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Parent(s):
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Add model
Browse files- .gitignore +2 -0
- README.md +60 -11
- app.py +19 -0
- dockerfile +10 -0
- models/__init__.py +0 -0
- models/models.py +83 -0
- requirements.txt +64 -0
- upscaled_image.jpg +0 -0
.gitignore
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__pycache__
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.local
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README.md
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# Image Transfer and Upscaling API
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This service provides APIs to perform image upscaling and style transfer (Monet style) using Machine Learning models. The project is built using Flask, a lightweight WSGI web application framework.
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```bash
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git clone <repository-url>
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cd <project-directory>
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Install the dependencies using pip.
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```
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```bash
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pip install -r requirements.txt
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```
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## Running the Application
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Run the application with the following command:
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```bash
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python3 -m flask run --host=0.0.0.0
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```
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The application should start and be accessible at localhost:5000.
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## API Endpoints
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The application exposes two API endpoints:
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```/monet```: POST endpoint which accepts an image file and returns the image stylized in the style of Monet paintings.
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```/upscale```: POST endpoint which accepts an image file and returns the upscaled version of the image.
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#File Structure
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```app.py```: This is the main application file which runs the Flask application and defines the API endpoints.
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```models/models.py```: This file contains the model loading and prediction functions.
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## models/models.py
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```tensor_to_image(tensor)```: Converts a TensorFlow tensor to an image and saves it to 'upscaled_image.jpg'.
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```request_to_image(request)```: Converts a Flask request object containing an image to a numpy array.
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```_monet(image, upscale=False)```: Accepts an image as input and returns the image stylized in the style of Monet paintings.
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```_upscale(image)```: Accepts an image as input and returns the upscaled version of the image.
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## Sending Requests to the API
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For both endpoints, the image file should be included in the request as form data with the key 'image'. Here is an example using curl:
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```bash
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Copy code
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curl -X POST -F "image=@<image-file-path>" http://localhost:5000/monet
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```
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```bash
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Copy code
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curl -X POST -F "image=@<image-file-path>" http://localhost:5000/upscale
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Replace <image-file-path> with the path to your image file.
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```
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## Notes
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The model files for the Monet style transfer (models/monet_generator) are not included in this repository. You need to download them separately and place them in the correct location.
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Contributing
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Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
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## License
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MIT
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docker buildx build --platform linux/amd64,linux/arm64 -t joshuapeddle/imagetransfer-server:0.0.2 --push .
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docker run -p 5000:5000 joshuapeddle/imagetransfer-server:0.0.2
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app.py
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from flask import Flask , request, send_file
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from flask_cors import CORS
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from PIL import Image
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from models.models import _upscale, _monet, request_to_image
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from numpy import asarray
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app = Flask(__name__)
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CORS(app)
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@app.route("/monet", methods=['POST'])
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def monet():
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return send_file(_monet(request_to_image(request) ,True), mimetype='image/jpg')
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@app.route("/upscale", methods=['POST'])
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def upscale():
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return send_file(_upscale(request_to_image(request)), mimetype='image/jpg')
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@app.route("/", methods=['GET'])
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def hello():
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return "Hello World"
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dockerfile
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FROM python:3.9.16-buster
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WORKDIR /usr/src/app
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COPY requirements.txt ./
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RUN pip install --no-cache-dir -r requirements.txt
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COPY . .
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CMD [ "python3", "-m" , "flask", "run", "--host=0.0.0.0", "--debug"]
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models/__init__.py
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models/models.py
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import os
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import time
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from PIL import Image
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import tensorflow as tf
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import tensorflow_hub as hub
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from numpy import asarray
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from tensorflow.python.ops.numpy_ops import np_config
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np_config.enable_numpy_behavior()
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os.environ["TFHUB_DOWNLOAD_PROGRESS"] = "True"
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from huggingface_hub import from_pretrained_keras
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def tensor_to_image(tensor):
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tensor = tf.cast(tf.clip_by_value(tensor, 0, 255), tf.uint8)
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tensor = Image.fromarray(tensor.numpy())
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tensor.save('upscaled_image.jpg')
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return 'upscaled_image.jpg'
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def request_to_image(request):
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image = request.files.get('image', False)
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print(image)
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if image:
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return asarray(Image.open(image))
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else:return asarray(Image.open(request.files['image']))
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def _monet(image, upscale=False):
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IMAGE_SIZE = (256, 256)
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def decode_image(image):
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#image = tf.image.decode_jpeg(image, channels=3)
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image = (tf.cast(image, tf.float32) / 127.5) - 1
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image = tf.reshape(image, [*IMAGE_SIZE, 3])
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return image
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#new_model = tf.keras.models.load_model('models/monet_generator', compile=False)
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new_model = from_pretrained_keras("JoshuaPeddle/MonetGenerator")
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#new_model.summary()
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image = tf.image.resize(image, IMAGE_SIZE)
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image = decode_image(image)
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image = tf.expand_dims(image, 0)
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start = time.time()
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prediction = new_model(image, training=False)
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prediction = tf.reshape(prediction, [256, 256, 3])
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prediction = (prediction + 1) / 2
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prediction = tf.image.convert_image_dtype(prediction, tf.uint8)
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print("Time Taken: %f" % (time.time() - start))
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if upscale:
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return _upscale(prediction)
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return tensor_to_image(prediction)
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def _upscale(image):
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SAVED_MODEL_PATH = "https://tfhub.dev/captain-pool/esrgan-tf2/1"
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def preprocess_image(_image):
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""" Loads image from path and preprocesses to make it model ready
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Args:
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image_path: Path to the image file
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"""
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#hr_image = tf.image.decode_image(tf.io.read_file(image_path))
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hr_image = _image
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# If PNG, remove the alpha channel. The model only supports
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# images with 3 color channels.
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if hr_image.shape[-1] == 4:
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hr_image = hr_image[...,:-1]
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hr_size = (tf.convert_to_tensor(hr_image.shape[:-1]) // 4) * 4
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hr_image = tf.image.crop_to_bounding_box(hr_image, 0, 0, hr_size[0], hr_size[1])
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hr_image = tf.cast(hr_image, tf.float32)
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return tf.expand_dims(hr_image, 0)
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hr_image = preprocess_image(image)
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model = hub.load(SAVED_MODEL_PATH)
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start = time.time()
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fake_image = model(hr_image)
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fake_image = tf.squeeze(fake_image)
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print("Time Taken: %f" % (time.time() - start))
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fake_image = tf.image.resize(fake_image, (512,512))
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return tensor_to_image(fake_image)
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requirements.txt
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absl-py==1.4.0
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astunparse==1.6.3
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blinker==1.6.2
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cachetools==5.3.0
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certifi==2023.5.7
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charset-normalizer==3.1.0
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click==8.1.3
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contourpy==1.0.7
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cycler==0.11.0
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filelock==3.12.0
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Flask==2.3.2
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Flask-Cors==3.0.10
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flatbuffers==23.5.8
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fonttools==4.39.4
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fsspec==2023.5.0
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gast==0.4.0
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google-auth==2.18.0
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google-auth-oauthlib==1.0.0
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google-pasta==0.2.0
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grpcio==1.54.0
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h5py==3.8.0
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huggingface-hub==0.14.1
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idna==3.4
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importlib-metadata==6.6.0
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importlib-resources==5.12.0
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itsdangerous==2.1.2
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jax==0.4.9
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Jinja2==3.1.2
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keras==2.12.0
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kiwisolver==1.4.4
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libclang==16.0.0
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Markdown==3.4.3
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MarkupSafe==2.1.2
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matplotlib==3.7.1
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ml-dtypes==0.1.0
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numpy==1.23.5
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oauthlib==3.2.2
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opt-einsum==3.3.0
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packaging==23.1
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Pillow==9.5.0
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protobuf==4.23.0
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pyasn1==0.5.0
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pyasn1-modules==0.3.0
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pyparsing==3.0.9
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python-dateutil==2.8.2
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PyYAML==6.0
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requests==2.30.0
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requests-oauthlib==1.3.1
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rsa==4.9
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scipy==1.10.1
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six==1.16.0
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tensorboard==2.12.3
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tensorboard-data-server==0.7.0
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tensorflow==2.12.0
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tensorflow-estimator==2.12.0
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tensorflow-hub==0.13.0
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tensorflow-io-gcs-filesystem==0.32.0
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termcolor==2.3.0
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tqdm==4.65.0
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typing_extensions==4.5.0
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urllib3==1.26.15
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Werkzeug==2.3.4
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wrapt==1.14.1
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zipp==3.15.0
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upscaled_image.jpg
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