def predict_url():
url_link = request.args['url_link']
url_image = requests.get(url_link)
image_bytes = url_image.content
class_id, class_name = get_prediction(image_bytes=image_bytes)
class_name = format_class_name(class_name)
return render_template('result.html', class_name=class_name)
def predictResNet50():
if request.method == 'POST':
file_path = get_file_path_and_save(request)
im = Image.open(file_path)
pred_class, class_name = get_prediction(image_bytes=im)
result = format_class_name(class_name)
return result
return None
def upload_file():
if request.method == 'POST':
if 'file' not in request.files:
return redirect(request.url)
file = request.files.get('file')
if not file:
return
img_bytes = file.read()
class_name = get_prediction(image_bytes=img_bytes)
class_name = format_class_name(class_name)
return render_template('result.html', class_name=class_name)
return render_template('index.html')
def upload_file():
if request.method == 'POST':
text = request.form.get('textbox')
print('text is :', text)
# if 'file' not in request.files:
# return redirect(request.url)
# file = request.files.get('file')
# if not file:
# return
#img_bytes = file.read()
img_bytes = text
# class_id, class_name = get_prediction(image_bytes=img_bytes)
reply = get_prediction(image_bytes=img_bytes)
class_name = format_class_name(reply)
return render_template('result.html', class_name=class_name)
# class_name=class_name)
return render_template('index.html')