def predict():
if (request.method == 'POST'):
file = request.files.get('file')
if file is None or file.filename == "":
return jsonify({'error': 'no file'})
if not allowed_file(file.filename):
return jsonify({'error': 'not supported'})
try:
img_bytes = file.read()
tensor = transform_image(img_bytes)
prediction = get_predication(tensor)
data = {
'prediction': prediction.item(),
'class_name': str(prediction.item())
}
return jsonify(data)
except Exception as e:
print(e)
return jsonify({'error': 'Error during Predictions'})
# 1. Load the image
# 2. image -> tensor
# 3. predication
# 4. return json
return jsonify({'result': 1})
def predict():
if request.method == 'POST':
file = request.files.get('file')
if file is None or file.filename == "":
return jsonify({'error': 'no file'})
if not allowed_file(file.filename):
return jsonify({'error': 'format not supported'})
try:
img_bytes = file.read()
tensor = transform_image(img_bytes)
prediction = get_prediction(tensor)
data = {'prediction': prediction.item(), 'class_name': str(prediction.item())}
return jsonify(data)
except:
return jsonify({'error': 'error during prediction'})
def predick():
if request.method in ['POST', 'PUT']:
file = request.files.get('file')
if file is None or file.filename == "":
return jsonify({'error': 'no file'})
if not allowed_file(file.filename):
return jsonify({'error': 'format not supported'})
try:
img_bytes = file.read()
tensor = transform_image(img_bytes)
prediction = get_prediction(tensor)
probability = "{:.2%}".format(prediction[1].item())
data = {'prediction_id': prediction[0].item(), 'class_name': str(classes[prediction[0].item()]), 'probability': probability}
return jsonify(data)
except Exception as e:
return jsonify({'error': str(e)})
def main_post():
file = request.files.get("image")
print("=" * 80)
print(request.headers)
print(list(request.files.keys()))
print(list(request.form.keys()))
if file is None or file.filename == "":
return jsonify(dict(error="no file"))
if not allowed_file(file.filename):
return jsonify(dict(error="format not supported"))
try:
image_bytes = file.read()
image_tensor = transform_image(image_bytes)
probability, prediction = get_prediction(image_tensor)
data = dict(
probability=probability.item(), prediction=labels[str(prediction.item())]
)
print(data)
return jsonify(data)
except:
return jsonify(dict(error="error during prediction"))