Update app.py

app.py

@@ -1,54 +1,29 @@
-import platform
-import pathlib
-
-# معالجة مشكلة المسارات على الأنظمة المختلفة
-plt = platform.system()
-pathlib.WindowsPath = pathlib.PosixPath
-
-import gradio as gr
-from tensorflow.keras.models import load_model
-import numpy as np
-import cv2
-from huggingface_hub import hf_hub_download
-import base64
-from io import BytesIO
-from PIL import Image
-
-model_path = hf_hub_download(repo_id="SalmanAboAraj/Tooth1", filename="unet_model.h5")
-model = load_model(model_path)
-
-def decode_base64(image_str):
-    image_data = base64.b64decode(image_str)
-    image = Image.open(BytesIO(image_data))
-    image = np.array(image)
-    return image
-
-def encode_base64(image):
-    pil_image = Image.fromarray(image)
-    buffered = BytesIO()
-    pil_image.save(buffered, format="PNG")
-    return base64.b64encode(buffered.getvalue()).decode("utf-8")
-
-def predict(image_str):
-    image = decode_base64(image_str)
-    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 encode_base64(mask)
-
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Textbox(label="Input X-ray Image (base64)"),
-    outputs=gr.Textbox(label="Annotation Mask (base64)"),
-    title="Tooth Segmentation Model",
-    description="Upload a dental X-ray image in base64 format to generate the annotation mask in base64 format.",
-)
-
-if __name__ == "__main__":
-    interface.launch()
+import gradio as gr 
+from tensorflow.keras.models 
+import load_model 
+import numpy as np 
+import cv2 from huggingface_hub 
+import hf_hub_download 
+
+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 
+    
+iface = gr.Interface( 
+    fn=predict, 
+    inputs=gr.Image(type="numpy", label="Input X-ray Image"), 
+    outputs=gr.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()