Merge pull request #25 from MLOps-essi-upc/api_tests

Api tests

src/app/backend/api.py

@@ -12,41 +12,65 @@
 from src import MODELS_DIR, API_DIR
 from ultralytics import YOLO
 import os
+import asyncio
 model_wrappers_list: List[dict] = []
 
 # Define application
 app = FastAPI(
     title="Where is Wally",
-    description="lorem ipsum",
+    description="Upload an image and we will help you to find Wally",
     version="0.1",
 )
 
 
 def construct_response(f):
-    """Construct a JSON response for an endpoint's results."""
-
     @wraps(f)
-    def wrap(request: Request, *args, **kwargs):
-        results = f(request, *args, **kwargs)
-
-        # Construct response
-        response = {
-            "message": results["message"],
-            "method": request.method,
-            "status-code": results["status-code"],
-            "timestamp": datetime.now().isoformat(),
-            "url": request.url._url,
-        }
-
-        # Add data
-        if "data" in results:
-            response["data"] = results["data"]
-
-        return response
+    async def wrap(request: Request, *args, **kwargs):
+        try:
+            if asyncio.iscoroutinefunction(f):
+                results = await f(request, *args, **kwargs)
+            else:
+                results = f(request, *args, **kwargs)
+
+            # Default status code
+            status_code = results.get("status-code", HTTPStatus.OK)
+
+            response = {
+                "message": results.get("message", status_code.phrase),
+                "method": request.method,
+                "status-code": status_code,
+                "timestamp": datetime.now().isoformat(),
+                "url": request.url._url,
+                "data": results.get("data", {}),
+                "found": results.get("found", None)
+            }
+
+            # Include additional keys if present
+            for key in ['boxes', 'conf', 'encoded_img']:
+                if key in results:
+                    response[key] = results[key]
+
+            return response
+
+        except HTTPException as http_exc:
+            # Forward HTTP exceptions as they are
+            raise http_exc
+
+        except Exception as exc:
+            # Handle other exceptions
+            return {
+                "message": "An error occurred",
+                "method": request.method,
+                "status-code": HTTPStatus.INTERNAL_SERVER_ERROR,
+                "timestamp": datetime.now().isoformat(),
+                "url": request.url._url,
+                "detail": str(exc),
+            }
 
     return wrap
 
 
+
 @app.on_event("startup")
 def _load_models():
     """Loads all pickled models found in `MODELS_DIR` and adds them to `models_list`"""
@@ -61,7 +85,6 @@ def _load_models():
         with open(path, "rb") as file:
             # model_wrapper = pickle.load(file)
             # model_wrappers_list.append(model_wrapper)
-            print("file",str(file))
             model_wrapper=dict()
             model = YOLO(path)
             model_wrapper["model"]=model
@@ -78,7 +101,7 @@ def _index(request: Request):
     response = {
         "message": HTTPStatus.OK.phrase,
         "status-code": HTTPStatus.OK,
-        "data": {"message": "Hello world"},
+        "data": {"message": "Welcome to Where is Wally!"},
     }
     return response
 
@@ -109,42 +132,51 @@ def _get_models_list(request: Request, type: str = None):
         }
 
 
-
 @construct_response
 @app.post("/predict/{type}")
 async def _predict(type: str,file: UploadFile = File(...)):
     model_wrapper = next((m for m in model_wrappers_list if m["type"] == type), None)
 
-    if model_wrapper:
-        model=model_wrapper['model']
-        contents = file.file.read()
-        # contents = await file.read()
-        nparr = np.fromstring(contents, np.uint8)
-        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-        results = model.predict(source=img, conf=0.25)
-        boxes=results[0].boxes.xyxy
-        conf=results[0].boxes.conf
-
-        for r in results:
-            annotator = Annotator(img)
-            boxes = r.boxes
-            for box in boxes:
-                b = box.xyxy[0]  # get box coordinates in (top, left, bottom, right) format
-                c = box.cls
-                annotator.box_label(b, model.names[int(c)])
-
-        return_img = annotator.result()  
-
-        # line that fixed it
-        _, encoded_img = cv2.imencode('.PNG', return_img)
-        encoded_img = base64.b64encode(encoded_img)
-
+    if not model_wrapper:
+        raise HTTPException(status_code=HTTPStatus.BAD_REQUEST, detail="Model not found")
+
     else:
-        raise HTTPException(
-            status_code=HTTPStatus.BAD_REQUEST, detail="Model not found"
-        )
-    return{
-        'boxes':boxes,
-        'conf':conf,
-        'encoded_img': encoded_img,
-    }
+        model = model_wrapper['model']
+        contents = await file.read()
+        nparr = np.frombuffer(contents, np.uint8)
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+        if img is None:
+            raise HTTPException(status_code=HTTPStatus.BAD_REQUEST, detail="Invalid image file")
+        else: 
+            results = model.predict(source=img, conf=0.25)
+            boxes = results[0].boxes.xyxy
+            conf = results[0].boxes.conf
+
+            for r in results:
+                annotator = Annotator(img)
+                boxes = r.boxes
+                for box in boxes:
+                    b = box.xyxy[0]  # get box coordinates in (top, left, bottom, right) format
+                    c = box.cls
+                    color = (0, 0, 0)
+                    annotator.box_label(b, model.names[int(c)], color=color)
+
+            return_img = annotator.result()
+
+            _, encoded_img = cv2.imencode('.PNG', return_img)
+            encoded_img = base64.b64encode(encoded_img)
+            is_empty = len(boxes) == 0
+            if(is_empty):
+                return {
+                    'boxes': boxes,
+                    'encoded_img': encoded_img.decode(),
+                    'message': "Processing completed, but Wally was not found in the image.",
+                    'found': False,
+                }
+
+            return {
+                'boxes': boxes,
+                'conf': conf,
+                'encoded_img': encoded_img.decode(),
+                'found': True
+            }

src/app/frontend/web/pages/templates/pages/home.html

@@ -194,7 +194,7 @@ <h1>Where Is Wally?</h1>
             <h2>Select a Model</h2>
             <ul id="model-selection-list">
                 <li class="model-option selected-model" data-keyword="all">Model that finds all characters</li>
-                <li class="model-option" data-keyword="wally">Model that only finds Waldo</li>
+                <li class="model-option" data-keyword="wally">Model that only finds Wally</li>
             </ul>
         </div>
 
@@ -241,16 +241,10 @@ <h2>Select a Model</h2>
         const modelOptions = document.querySelectorAll('.model-option');
         modelOptions.forEach(function(option) {
             option.addEventListener('click', function() {
-                // Remove the selected class from all model options
                 modelOptions.forEach(function(opt) {
                     opt.classList.remove('selected-model');
                 });
-
-            // Add the selected class to the clicked option
             option.classList.add('selected-model');
-
-            // You can also update the server or UI based on the selection here
-            console.log(option.textContent + ' selected');
              });
         });
 
@@ -289,7 +283,7 @@ <h2>Select a Model</h2>
             const selectedModelKeyword = selectedModelElement ? selectedModelElement.dataset.keyword : null;
 
             const imagePreview = document.getElementById('uploaded-image');
-            imagePreview.style.display = 'none'; // Hide the image preview
+            imagePreview.style.display = 'none';
 
 
             // Show spinner
@@ -312,7 +306,11 @@ <h2>Select a Model</h2>
                 })
                 .then(response => {
                     if (!response.ok) {
-                        throw new Error('Network response was not ok');
+                        return response.json().then(errorData => {
+                            const error = new Error(errorData.detail.detail || 'Network response was not ok');
+                            error.isCustomError = true;
+                            throw error;
+                        });
                     }
                     return response.json();
                 }
@@ -321,17 +319,13 @@ <h2>Select a Model</h2>
                     const encodedImg = data.encoded_img;
                     const imagePreview = document.getElementById('uploaded-image');
                     const boxes =  data.boxes;
+                    const found = data.found
 
                     const dataIsEmpty = isDataEmpty(boxes);
-                    if(dataIsEmpty){
-                        document.getElementById('error-message').textContent = "We are very sorry but the model was not able to find Wally. Please try with the other model";
+                    if(!found){
+                        document.getElementById('error-message').textContent = data.message + "Please try the other model.";
                         document.getElementById('error-message').style.display = 'block';
                     }
-
-
-                    console.log("boxes", boxes);
-
-                    console.log('Is data empty:', dataIsEmpty);
                     imagePreview.src = `data:image/png;base64, ${encodedImg}`;
                     imagePreviewContainer.style.display = 'block';
                     imagePreview.style.display = 'block';
@@ -341,13 +335,13 @@ <h2>Select a Model</h2>
 
                 })
                 .catch(error => {
-                    console.error('Error:', error);
-            
+                    const errorMessage = error.isCustomError ? error.message : "An unexpected error occurred.";
+
                     // Hide spinner in case of error as well
                     document.getElementById('spinner').style.display = 'none';
 
-                       // Display error message
-                    document.getElementById('error-message').textContent = error.message;
+                    // Display error message
+                    document.getElementById('error-message').textContent = errorMessage;
                     document.getElementById('error-message').style.display = 'block';
 
                 });

src/app/frontend/web/pages/views.py

@@ -13,25 +13,28 @@
 @csrf_exempt
 def upload_image(request):
     if request.method == 'POST' and request.FILES.get('image'):
+        try:
+            model_request = request.GET.get('model', "all")
+            image = request.FILES['image'].read()
+            data = {
+                'file': image
+            }
+            url= api.PREDICT+model_request
+            response = requests.post(url,files=data)
+
+            if response.status_code != 200:
+                return JsonResponse({
+                    'status': 'error',
+                    'message': f'Error from server: {response.status_code}',
+                    'detail': response.json()
+                }, status=response.status_code, )
+
+            response_data = response.json()
+            return JsonResponse(response_data)
 
-        model_request = request.GET.get('model', "all")
-        print("model_request", model_request)
 
-
-        image = request.FILES['image'].read()
-
-        # API call
-        # TODO: not working the endpoint call 
-        data = {
-            'file': image
-        }
-        url= api.PREDICT+model_request
-        response = requests.post(url,files=data)
-
-        # #convert reponse data into json
-        response = response.json()
-
-        return JsonResponse(response)
+        except requests.exceptions.RequestException as e:
+            return JsonResponse({'status': 'error', 'message': str(e)})
 
     return JsonResponse({'status': 'error'})
 

tests/api_test/pytest.ini

@@ -0,0 +1,3 @@
+[pytest]
+filterwarnings =
+    ignore::DeprecationWarning