Update app/process.py

app/process.py

@@ -1,48 +1,48 @@
-
-
-from ultralytics import YOLO
-from tensorflow.keras.preprocessing.image import img_to_array
-import numpy as np
-import cv2
-from tensorflow.keras.models import load_model
-from tensorflow.keras.applications.mobilenet_v3 import preprocess_input
-import PIL.Image  as Image
-import io
-import base64
-import os 
-
-BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-YOLO_PATH= os.join(BASE_DIR,"models",'Yolo.pt')
-MOBILENET_PATH= os.join(BASE_DIR,"models",'MobileNetV3_rust_classifier.keras')
-
-YOLO_MODEL=YOLO(YOLO_PATH)
-MOBILENET_MODEL=load_model(MOBILENET_PATH)
-
-
-
-def predict(image_bytes):
-    img = Image.open(io.BytesIO(image_bytes)).resize((224, 224))
-    x = img_to_array(img)
-    x = np.expand_dims(x, axis=0)
-    x = preprocess_input(x)
-    proba = MOBILENET_MODEL.predict(x)[0]
-    prediction = np.argmax(proba)
-    return prediction,proba[prediction]
-    
-
-def detect(image_bytes):
-    image  = Image.open(io.BytesIO(image_bytes)).convert("RGB")
-    result = YOLO_MODEL(image)[0]
-    img_result= Image.fromarray(result.plot()) 
-    buffer=io.BytesIO()
-    img_result.save(buffer,format="PNG")
-    base64_str = base64.b64encode(buffer.getvalue()).decode('utf-8')
-    uri =f"data:image/png;base64,{base64_str}"
-    return uri
-    
-
-
-
-
-
-
+
+
+from ultralytics import YOLO
+from tensorflow.keras.preprocessing.image import img_to_array
+import numpy as np
+import cv2
+from tensorflow.keras.models import load_model
+from tensorflow.keras.applications.mobilenet_v3 import preprocess_input
+import PIL.Image  as Image
+import io
+import base64
+import os 
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+YOLO_PATH= os.path.join(BASE_DIR,"models",'Yolo.pt')
+MOBILENET_PATH= os.path.join(BASE_DIR,"models",'MobileNetV3_rust_classifier.keras')
+
+YOLO_MODEL=YOLO(YOLO_PATH)
+MOBILENET_MODEL=load_model(MOBILENET_PATH)
+
+
+
+def predict(image_bytes):
+    img = Image.open(io.BytesIO(image_bytes)).resize((224, 224))
+    x = img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+    x = preprocess_input(x)
+    proba = MOBILENET_MODEL.predict(x)[0]
+    prediction = np.argmax(proba)
+    return prediction,proba[prediction]
+    
+
+def detect(image_bytes):
+    image  = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    result = YOLO_MODEL(image)[0]
+    img_result= Image.fromarray(result.plot()) 
+    buffer=io.BytesIO()
+    img_result.save(buffer,format="PNG")
+    base64_str = base64.b64encode(buffer.getvalue()).decode('utf-8')
+    uri =f"data:image/png;base64,{base64_str}"
+    return uri
+    
+
+
+
+
+
+