Json error while trying to run locally

Part 1 (2019)
1

Hi there,


I am using the guide above to try to deploy my deep learning model for the first time to Heroku. The homepage loads just fine. However, I am getting the following error when I try to actually analyze the image:
“json: cannot unmarshal array into Go value of type types.ContainerJSON”

I’m not sure how to fix this error and would appreciate your help in solving it. Thank you in advance!
Here’s the code:

from starlette.applications import Starlette
from starlette.responses     import JSONResponse, HTMLResponse, RedirectResponse
    #from fastai.vision import (
    #    ImageDataBunch,
    #    create_cnn,
    #    open_image,
    #    get_transforms,
    #    models,
    #)
    from fastai import *
    from fastai.vision import *

import torch
from pathlib import Path
from io import BytesIO
import sys
import uvicorn
import aiohttp
import asyncio
import os
import json
import requests
import base64
from PIL import Image as PILImage


async def get_bytes(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.read()
def encode(img):
    img = (image2np(img.data) * 255).astype('uint8')
    pil_img = PILImage.fromarray(img)
    buff = BytesIO()
    pil_img.save(buff, format="JPEG")
    return base64.b64encode(buff.getvalue()).decode("utf-8")

app = Starlette()
path = Path(__file__).parent
path2 = Path('model-weights.pth')
classes = ['sedan', 'suv']
learner = load_learner(path,path/path2)

@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):
    img = open_image(BytesIO(bytes))
    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
        )
    img_data = encode(img)
    return HTMLResponse(
        """
        <html>
           <body>
             <p>Prediction: <b>%s</b></p>
             <p>Confidence: %s</p>
           </body>
        <figure class="figure">
          <img src="data:image/png;base64, %s" class="figure-img img-thumbnail input-image">
        </figure>
        </html>
    """ %(pred_class.upper(), pred_probs, img_data))

@app.route("/")
def form(request):
    return HTMLResponse(
        """
        <h1>Healthy Or Not !</h1>
        <p>Find out if the vehicle is a sedan or suv. Upload image or specify URL.</p><br>
        <form action="/upload" method="post" enctype="multipart/form-data">
            <u>Select image to upload:</u><br><p>
            1. <input type="file" name="file"><br><p>
            2. <input type="submit" value="Upload and analyze image">
        </form>
        <br>
        <strong>OR</strong><br><p>
        <u>Submit a URL:</u>
        <form action="/classify-url" method="get">
            1. <input type="url" name="url" size="60"><br><p>
            2. <input type="submit" value="Fetch and analyze image">
        </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)
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