initial commit

.idea/$PROJECT_FILE$

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="AnalysisProjectProfileManager">
+    <option name="PROJECT_PROFILE" />
+    <option name="USE_PROJECT_LEVEL_SETTINGS" value="false" />
+    <list size="0" />
+  </component>
+  <component name="SuppressionsComponent">
+    <option name="suppComments" value="[]" />
+  </component>
+</project>
\ No newline at end of file

.idea/.gitignore

+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="CheckstyleConfigurable">
+    <option name="suppFilterFilename" value="" />
+    <option name="suppCommentFilter" value="false" />
+    <option name="offComment" value="CHECKSTYLE\:OFF" />
+    <option name="onComment" value="CHECKSTYLE\:ON" />
+    <option name="checkFormat" value=".*" />
+    <option name="messageFormat" value="" />
+    <option name="checkCPP" value="true" />
+    <option name="checkC" value="true" />
+    <option name="suppNearbyCommentFilter" value="false" />
+    <option name="snCommentFormat" value="SUPPRESS CHECKSTYLE (\w+)" />
+    <option name="snCheckFormat" value="$1" />
+    <option name="snMessageFormat" value="" />
+    <option name="snInfluenceFormat" value="0" />
+    <option name="snCheckCPP" value="true" />
+    <option name="snCheckC" value="true" />
+    <option name="pathToUserRulesConfiguration" value="" />
+    <option name="pathToJarWithRules" value="" />
+  </component>
+  <component name="FindBugsConfigurable">
+    <option name="make" value="true" />
+    <option name="effort" value="default" />
+    <option name="priority" value="Medium" />
+    <option name="excludeFilter" value="" />
+  </component>
+  <component name="GOROOT" url="file:///usr/local/go" />
+  <component name="ProjectRootManager" version="2" languageLevel="JDK_11" project-jdk-name="Python 3.11" project-jdk-type="Python SDK">
+    <output url="file://$PROJECT_DIR$/out" />
+  </component>
+  <component name="SuppressionsComponent">
+    <option name="suppComments" value="[]" />
+  </component>
+</project>
\ No newline at end of file

.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/image.iml" filepath="$PROJECT_DIR$/image.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

README

+1.安装必要依赖
+pip install -r requirements.txt
+
+
+2.运行服务
+python app.py
+
+
+3.测试接口： 使用工具（如 Postman 或 curl）测试
+curl --location --request POST 'http://127.0.0.1:5001/predict' \
+--header 'Content-Type: application/json' \
+--header 'Signature: "just for test"' \
+--header 'TimeStamp: 1733802424' \
+--data-raw '{
+    "url": "https://dianlv-res.oss-cn-shenzhen.aliyuncs.com/exportFiles/1_265261960876529885.jpg",
+    "tenantID": 1
+}'
\ No newline at end of file

app.py

+from flask import Flask, request, jsonify
+from model import load_model, predict
+from PIL import Image
+import requests
+from io import BytesIO
+
+app = Flask(__name__)
+
+# 加载模型
+model, preprocess, device = load_model()
+
+
+@app.route('/predict', methods=['POST'])
+def classify_image():
+
+    # 获取请求头
+    _s = request.headers.get('Signature')
+    _t = request.headers.get('TimeStamp')
+    if not _s or not _t:
+        return jsonify({"error": "Missing required headers '_s' or '_t'"}), 400
+
+
+    # 从请求中获取图片 URL
+    data = request.json
+    if 'url' not in data:
+        return jsonify({'error': 'Image URL is required'}), 400
+
+    image_url = data['url']
+    try:
+        # 下载图片并加载为Pillow Image
+        response = requests.get(image_url)
+        response.raise_for_status()
+        input_image = Image.open(BytesIO(response.content))
+    except Exception as e:
+        return jsonify({'error': f'Failed to load image: {str(e)}'}), 400
+
+    # 执行推理
+    try:
+        result, inference_time = predict(model, preprocess, device, input_image)
+        return jsonify({
+            'succses': True,
+            'code': 0,
+            'message': "",
+            'data':
+                {
+                    'predicted_class': result['predicted_class'],
+                    'probabilities': result['probabilities'],
+                    'inference_time': inference_time
+                }
+        })
+    except Exception as e:
+        return jsonify({'error': f'Prediction failed: {str(e)}'}), 500
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5001)

image.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager" inherit-compiler-output="true">
+    <exclude-output />
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file

model.py

+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+import time
+
+
+def load_model():
+    # 定义预处理步骤
+    preprocess = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+
+    # 加载模型
+    model = models.resnet18(weights="IMAGENET1K_V1")
+    model.fc = nn.Linear(model.fc.in_features, 2)  # 修改为2分类
+    model.load_state_dict(torch.load('./pth/trained_model.pth', map_location=torch.device('cpu')))
+    model.eval()
+
+    # 使用CPU或GPU
+    device = torch.device('cpu')
+    model = model.to(device)
+
+    return model, preprocess, device
+
+
+def predict(model, preprocess, device, input_image):
+    # 预处理图像
+    input_tensor = preprocess(input_image)
+    input_batch = input_tensor.unsqueeze(0).to(device)
+
+    # 测量推理时间
+    start_time = time.time()
+    with torch.no_grad():
+        output = model(input_batch)
+        probabilities = torch.softmax(output, dim=1)
+    end_time = time.time()
+
+    # 获取预测结果
+    _, predicted = torch.max(output, 1)
+    result = {
+        'predicted_class': predicted.item(),
+        'probabilities': [prob.item() for prob in probabilities[0]]
+    }
+    inference_time = end_time - start_time
+    return result, inference_time

requirements.txt

+flask
+torch
+torchvision
+pillow
+requests