changing the dir struct

scripts_and_models/app.py → app.py

@@ -24,7 +24,7 @@ def predict(img) -> Tuple[Dict, float]:
     """Transforms and performs a prediction on img and returns prediction and time taken.
     """
 
-    print("---img path is: ",img)
+    #print("---img path is: ",img)
     start_time = timer()    
     model.to("cpu")
     model.eval()
@@ -46,7 +46,7 @@ title = "Meme classifiication"
 description = "An EfficientNetB2 model to classify images of food into 2 classes:meme and non-meme"
 
 
-example_list = ["../example_imgs/"+i for i in os.listdir("../example_imgs")]
+example_list = ["./example_imgs/"+i for i in os.listdir("./example_imgs")]
 #print(example_list)
 
 demo = gr.Interface(

scripts_and_models/inference.py

@@ -1,61 +0,0 @@
-from typing import List, Tuple
-from PIL import Image
-import torch
-import torchvision
-from torchvision import datasets, transforms
-import matplotlib.pyplot as plt
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-
-def pred_and_plot_image(model: torch.nn.Module,
-                        image_path: str, 
-                        class_names: List[str],
-                        image_size: Tuple[int, int] = (224, 224),
-                        transform: torchvision.transforms = None,
-                        device: torch.device=device):
-    
-
-    img = Image.open(image_path)
-
-    if transform is not None:
-        image_transform = transform
-    else:
-        image_transform = transforms.Compose([
-            transforms.Resize(image_size),
-            transforms.ToTensor(),
-            transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                                 std=[0.229, 0.224, 0.225]),
-        ])
-
-
-    model.to(device)
-    model.eval()
-    with torch.inference_mode():
-      transformed_image = image_transform(img).unsqueeze(dim=0)
-      target_image_pred = model(transformed_image.to(device))
-    target_image_pred_probs = torch.softmax(target_image_pred, dim=1)
-    target_image_pred_label = torch.argmax(target_image_pred_probs, dim=1)
-
-    plt.figure()
-    plt.imshow(img)
-    plt.title(f"Pred: {class_names[target_image_pred_label]} | Prob: {target_image_pred_probs.max():.3f}")
-    plt.axis(False);
-    plt.show()
-    
-    
-    
-    
-from pathlib import Path    
-
-model_path=Path("efficientNet_clf.pt")
-print(model_path)
-
-model = torch.jit.load(model_path)
-
-class_names=['meme', 'non-meme']
-
-pred_and_plot_image(model=model,
-                    image_path="../example_imgs/meme.png",
-                    class_names=class_names)