部署深度学习模型时出错（JSONResponse错误（？））

Question

我正在尝试使用以下存储库-https://github.com/render-examples/fastai-v3部署深度学习模型。我能够将其部署到Internet上，但是经过修改后，它不起作用。

这是两个文件： Client.js

var el = x => document.getElementById(x);

function showPicker() {
  el("file-input").click();
}

function showPicked(input) {
  el("upload-label").innerHTML = input.files[0].name;
  var reader = new FileReader();
  reader.onload = function(e) {
    el("image-picked").src = e.target.result;
    el("image-picked").className = "";
  };
  reader.readAsDataURL(input.files[0]);
}

function analyze() {
  var uploadFiles = el("file-input").files;
  if (uploadFiles.length !== 1) alert("Please select a file to analyze!");

  el("analyze-button").innerHTML = "Finding out...";
  var xhr = new XMLHttpRequest();
  var loc = window.location;
  xhr.open("POST", `${loc.protocol}//${loc.hostname}:${loc.port}/analyze`,
    true);
  xhr.onerror = function() {
    alert(xhr.responseText);
  };
  xhr.onload = function(e) {
    if (this.readyState === 4) {
        var response = JSON.parse(JSON.stringify(e.target.responseText));
      el("result-label").innerHTML = `inf = ${response["inf"]}`;

    }
    el("analyze-button").innerHTML = "Analyze";
  };

  var fileData = new FormData();
  fileData.append("file", uploadFiles[0]);
  xhr.send(fileData);
}

和Server.py

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://www.googleapis.com/drive/v3/files/1-6ACEO2XCVWtHDM84fCAmspkx99zpKbA?alt=media&key=AIzaSyCZRHQRQoWYvCCzyax5lxAdrNyfP-nibSo'
export_file_name = 'New_P100.pkl'

classes = ['dipstation', 'Battle', 'BenchPress', 'InclineBenchPress', 'HammerStrengthmachine', 'LatPullDownMachine', 'PecDeckMachine', 'PullupBar', 'DumbBells', 'tricepbars', 'PreacherBench', 'HandgripExerciser', 'reversehyper', 'Plyometric', 'airresistance', 'Stair', 'Ankle', 'LegCurlMachine', 'LegPressMachine', 'LegExtensionMachine', 'HackSquatMachine', 'CalfMachines', 'LegAbductionAbductionMachine', 'prowler', 'Mini', 'Inversion', 'Vibration', 'PowerRack', 'MaxiClimber', 'StretchingMachine', 'SmithMachine', 'Suspension', 'CablesandPulleys', 'KettleBells', 'Roman', 'AbdominalBench', 'AbCoaster', 'Stationary', 'CruiserBikes', 'FixieBikes', 'MountainBike', 'RecumbentBikes', 'RoadBikes', 'SpinBikes', 'Comfort', 'Treadmill', 'Mini_Exercise_\ufeffBikes', 'metalplates', 'Medicine', 'Pedometers', 'Pull', 'BloodGlucoseMeter', 'GPSWatches', 'GymnasticsGrips&Gloves', 'hoverboard', 'JumpRope', 'ResistanceBand', 'YogaMat', 'Fitness', 'barbells', 'WallBall', 'FoamRoller', 'Stabilityball', 'AgilityLadder', 'BalanceBoards', 'BalanceBoards']
path = Path(__file__).parent

info = { "KettleBells" : "Kettle Balls are used for the muscles"}

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]
    prediction = learn.predict(img)[0]
    result = info[str(prediction)]
    return JSONResponse({'inf': str(result)})



if __name__ == '__main__':
    if 'serve' in sys.argv:
        uvicorn.run(app=app, host='0.0.0.0', port=5000, log_level="info")

我相信，这就是问题所在：

result = info[str(prediction)]
    return JSONResponse({'inf': str(result)})

现在，当我部署模型时，得到以下结果：

inf =未定义

应该是： inf =肌肉使用水壶球

我不知道为什么会出错。感谢您的帮助！

PS：您也可以在此处查看代码：https://github.com/cabhijith/Gym-Trainer/tree/master/app 另外，这是在线应用程序。通常，您可以在此处查看错误- https://gym-trainer.onrender.com/

PPS：我知道我不应该在SO上发布整个代码，但是由于我不知道错误发生在哪里，所以我无法发布代码以产生特定错误：）