Update app.py

Omitted ds completely

app.py

@@ -9,11 +9,11 @@ from PIL import Image
 import numpy as np
 import random
 
-from datasets import load_dataset
-from datasets import DatasetDict
-ds = DatasetDict({
-    "validation": load_dataset("chronopt-research/cropped-vggface2-224", split="validation"),
-})
+# from datasets import load_dataset
+# from datasets import DatasetDict
+# ds = DatasetDict({
+#     "validation": load_dataset("chronopt-research/cropped-vggface2-224", split="validation"),
+# })
 
 
 
@@ -49,22 +49,22 @@ preprocess = transforms.Compose([
     transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), # vggface2
 ])
 
-# Function to make predictions
-def predict(image):
-    if isinstance(image, np.ndarray):
-        image = Image.fromarray(image)  # Convert to PIL Image if i
-    image = preprocess(image).unsqueeze(0)  # Add batch dimension
-    with torch.no_grad():
-        output = model(image)  # Perform inference on CPU
-    _, predicted_class = output.max(1)
-    # Fetch 9 random samples from the predicted class
-    class_samples = ds.filter(lambda example: example['label'] == predicted_class.item())
-
-    sample_images = random.sample(list(class_samples), min(len(class_samples), 9))
+# # Function to make predictions
+# def predict(image):
+#     if isinstance(image, np.ndarray):
+#         image = Image.fromarray(image)  # Convert to PIL Image if i
+#     image = preprocess(image).unsqueeze(0)  # Add batch dimension
+#     with torch.no_grad():
+#         output = model(image)  # Perform inference on CPU
+#     _, predicted_class = output.max(1)
+#     # Fetch 9 random samples from the predicted class
+#     class_samples = ds.filter(lambda example: example['label'] == predicted_class.item())
+
+#     sample_images = random.sample(list(class_samples), min(len(class_samples), 9))
     
-    sample_images_urls = [sample['image'] for sample in sample_images]
+#     sample_images_urls = [sample['image'] for sample in sample_images]
     
-    return f"Predicted class: {predicted_class.item()}", sample_images_urls
+#     return f"Predicted class: {predicted_class.item()}", sample_images_urls
 
 
 # Simplified Generative Inference