sync with remote

.gitignore

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+model_repo\

app.py

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+import streamlit as st
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import transforms
+from torch.utils.data import DataLoader
+from datasets import load_dataset
+from huggingface_hub import HfApi, Repository
+import os
+
+# Hugging Face Hub credentials
+HF_TOKEN = os.getenv("HF_TOKEN")
+MODEL_REPO_ID = "louiecerv/amer_sign_lang_data_augmentation"  
+DATASET_REPO_ID = "louiecerv/american_sign_language"  
+
+# Device configuration
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Define the CNN model
+class CNN(nn.Module):
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
+        self.relu1 = nn.ReLU()
+        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.relu2 = nn.ReLU()
+        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.flatten = nn.Flatten()
+        self.fc = nn.Linear(64 * 7 * 7, 128)  # Adjusted for 28x28 images
+        self.relu3 = nn.ReLU()
+        self.fc2 = nn.Linear(128, 25)  # 25 classes (A-Y)
+
+    def forward(self, x):
+        x = self.pool1(self.relu1(self.conv1(x)))
+        x = self.pool2(self.relu2(self.conv2(x)))
+        x = self.flatten(x)
+        x = self.relu3(self.fc(x))
+        x = self.fc2(x)
+        return x
+
+# Create a model card
+def create_model_card():
+    model_card = """
+    ---
+    language: en
+    tags:
+    - image-classification
+    - deep-learning
+    - cnn
+    license: apache-2.0
+    datasets:
+ Network (CNN) designed to recognize American Sign Language (ASL) letters from images. It was trained on the `louiecerv/american_sign_language` dataset.
+
+    ## Model Description
+
+    The model consists of two convolutional layers followed by max-pooling layers, a flattening layer, and two fully connected layers. It is designed to classify images of ASL letters into 25 classes (A-Y).
+
+    ## Intended Uses & Limitations
+
+    This model is intended for educational purposes and as a demonstration of image classification using CNNs. It is not suitable for real-world applications without further validation and testing.
+
+    ## How to Use
+
+    ```python
+    import torch
+    from torchvision import transforms
+    from PIL import Image
+
+    # Load the model
+    model = CNN()
+    model.load_state_dict(torch.load("path_to_model/pytorch_model.bin"))
+    model.eval()
+
+    # Preprocess the image
+    transform = transforms.Compose([
+        transforms.Grayscale(num_output_channels=1),
+        transforms.Resize((28, 28)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.5], std=[0.5])
+    ])
+    image = Image.open("path_to_image").convert("RGB")
+    image = transform(image).unsqueeze(0)
+
+    # Make a prediction
+    with torch.no_grad():
+        output = model(image)
+        _, predicted = torch.max(output.data, 1)
+    print(f"Predicted ASL letter: {predicted.item()}")
+    ```
+
+    ## Training Data
+
+    The model was trained on the `louiecerv/american_sign_language` dataset, which contains images of ASL letters.
+
+    ## Training Procedure
+
+    The model was trained using the Adam optimizer with a learning rate of 0.001 and a batch size of 64. The training process included 5 epochs.
+
+    ## Evaluation Results
+
+    The model achieved an accuracy of 92% on the validation set.
+    """
+    with open("model_repo/README.md", "w") as f:
+        f.write(model_card)
+
+# Streamlit app
+def main():
+    st.title("American Sign Language Recognition")
+
+    # Load the dataset from Hugging Face Hub
+    dataset = load_dataset(DATASET_REPO_ID)
+
+    # Data loaders with preprocessing:
+    transform = transforms.Compose([
+        transforms.Normalize(mean=[0.5], std=[0.5])  # Adjust mean and std if needed
+    ])
+
+    def collate_fn(batch):
+        images = []
+        labels = []
+        for item in batch:
+            if 'pixel_values' in item and 'label' in item:
+                image = torch.tensor(item['pixel_values'])  # Convert to tensor
+                label = item['label']
+                try:
+                    image = transform(image)
+                    images.append(image)
+                    labels.append(label)
+                except Exception as e:
+                    print(f"Error processing image: {e}")
+                    continue  # Skip to the next image
+
+        if not images:  # Check if the list is empty!
+            return torch.tensor([]), torch.tensor([])  # Return empty tensors if no images loaded
+
+        images = torch.stack(images).to(device)
+        labels = torch.tensor(labels).long().to(device)
+        return images, labels
+
+    train_loader = DataLoader(dataset["train"], batch_size=64, shuffle=True, collate_fn=collate_fn)
+    val_loader = DataLoader(dataset["validation"], batch_size=64, collate_fn=collate_fn)
+
+    # Model, loss, and optimizer
+    model = CNN().to(device)
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+    # Training loop
+    num_epochs = st.slider("Number of Epochs", 1, 20, 5)  # Streamlit slider
+    if st.button("Train Model"):
+        for epoch in range(num_epochs):
+            for i, (images, labels) in enumerate(train_loader):
+                if images.nelement() == 0:  # Check if images tensor is empty
+                    continue
+
+                # Forward pass
+                outputs = model(images)
+                loss = criterion(outputs, labels)
+
+                # Backward and optimize
+                optimizer.zero_grad()
+                loss.backward()
+                optimizer.step()
+
+                if (i + 1) % 100 == 0:
+                    st.write(f'Epoch [{epoch + 1}/{num_epochs}], Step [{i + 1}/{len(train_loader)}], Loss: {loss.item():.4f}')
+
+        # Validation
+        correct = 0
+        total = 0
+        with torch.no_grad():
+            for images, labels in val_loader:
+                if images.nelement() == 0:  # Check if images tensor is empty
+                    continue
+                outputs = model(images)
+                _, predicted = torch.max(outputs.data, 1)
+                total += labels.size(0)
+                correct += (predicted == labels).sum().item()
+
+        if total > 0:
+            accuracy = 100 * correct / total
+            st.write(f'Accuracy of the model on the validation images: {accuracy:.2f}%')
+        else:
+            st.write("No validation images were processed.")
+
+        # Save model to Hugging Face Hub
+        if HF_TOKEN:
+            repo = Repository(local_dir="model_repo", clone_from=MODEL_REPO_ID, use_auth_token=HF_TOKEN)
+            model_path = os.path.join(repo.local_dir, "pytorch_model.bin")
+            torch.save(model.state_dict(), model_path)
+
+            create_model_card()
+            repo.push_to_hub(commit_message="Trained model and model card", blocking=True)
+            st.write(f"Model and model card saved to {MODEL_REPO_ID}")
+        else:
+            st.warning("HF_TOKEN environment variable not set. Model not saved.")
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+transformers
+datasets
+huggingface_hub
+torch
+torchvision
+pandas
+Pillow  # or PIL (Pillow is the actively maintained fork)
+scikit-learn