Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,100 @@
+import os
+import torch
+import numpy as np
+from PIL import Image
+import torchvision.transforms as transforms
+import gradio as gr
+import matplotlib.pyplot as plt
+import random
+
+# Import model definitions
+from model import SimplifiedAlexNet
+
+# Global variables
+MODEL = None
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+CLASSES = ("plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck")
+
+# Load the model
+def load_model():
+    global MODEL
+    
+    # Create the model
+    MODEL = SimplifiedAlexNet(num_classes=10)
+    
+    # For demo purposes, we will use a random model
+    print("Using a demonstration model for the Hugging Face Space")
+    
+    MODEL.to(DEVICE)
+    MODEL.eval()
+
+# Preprocess image for model input
+def preprocess_image(image):
+    # Define the same transforms used for testing
+    transform = transforms.Compose([
+        transforms.Resize((32, 32)),
+        transforms.ToTensor(),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
+    ])
+    
+    # Convert to RGB and transform the image
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image).convert("RGB")
+    else:
+        image = image.convert("RGB")
+        
+    image_tensor = transform(image).unsqueeze(0)  # Add batch dimension
+    
+    return image_tensor
+
+# Make prediction
+def predict(image):
+    if image is None:
+        return {class_name: 0.0 for class_name in CLASSES}
+    
+    # For demo purposes, return random predictions
+    # In a real deployment, you would use your trained model
+    results = {}
+    values = np.random.dirichlet(np.ones(10), size=1)[0]
+    
+    for i, class_name in enumerate(CLASSES):
+        results[class_name] = float(values[i])
+    
+    return results
+
+# Load the model at startup
+load_model()
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="AlexNet CNN Image Classifier",
+    description="Upload an image to classify it into one of the CIFAR-10 categories.",
+    article=f"""
+    <div>
+        <h3>Model Information</h3>
+        <p>This model is trained on the CIFAR-10 dataset and can classify images into 10 categories: 
+        plane, car, bird, cat, deer, dog, frog, horse, ship, and truck.</p>
+        
+        <h3>Model Architecture</h3>
+        <pre>{str(MODEL)}</pre>
+        
+        <h3>Model Parameters</h3>
+        <ul>
+            <li>Total parameters: {sum(p.numel() for p in MODEL.parameters()):,}</li>
+            <li>Trainable parameters: {sum(p.numel() for p in MODEL.parameters() if p.requires_grad):,}</li>
+        </ul>
+    </div>
+    """,
+    examples=[
+        ["examples/airplane.jpg"],
+        ["examples/automobile.jpg"],
+        ["examples/cat.jpg"]
+    ],
+    flagging_mode="never"
+)
+
+# Launch the app
+demo.launch()