Upload models.py

models.py

@@ -0,0 +1,49 @@
+import tensorflow as tf
+import numpy as np
+import cv2
+
+
+def outputs(y):
+    return {
+        "Achaemenid architecture": y[0],
+        "American craftsman style": y[1],
+        "American Foursquare architecture": y[2],
+        "Ancient Egyptian architecture": y[3],
+        "Art Deco architecture": y[4],
+        "Art Nouveau architecture": y[5],
+        "Baroque architecture": y[6],
+        "Bauhaus architecture": y[7],
+        "Beaux Arts architecture": y[8],
+        "Byzantine architecture": y[9],
+        "Chicago school_architecture": y[10],
+        "Colonial architecture": y[11],
+        "Deconstructivism": y[12],
+        "Edwardian architecture": y[13],
+        "Georgian architecture": y[14],
+        "Gothic architecture": y[15],
+        "Greek Revival architecture": y[16],
+        "International style": y[17],
+        "Novelty architecture": y[18],
+        "Palladian architecture": y[19],
+        "Postmodern architecture": y[20],
+        "Queen Anne architecture": y[21],
+        "Romanesque architecture": y[22],
+        "Russian Revival_architecture": y[23],
+        "Tudor Revival architecture": y[24],
+    }
+
+
+def efficientnetv2b0_25_arch_styles_Classifier(image):
+    # file_path = f"./images/{file.filename}"
+    # with open(file_path, "wb") as f:
+    #     f.write(file.file.read())
+    resized_image = cv2.resize(image, dsize=(
+        224, 224), interpolation=cv2.INTER_CUBIC)
+    img = tf.expand_dims(resized_image, 0)
+    efficientnetv2b0 = tf.keras.models.load_model(
+        "models\EfficientNetV2B0.h5")
+
+    y = efficientnetv2b0.predict(img).reshape(-1)
+    y = (np.round(y, 3)*100).tolist()
+
+    return outputs(y)