AttributeError：部分初始化的模块“keras.src”没有属性“utils”（很可能是由于循环导入）
这是我的导入文件
from flask import Flask, render_template, request, jsonify
from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np
app = Flask(__name__)
# Load the model and labels
model = load_model('/Users/Pratik/PycharmProjects/SIG_Project/keras_model.h55', compile=False)
class_names = open("/Users/Pratik/PycharmProjects/SIG_Project/labels.txt", "r").readlines()
# Set the confidence threshold
confidence_threshold = 0.2
@app.route('/')
def index():
return '''
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Image Classifier</title>
</head>
<body>
<h1>Image Classifier</h1>
<form id="upload-form" enctype="multipart/form-data">
<input type="file" name="file" accept="image/*">
<button type="button" onclick="predict()">Predict</button>
</form>
<div id="result"></div>
<script>
function predict() {
var form = document.getElementById('upload-form');
var formData = new FormData(form);
fetch('/predict', {
method: 'POST',
body: formData
})
.then(response => response.json())
.then(data => {
var resultDiv = document.getElementById('result');
resultDiv.innerHTML = '';
data.forEach(prediction => {
var classDiv = document.createElement('div');
classDiv.innerHTML = `<p>Class: ${prediction.class}</p><p>Confidence Score: ${prediction.confidence.toFixed(8)}</p>`;
resultDiv.appendChild(classDiv);
});
})
.catch(error => console.error('Error:', error));
}
</script>
</body>
</html>
'''
@app.route('/predict', methods=['POST'])
def predict():
# Get image from frontend
file = request.files['file']
# Preprocess the image
image = Image.open(file).convert("RGB")
size = (224, 224)
image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
image_array = np.asarray(image)
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
data[0] = normalized_image_array
# Predict
prediction = model.predict(data)
# Prepare response
response = []
for i in range(len(class_names)):
class_name = class_names[i].strip()
confidence_score = prediction[0][i]
if confidence_score > confidence_threshold:
response.append({
'class': class_name[2:],
'confidence': float(confidence_score)
})
return jsonify(response)
if __name__ == '__main__':
app.run(debug=True)
#我正在 jupyter 中运行此代码
您可以发布错误回溯吗？本来会添加评论而不是答案，但我没有足够的学分。新帐户。