actually classify images

app.py

@@ -1,7 +1,41 @@
 import gradio as gr
+import torch
+from torchvision import transforms
+from PIL import Image
+import numpy as np
 
-def greet(name):
-    return "Bonjour " + name + "!!"
+np.int = int
+
+
+model = torch.hub.load('nicolalandro/ntsnet-cub200', 'ntsnet', pretrained=True,
+                       **{'topN': 6, 'device':'cpu', 'num_classes': 200})
+
+model.eval()
+
+def classify_bird(img):
+
+    transform_test = transforms.Compose([
+        transforms.Resize((600, 600), Image.BILINEAR),
+        transforms.CenterCrop((448, 448)),
+        # transforms.RandomHorizontalFlip(),  # only if train
+        transforms.ToTensor(),
+        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+    ])
+
+    scaled_img = transform_test(img)
+    torch_images = scaled_img.unsqueeze(0)
+
+    with torch.no_grad():
+        top_n_coordinates, concat_out, raw_logits, concat_logits, part_logits, top_n_index, top_n_prob = model(torch_images)
+
+        _, predict = torch.max(concat_logits, 1)
+        pred_id = predict.item()
+        bird_class = model.bird_classes[pred_id]
+        print(f"{bird_class=}")
+
+    return bird_class
+
+image_component = gr.Image(type="pil", label="Bird Image")
+demo = gr.Interface(fn=classify_bird, inputs=image_component, outputs="text")
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 demo.launch()