changes in structure

README.md

@@ -1,9 +1,11 @@
 ---
+library_name: pytorch
 tags:
 - image-classification
 - mnist
 - pytorch
-license: mit
+license: apache-2.0
+pipeline_tag: image-classification
 ---
 
 # MNIST Digit Classifier

config.json

@@ -0,0 +1,5 @@
+{
+    "architectures": ["DigitClassifier"],
+    "model_type": "pytorch",
+    "num_labels": 10
+  }

scripts/evaluate.py

@@ -4,6 +4,6 @@ if __name__ == "__main__":
     # Evaluate
     metrics = evaluate_model()
     print(f"Test Accuracy: {metrics['accuracy']:.2%}")
-    
+
     # Upload results to Hub
-    upload_results()
+    upload_results()

scripts/train.py

@@ -1,4 +1,4 @@
 from symmetric_test.train import train
 
 if __name__ == "__main__":
-    train()
+    train()

symmetric_test/evaluate.py

@@ -8,34 +8,31 @@ from pathlib import Path
 from huggingface_hub import HfApi
 from .model import DigitClassifier
 
+
 def evaluate_model(model_path="model_weights.pth", output_dir="results"):
     # Setup
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     Path(output_dir).mkdir(exist_ok=True)
-    
+
     # Load model
     model = DigitClassifier()
     model.load_state_dict(torch.load(model_path))
     model.to(device)
     model.eval()
-    
+
     # Data (MNIST Test Set)
-    transform = transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize((0.1307,), (0.3081,))
-    ])
+    transform = transforms.Compose(
+        [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+    )
     test_set = datasets.MNIST(
-        root='./data', 
-        train=False,
-        download=True, 
-        transform=transform
+        root="./data", train=False, download=True, transform=transform
     )
     test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, shuffle=False)
-    
+
     # Evaluation
     all_preds = []
     all_targets = []
-    
+
     with torch.no_grad():
         for images, labels in test_loader:
             images = images.to(device)
@@ -43,38 +40,40 @@ def evaluate_model(model_path="model_weights.pth", output_dir="results"):
             _, preds = torch.max(outputs, 1)
             all_preds.extend(preds.cpu().numpy())
             all_targets.extend(labels.cpu().numpy())
-    
+
     # Metrics
     metrics = {
         "accuracy": np.mean(np.array(all_preds) == np.array(all_targets)),
-        "classification_report": classification_report(all_targets, all_preds, output_dict=True),
-        "confusion_matrix": confusion_matrix(all_targets, all_preds).tolist()
+        "classification_report": classification_report(
+            all_targets, all_preds, output_dict=True
+        ),
+        "confusion_matrix": confusion_matrix(all_targets, all_preds).tolist(),
     }
-    
+
     # Save metrics
-    with open(Path(output_dir)/"metrics.json", "w") as f:
+    with open(Path(output_dir) / "metrics.json", "w") as f:
         json.dump(metrics, f, indent=2)
-    
+
     # Plot confusion matrix
     plt.figure(figsize=(10, 8))
-    plt.imshow(metrics["confusion_matrix"], cmap='Blues')
+    plt.imshow(metrics["confusion_matrix"], cmap="Blues")
     plt.colorbar()
     plt.title("Confusion Matrix")
     plt.xlabel("Predicted")
     plt.ylabel("True")
     plt.xticks(range(10))
     plt.yticks(range(10))
-    plt.savefig(Path(output_dir)/"confusion_matrix.png")
+    plt.savefig(Path(output_dir) / "confusion_matrix.png")
     plt.close()
-    
+
     return metrics
 
+
 def upload_results(repo_id="SupremoUGH/symmetric_test"):
     api = HfApi()
     api.upload_folder(
         folder_path="results",
         path_in_repo="evaluation",
         repo_id=repo_id,
-        repo_type="model"
+        repo_type="model",
     )
-

symmetric_test/model.py

@@ -1,5 +1,6 @@
 import torch.nn as nn
 
+
 class DigitClassifier(nn.Module):
     def __init__(self):
         super().__init__()
@@ -9,15 +10,13 @@ class DigitClassifier(nn.Module):
             nn.MaxPool2d(2),
             nn.Conv2d(32, 64, 3),
             nn.ReLU(),
-            nn.MaxPool2d(2)
+            nn.MaxPool2d(2),
         )
         self.classifier = nn.Sequential(
-            nn.Linear(64*5*5, 128),
-            nn.ReLU(),
-            nn.Linear(128, 10)
+            nn.Linear(64 * 5 * 5, 128), nn.ReLU(), nn.Linear(128, 10)
         )
-    
+
     def forward(self, x):
         x = self.conv_block(x)
         x = x.view(x.size(0), -1)
-        return self.classifier(x)
+        return self.classifier(x)

symmetric_test/train.py

@@ -10,24 +10,24 @@ BATCH_SIZE = 64
 EPOCHS = 5
 LR = 0.001
 
+
 def train():
     # Initialize
     model = DigitClassifier()
     optimizer = optim.Adam(model.parameters(), lr=LR)
     criterion = nn.CrossEntropyLoss()
-    
+
     # Data
-    transform = transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize((0.1307,), (0.3081,))
-    ])
-    
+    transform = transforms.Compose(
+        [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+    )
+
     train_loader = torch.utils.data.DataLoader(
-        datasets.MNIST('./data', train=True, download=True, transform=transform),
+        datasets.MNIST("./data", train=True, download=True, transform=transform),
         batch_size=BATCH_SIZE,
-        shuffle=True
+        shuffle=True,
     )
-    
+
     # Training loop
     for epoch in range(EPOCHS):
         model.train()
@@ -37,16 +37,24 @@ def train():
             loss = criterion(output, target)
             loss.backward()
             optimizer.step()
-    
+
     # Save artifacts
     torch.save(model.state_dict(), "model_weights.pth")
-    
+    torch.save(model, "pytorch_model.bin")  # Standard HF naming
+
     # Upload to Hub
-api = HfApi()
-api.upload_file(
-    path_or_fileobj="model_weights.pth",
-    path_in_repo="model_weights.pth",
-    repo_id="SupremoUGH/symmetric_test",
-    repo_type="model",
-    token=True  # Explicitly use your credentials
-)
+    api = HfApi()
+    api.upload_file(
+        path_or_fileobj="model_weights.pth",
+        path_in_repo="model_weights.pth",
+        repo_id="SupremoUGH/symmetric_test",
+        repo_type="model",
+        token=True,  # Explicitly use your credentials
+    )
+    api.upload_file(
+        path_or_fileobj="pytorch_model.bin",
+        path_in_repo="pytorch_model.bin",
+        repo_id="SupremoUGH/symmetric_test",
+        repo_type="model",
+        token=True,  # Explicitly use your credentials
+    )