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yolo / app.py

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Update app.py

73e01c0 about 1 year ago

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	import warnings
	warnings.filterwarnings("ignore")
	## import necessary packages


	import os
	from typing import Any, Union,Dict, List
	import numpy as np
	import io
	import base64
	import requests
	import tensorflow as tf
	import tensorflow_hub as hub
	from PIL import Image
	from helper import *
	from fastapi import FastAPI

	# Create a new FastAPI app instance
	app = FastAPI()

	hub_handle = 'https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2'
	hub_module = hub.load(hub_handle)



	# Define a function to handle the GET request at `/generate`
	# The generate() function is defined as a FastAPI route that takes a
	# string parameter called text. The function generates text based on the # input using the pipeline() object, and returns a JSON response
	# containing the generated text under the key "output"
	@app.get("/generate")
	def generate(features: Dict[str, str]):
	"""
	Using the text summarization pipeline from `transformers`, summerize text
	from the given input text. The model used is `philschmid/bart-large-cnn-samsum`, which
	can be found [here](<https://huggingface.co/philschmid/bart-large-cnn-samsum>).
	"""
	# Use the pipeline to generate text from the given input text

	content_image_url = features['url']
	if 'style_url' not in features:
	style_image_url = 'https://upload.wikimedia.org/wikipedia/commons/c/c5/Edvard_Munch%2C_1893%2C_The_Scream%2C_oil%2C_tempera_and_pastel_on_cardboard%2C_91_x_73_cm%2C_National_Gallery_of_Norway.jpg'
	else:
	style_image_url = features['style_url']

	# Load images
	content_img_size = (500, 500)
	style_img_size = (300, 300)

	style_image = load_image(style_image_url, style_img_size)
	content_image = load_image(content_image_url, content_img_size)
	style_image = tf.nn.avg_pool(
	style_image, ksize=[3, 3], strides=[1, 1], padding='SAME')

	# Stylize content image with given style image.
	outputs = hub_module(tf.constant(content_image),
	tf.constant(style_image))
	stylized_image = outputs[0]

	# get PIL image and convert to base64
	img = Image.fromarray(np.uint8(stylized_image.numpy()[0] * 255))
	im_file = io.BytesIO()
	img.save(im_file, format="PNG")
	im_bytes = base64.b64encode(im_file.getvalue()).decode("utf-8")
	# Return the generated text in a JSON response
	return {"output": im_bytes}