btphan95 · May 9, 2020 23:00
diff --git a/app.py b/app.py
 #deep learning libraries
 from fastai.vision import *
 import torch
 defaults.device = torch.device('cpu')

 #web frameworks
 from starlette.applications import Starlette
 from starlette.responses import JSONResponse, HTMLResponse, RedirectResponse
 import uvicorn
 import aiohttp
 import asyncio

 import os
 import sys
 import base64 
 from PIL import Image

 async def get_bytes(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.read()

 app = Starlette()
 path = Path('')
 learner = load_learner(path)

 @app.route("/upload", methods = ["POST"])
 async def upload(request):
    data = await request.form()
    bytes = await (data["file"].read())
    return predict_image_from_bytes(bytes)

 @app.route("/classify-url", methods = ["GET"])
 async def classify_url(request):
    bytes = await get_bytes(request.query_params["url"])
    return predict_image_from_bytes(bytes)

 def predict_image_from_bytes(bytes):
    #load byte data into a stream
    img_file = io.BytesIO(bytes)
    #encoding the image in base64 to serve in HTML
    img_pil = Image.open(img_file)
    img_pil.save("img.jpg", format="JPEG")
    img_uri = base64.b64encode(open("img.jpg", 'rb').read()).decode('utf-8')
    
    #make inference on image and return an HTML response
    img = open_image(img_file)
    pred_class, pred_idx, outputs = learner.predict(img)
    formatted_outputs = ["{:.1f}%".format(value) for value in [x * 100 for x in torch.nn.functional.softmax(outputs, dim = 0)]]
    pred_probs = sorted(zip(learner.data.classes, map(str, formatted_outputs)),
                        key = lambda p: p[1],
                        reverse = True
                       )
    return HTMLResponse(
        """
        <html>
            <body>
                <p> Prediction: <b> %s </b> </p>
                <p> Confidence: <b> %s </b> </p>
            </body>
        <figure class = "figure">
            <img src="data:image/png;base64, %s" class = "figure-img">
        </figure>
        </html>
        """ %(pred_class, pred_probs, img_uri))
        
 @app.route("/")
 def form(request):
        return HTMLResponse(
            """
            <h1> Greenr </h1>
            <p> Is your picture of a dandelion or grass? </p>
            <form action="/upload" method = "post" enctype = "multipart/form-data">
                <u> Select picture to upload: </u> <br> <p>
                1. <input type="file" name="file"><br><p>
                2. <input type="submit" value="Upload">
            </form>
            <br>
            <br>
            <u> Submit picture URL </u>
            <form action = "/classify-url" method="get">
                1. <input type="url" name="url" size="60"><br><p>
                2. <input type="submit" value="Upload">
            </form>
            """)
        
 @app.route("/form")
 def redirect_to_homepage(request):
        return RedirectResponse("/")
        
 if __name__ == "__main__":
    if "serve" in sys.argv:
        port = int(os.environ.get("PORT", 8008)) 
        uvicorn.run(app, host = "0.0.0.0", port = port)
	#deep learning libraries
	from fastai.vision import *
	import torch
	defaults.device = torch.device('cpu')

	#web frameworks
	from starlette.applications import Starlette
	from starlette.responses import JSONResponse, HTMLResponse, RedirectResponse
	import uvicorn
	import aiohttp
	import asyncio

	import os
	import sys
	import base64
	from PIL import Image

	async def get_bytes(url):
	async with aiohttp.ClientSession() as session:
	async with session.get(url) as response:
	return await response.read()

	app = Starlette()
	path = Path('')
	learner = load_learner(path)

	@app.route("/upload", methods = ["POST"])
	async def upload(request):
	data = await request.form()
	bytes = await (data["file"].read())
	return predict_image_from_bytes(bytes)

	@app.route("/classify-url", methods = ["GET"])
	async def classify_url(request):
	bytes = await get_bytes(request.query_params["url"])
	return predict_image_from_bytes(bytes)

	def predict_image_from_bytes(bytes):
	#load byte data into a stream
	img_file = io.BytesIO(bytes)
	#encoding the image in base64 to serve in HTML
	img_pil = Image.open(img_file)
	img_pil.save("img.jpg", format="JPEG")
	img_uri = base64.b64encode(open("img.jpg", 'rb').read()).decode('utf-8')

	#make inference on image and return an HTML response
	img = open_image(img_file)
	pred_class, pred_idx, outputs = learner.predict(img)
	formatted_outputs = ["{:.1f}%".format(value) for value in [x * 100 for x in torch.nn.functional.softmax(outputs, dim = 0)]]
	pred_probs = sorted(zip(learner.data.classes, map(str, formatted_outputs)),
	key = lambda p: p[1],
	reverse = True
	)
	return HTMLResponse(
	"""
	<html>
	<body>
	<p> Prediction: <b> %s </b> </p>
	<p> Confidence: <b> %s </b> </p>
	</body>
	<figure class = "figure">
	<img src="data:image/png;base64, %s" class = "figure-img">
	</figure>
	</html>
	""" %(pred_class, pred_probs, img_uri))

	@app.route("/")
	def form(request):
	return HTMLResponse(
	"""
	<h1> Greenr </h1>
	<p> Is your picture of a dandelion or grass? </p>
	<form action="/upload" method = "post" enctype = "multipart/form-data">
	<u> Select picture to upload: </u> <br> <p>
	1. <input type="file" name="file"><br><p>
	2. <input type="submit" value="Upload">
	</form>
	<br>
	<br>
	<u> Submit picture URL </u>
	<form action = "/classify-url" method="get">
	1. <input type="url" name="url" size="60"><br><p>
	2. <input type="submit" value="Upload">
	</form>
	""")

	@app.route("/form")
	def redirect_to_homepage(request):
	return RedirectResponse("/")

	if __name__ == "__main__":
	if "serve" in sys.argv:
	port = int(os.environ.get("PORT", 8008))
	uvicorn.run(app, host = "0.0.0.0", port = port)