Duplicate from aravinds1811/neural-style-transfer

Co-authored-by: Aravind Sundaresan <[email protected]>

.gitattributes

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README.md

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+---
+title: Neural Style Transfer
+emoji: 📊
+colorFrom: green
+colorTo: green
+sdk: gradio
+app_file: app.py
+pinned: false
+duplicated_from: aravinds1811/neural-style-transfer
+---
+
+# Configuration
+
+`title`: _string_  
+Display title for the Space
+
+`emoji`: _string_  
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_  
+Can be either `gradio` or `streamlit`
+
+`sdk_version` : _string_  
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_  
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_  
+Whether the Space stays on top of your list.

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+import tensorflow_hub as hub
+
+style_transfer_model = hub.load("https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2")
+
+def perform_style_transfer(content_image, style_image):
+    
+    content_image = tf.convert_to_tensor(content_image, np.float32)[tf.newaxis, ...] / 255.
+    style_image = tf.convert_to_tensor(style_image, np.float32)[tf.newaxis, ...] / 255.
+    
+    output = style_transfer_model(content_image, style_image)
+    stylized_image = output[0]
+    
+    return Image.fromarray(np.uint8(stylized_image[0] * 255))
+
+
+content_image_input = gr.inputs.Image(label="Content Image")
+style_image_input = gr.inputs.Image(shape=(256, 256), label="Style Image")
+
+# Examples
+golden_gate = ["examples/golden_gate_bridge.jpeg", "examples/the_great_wave.jpeg"]
+joshua_tree = ["examples/joshua_tree.jpeg", "examples/starry_night.jpeg"]
+glacier = ["examples/glacier_national_park.jpeg", "examples/the_scream.jpg"]
+
+app_interface = gr.Interface(fn=perform_style_transfer,
+                             inputs=[content_image_input, style_image_input],
+                             outputs="image",
+                             title="Fast Neural Style Transfer",
+                             description="Gradio demo for Fast Neural Style Transfer using a pretrained Image Stylization model from TensorFlow Hub. To use it, simply upload a content image and style image, or click one of the examples to load them. To learn more about the project, please find the references listed below.",
+                             examples=[glacier, golden_gate, joshua_tree],
+                             article="**References**\n\n"
+                             "<a href='https://www.tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization' target='_blank'>1. Tutorial to implement Fast Neural Style Transfer using the pretrained model from TensorFlow Hub</a>  \n"
+                             "<a href='https://huggingface.co/spaces/luca-martial/neural-style-transfer' target='_blank'>2. The idea to build a neural style transfer application was inspired from this Hugging Face Space </a>")
+app_interface.launch()

requirements.txt

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+numpy==1.21.2
+tensorflow==2.2.0
+tensorflow_hub==0.12.0