fastai.vision.all import *
-import PIL.Image
-PIL.Image.MAX_IMAGE_PIXELS = None
-from PIL import Image
-
-import gradio as gr
-
-# %% app.ipynb 2
-learn = load_learner('Binarymodel.pkl')
-
-# %% app.ipynb 3
-categories=('Brain','Chest','Random')
-
-def classify_image(img):
-    pred,indx,probs=learn.predict(img)
-    return dict(zip(categories,map(float,probs)))
-
-
-# %% app.ipynb 4
-image=gr.inputs.Image(shape=(512,512))
-label=gr.outputs.Label()
-examples=['1.jpg','10.jpg','2.jpeg','9.jpg','3.jpg','4.jpg','5.jpg','6.png','7.jpg',
-'8.jpeg','9.jpg','b.jpg','d.jpg','f.jpg','e.jpg']
-
-
-interface=gr.Interface(fn=classify_image, inputs=image ,outputs=label,examples=examples)
-interface.launch(inline=False)
+# 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)