Merge pull request 'Flask API' (#2) from flask-ML into dev

Reviewed-on: #2

cat_detection.py

@@ -0,0 +1,48 @@
+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

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

main.py

@@ -1,59 +1,38 @@
-from PIL import Image
+from flask import Flask, request, jsonify, session
-import torch
-import torch.nn.functional as F
-from torchvision.models.resnet import resnet50, ResNet50_Weights
-from torchvision.transforms import transforms
 
-# Load the pre-trained model
+from cat_detection import is_cat
-model = resnet50(weights=ResNet50_Weights.DEFAULT)
 
-model.eval()
+# Define flask app
-
+app = Flask(__name__)
-# Define the image transformations
+app.secret_key = 'secret_key'
-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_path):
+@app.route('/detect-cat', methods=['POST'])
-    # Open the image
+def upload_file():
-    img = Image.open(image_path)
+    # 'Key' in body should be named as 'image'. Type should be 'File' and in 'Value' we should upload image from disc.
+    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)
 
-    # Preprocess the image
+    # Save result in session
-    img_t = preprocess(img)
+    session['result'] = max_class, max_prob
-    batch_t = torch.unsqueeze(img_t, 0)
 
-    # Make the prediction
+    # Tworzenie komunikatu na podstawie wyniku analizy zdjęcia
-    out = model(batch_t)
+    translator = {
-
-    # 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:
-    print(f"The image is recognized as '{translator[max_class]}' with a probability of {round(max_prob * 100, 2)}%")
+        result = f"The image is recognized as '{translator[max_class]}' with a probability of {round(max_prob * 100, 2)}%"
-else:
+    else:
-    print(f"The image is not recognized as a class within the range 281-285 ({max_class})")
+        result = 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)