na

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+db.sqlite3
+migrations/
+media/
+settings.py
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+.env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/

app.py

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+import numpy as np
+
+import gradio as gr
+from models import efficientnetv2b0_25_arch_styles_Classifier
+
+
+demo = gr.Interface(
+    efficientnetv2b0_25_arch_styles_Classifier, "image", outputs=["text"])
+
+demo.launch()

helper.py

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+import logging
+import os
+import tensorflow as tf
+
+
+def set_tf_loglevel(level):
+    if level >= logging.FATAL:
+        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+    if level >= logging.ERROR:
+        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+    if level >= logging.WARNING:
+        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
+    else:
+        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0'
+    logging.getLogger('tensorflow').setLevel(level)
+
+
+def img_to_tensor(path):
+    '''
+    convert images to tensor 
+        args:
+            path : image file path 
+    '''
+
+    img = tf.keras.utils.load_img(
+        path,
+        color_mode="rgb",
+        target_size=(224, 224),
+        interpolation="nearest",
+    )
+    img_arr = tf.keras.utils.img_to_array(img, data_format=None, dtype=None)
+    img = tf.expand_dims(img_arr, 0)
+    return img

models.py

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+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)

models/EfficientNetV2B0.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5cff674ff44315dd6fe737b4acd94ca9fc3e96f05c3063bfe098584264e82e3
+size 24507440

requirements.txt

@@ -0,0 +1,91 @@
+absl-py==1.1.0
+aiohttp==3.8.1
+aiosignal==1.2.0
+analytics-python==1.4.0
+anyio==3.6.1
+astunparse==1.6.3
+async-timeout==4.0.2
+attrs==21.4.0
+backoff==1.10.0
+bcrypt==3.2.2
+cachetools==5.2.0
+certifi==2022.6.15
+cffi==1.15.1
+charset-normalizer==2.1.0
+click==8.1.3
+colorama==0.4.5
+cryptography==37.0.2
+cycler==0.11.0
+fastapi==0.78.0
+ffmpy==0.3.0
+flatbuffers==1.12
+fonttools==4.33.3
+frozenlist==1.3.0
+fsspec==2022.5.0
+gast==0.4.0
+google-auth==2.9.0
+google-auth-oauthlib==0.4.6
+google-pasta==0.2.0
+gradio==3.0.22
+grpcio==1.47.0
+h11==0.12.0
+h5py==3.7.0
+httpcore==0.15.0
+httpx==0.23.0
+idna==3.3
+Jinja2==3.1.2
+keras==2.9.0
+Keras-Preprocessing==1.1.2
+kiwisolver==1.4.3
+libclang==14.0.1
+linkify-it-py==1.0.3
+Markdown==3.3.7
+markdown-it-py==2.1.0
+MarkupSafe==2.1.1
+matplotlib==3.5.2
+mdit-py-plugins==0.3.0
+mdurl==0.1.1
+monotonic==1.6
+multidict==6.0.2
+numpy==1.23.0
+oauthlib==3.2.0
+opencv-python==4.6.0.66
+opt-einsum==3.3.0
+orjson==3.7.5
+packaging==21.3
+pandas==1.4.3
+paramiko==2.11.0
+Pillow==9.1.1
+protobuf==3.19.4
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pycparser==2.21
+pycryptodome==3.15.0
+pydantic==1.9.1
+pydub==0.25.1
+PyNaCl==1.5.0
+pyparsing==3.0.9
+python-dateutil==2.8.2
+python-multipart==0.0.5
+pytz==2022.1
+requests==2.28.1
+requests-oauthlib==1.3.1
+rfc3986==1.5.0
+rsa==4.8
+six==1.16.0
+sniffio==1.2.0
+starlette==0.19.1
+tensorboard==2.9.1
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+tensorflow==2.9.1
+tensorflow-estimator==2.9.0
+tensorflow-io-gcs-filesystem==0.26.0
+termcolor==1.1.0
+typing_extensions==4.3.0
+uc-micro-py==1.0.1
+urllib3==1.26.9
+uvicorn==0.18.2
+Werkzeug==2.1.2
+wrapt==1.14.1
+yarl==1.7.2