Feat : cors 에러 해결

LEEHYUNBOK · LEEHYUNBOK · commit f97244883805 · 2023-06-14T14:33:06.000+09:00
diff --git a/corsTest.html b/corsTest.html
@@ -0,0 +1,40 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <title>이미지 업로드</title>
+    <script src="https://cdn.jsdelivr.net/npm/axios/dist/axios.min.js"></script>
+</head>
+<body>
+    <h1>이미지 업로드</h1>
+    <input type="file" id="imageInput">
+    <button onclick="uploadImage()">이미지 업로드</button>
+
+    <script>
+        function uploadImage() {
+            var input = document.getElementById('imageInput');
+            var image = input.files[0];
+            var file = new FormData();
+            file.append('file', image);
+            console.log("formData : %o", file);
+            var requestOptions = {
+                method: 'POST',
+                body: file,
+                redirect: 'follow'
+            };
+
+            axios.post('http://127.0.0.1:8000/photo', file)
+                .then(function (response) {
+                    // 업로드에 성공한 경우
+                    console.log(response.data);
+                    // 응답 데이터를 처리하거나 화면에 표시합니다.
+                })
+                .catch(function (error) {
+                    // 업로드에 실패한 경우
+                    console.log("에러네..");
+                    console.log(error);
+                    // 에러 처리 로직을 추가합니다.
+                });
+        }
+    </script>
+</body>
+</html>
diff --git a/main.py b/main.py
@@ -4,14 +4,28 @@
 from PIL import Image
 import torch
 import os
+from fastapi.middleware.cors import CORSMiddleware
 
 from loguru import logger
 
 
 model = torch.load('shufflenet_weight.pt', map_location=torch.device('cpu'))
 
+
 app = FastAPI()
 
+origins = [
+    "*"
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
 
 @app.post("/photo")
 async def create_upload_file(file: UploadFile = File(...)):
@@ -28,6 +42,10 @@ async def create_upload_file(file: UploadFile = File(...)):
   # image = ToTensor()(Image.open(file.file))
   # output = model(image)
   # logger.info("Classify Result = {}", output)
+  print({
+      "content_type": file.content_type,
+      "filename": file.filename
+  })
   return {
       "content_type": file.content_type,
       "filename": file.filename
