File size: 2,397 Bytes
e80fad1 4bda9c3 e80fad1 4bda9c3 e80fad1 4bda9c3 e80fad1 4bda9c3 e80fad1 4bda9c3 e80fad1 4bda9c3 e80fad1 4bda9c3 e80fad1 4bda9c3 e80fad1 4bda9c3 e80fad1 4bda9c3 e80fad1 4bda9c3 e80fad1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
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}
|