Optimized ResNet50 model loading

app.py

@@ -3,12 +3,53 @@ import glob
 import time
 import random
 
+# Import necessary libraries
+from torchvision import models, transforms
+from PIL import Image
+import torch
+
+# Load pre-trained ResNet model once
+model = models.resnet50(pretrained=True)
+model.eval()
+
+# Define image transformations
+transform = 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]),
+])
+
+# Load class labels
+with open('imagenet_classes.txt') as f:
+    labels = [line.strip() for line in f.readlines()]
+
 def classify_image(image):
     # Wait for a random interval between 0.5 and 1.5 seconds to look useful
-    time.sleep(random.uniform(0.5, 1.5))
+    # time.sleep(random.uniform(0.5, 1.5))
+    print("Classifying image...")
+    
+    # Preprocess the image
+    img = Image.fromarray(image).convert('RGB')
+    img_t = transform(img)
+    batch_t = torch.unsqueeze(img_t, 0)
+
+    # Make prediction
+    with torch.no_grad():
+        output = model(batch_t)
+
+    # Get the predicted class
+    _, predicted = torch.max(output, 1)
+    classification = labels[predicted.item()]
+
+    # Check if the predicted class is a bird
+    bird_classes = ['bird', 'fowl', 'hen', 'cock', 'rooster', 'peacock', 'parrot', 'eagle', 'owl', 'penguin']
+    is_bird = any(bird_class in classification.lower() for bird_class in bird_classes)
 
-    # This function just returns "Not a bird" for any input image
-    return "Not a bird"
+    if is_bird:
+        return f"This is a bird! Specifically, it looks like a {classification}."
+    else:
+        return f"This is not a bird. It appears to be a {classification}."
 
 # Dynamically create the list of example images
 example_files = sorted(glob.glob("examples/*.png"))