cat_detection.py

@@ -1,48 +0,0 @@
-from io import BytesIO
-
-import torch
-import torch.nn.functional as F
-from PIL import Image
-from torchvision.models.resnet import resnet50, ResNet50_Weights
-from torchvision.transforms import transforms
-
-model = resnet50(weights=ResNet50_Weights.DEFAULT)
-
-model.eval()
-
-# Define the image transformations
-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]),
-])
-
-
-def is_cat(image):
-    try:
-        img = Image.open(BytesIO(image.read()))
-
-        # Preprocess the image
-        img_t = preprocess(img)
-        batch_t = torch.unsqueeze(img_t, 0)
-
-        # Make the prediction
-        out = model(batch_t)
-
-        # Apply softmax to get probabilities
-        probabilities = F.softmax(out, dim=1)
-
-        # Get the maximum predicted class and its probability
-        max_prob, max_class = torch.max(probabilities, dim=1)
-        max_prob = max_prob.item()
-        max_class = max_class.item()
-
-        # Check if the maximum predicted class is within the range 281-285
-        if 281 <= max_class <= 285:
-            return max_class, max_prob
-        else:
-            return max_class, None
-    except Exception as e:
-        print("Error while processing the image:", e)
-        return None

docs.md

@@ -1,9 +0,0 @@
-# Api
-
-Port -> 5000
-
-endpoint -> /detect-cat
-
-Key -> 'Image'
-
-Value -> {UPLOADED_FILE}

main.py

@@ -1,38 +1,59 @@
-from flask import Flask, request, jsonify, session
+from PIL import Image
+import torch
+import torch.nn.functional as F
+from torchvision.models.resnet import resnet50, ResNet50_Weights
+from torchvision.transforms import transforms
 
-from cat_detection import is_cat
+# Load the pre-trained model
+model = resnet50(weights=ResNet50_Weights.DEFAULT)
 
-# Define flask app
+model.eval()
-app = Flask(__name__)
+
-app.secret_key = 'secret_key'
+# Define the image transformations
+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]),
+])
 
 
-@app.route('/detect-cat', methods=['POST'])
+def is_cat(image_path):
-def upload_file():
+    # Open the image
-    # 'Key' in body should be named as 'image'. Type should be 'File' and in 'Value' we should upload image from disc.
+    img = Image.open(image_path)
-    file = request.files['image']
-    if file.filename == '':
-        return jsonify({'error': "File name is empty. Please name a file."}), 400
-    max_class, max_prob = is_cat(file)
 
-    # Save result in session
+    # Preprocess the image
-    session['result'] = max_class, max_prob
+    img_t = preprocess(img)
+    batch_t = torch.unsqueeze(img_t, 0)
 
-    # Tworzenie komunikatu na podstawie wyniku analizy zdjęcia
+    # Make the prediction
-    translator = {
+    out = model(batch_t)
+
+    # Apply softmax to get probabilities
+    probabilities = F.softmax(out, dim=1)
+
+    # Get the maximum predicted class and its probability
+    max_prob, max_class = torch.max(probabilities, dim=1)
+    max_prob = max_prob.item()
+    max_class = max_class.item()
+
+    # Check if the maximum predicted class is within the range 281-285
+    if 281 <= max_class <= 285:
+        return max_class, max_prob
+    else:
+        return max_class, None
+
+
+image_path = 'wolf.jpg'
+max_class, max_prob = is_cat(image_path)
+translator = {
     281: "tabby cat",
     282: "tiger cat",
     283: "persian cat",
     284: "siamese cat",
     285: "egyptian cat"
-    }
+}
-    if max_prob is not None:
+if max_prob is not None:
-        result = f"The image is recognized as '{translator[max_class]}' with a probability of {round(max_prob * 100, 2)}%"
+    print(f"The image is recognized as '{translator[max_class]}' with a probability of {round(max_prob * 100, 2)}%")
-    else:
+else:
-        result = f"The image is not recognized as a class within the range 281-285 ({max_class})"
+    print(f"The image is not recognized as a class within the range 281-285 ({max_class})")
-
-    return jsonify({'result': result}), 200
-
-
-if __name__ == '__main__':
-    app.run(debug=True)