Thanks for looking into this. The jollyness is running out thin 
- The link to the model works fine - just tested it now
The server.py file:
import aiohttp
import asyncio
import uvicorn
from fastai import *
from fastai.vision import *
from io import BytesIO
from starlette.applications import Starlette
from starlette.middleware.cors import CORSMiddleware
from starlette.responses import HTMLResponse, JSONResponse
from starlette.staticfiles import StaticFiles
export_file_url = ‘https://drive.google.com/file/d/12jL-v6gtaSMRT_8S9GfQ-EeCuhtbmVIV/view?usp=sharing’
export_file_name = ‘export.pkl’
classes = [‘rosemary’, ‘thyme’, ‘sage’]
path = Path(file).parent
app = Starlette()
app.add_middleware(CORSMiddleware, allow_origins=[’*’], allow_headers=[‘X-Requested-With’, ‘Content-Type’])
app.mount(’/static’, StaticFiles(directory=‘app/static’))
async def download_file(url, dest):
if dest.exists(): return
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
data = await response.read()
with open(dest, ‘wb’) as f:
f.write(data)
async def setup_learner():
await download_file(export_file_url, path / export_file_name)
try:
learn = load_learner(path, export_file_name)
return learn
except RuntimeError as e:
if len(e.args) > 0 and ‘CPU-only machine’ in e.args[0]:
print(e)
message = “\n\nThis model was trained with an old version of fastai and will not work in a CPU environment.\n\nPlease update the fastai library in your training environment and export your model again.\n\nSee instructions for ‘Returning to work’ at https://course.fast.ai.”
raise RuntimeError(message)
else:
raise
loop = asyncio.get_event_loop()
tasks = [asyncio.ensure_future(setup_learner())]
learn = loop.run_until_complete(asyncio.gather(*tasks))[0]
loop.close()
@app.route(’/’)
async def homepage(request):
html_file = path / ‘view’ / ‘index.html’
return HTMLResponse(html_file.open().read())
@app.route(’/analyze’, methods=[‘POST’])
async def analyze(request):
img_data = await request.form()
img_bytes = await (img_data[‘file’].read())
img = open_image(BytesIO(img_bytes))
prediction = learn.predict(img)[0]
return JSONResponse({‘result’: str(prediction)})
if name == ‘main’:
if ‘serve’ in sys.argv:
uvicorn.run(app=app, host=‘0.0.0.0’, port=5000, log_level=“info”)
