Update README.md

README.md

@@ -46,72 +46,4 @@ This model is released under MIT License.
 
 ## Code Implementation
 
-  import requests
-  import torch
-  from torchvision import transforms, models
-  import torch.nn as nn
-  from PIL import Image
-  import io
-
-  def classify_image_huggingface(image_path, repo_id="mertincesu/property-indoor-or-outdoor-classifier"):
-      
-      # Define model structure
-      model = models.mobilenet_v2(weights=None)
-      num_ftrs = model.classifier[1].in_features
-      model.classifier[1] = nn.Linear(num_ftrs, 2)
-      
-      # Load model weights directly from Hugging Face
-      model_url = f"https://huggingface.co/{repo_id}/resolve/main/pytorch_model.bin"
-      
-      try:
-          # Download the model file
-          print(f"Downloading model from {model_url}")
-          response = requests.get(model_url)
-          response.raise_for_status()
-          
-          # Load the model weights
-          model_binary = io.BytesIO(response.content)
-          model.load_state_dict(torch.load(model_binary, map_location=device))
-          model.to(device)
-          model.eval()
-          
-          # Define image transformation
-          transform = transforms.Compose([
-              transforms.Resize((160, 160)),
-              transforms.ToTensor(),
-              transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-          ])
-        
-          # Load and process the image
-          img = Image.open(image_path).convert('RGB')
-          img_tensor = transform(img).unsqueeze(0).to(device)
-          
-          # Make prediction
-          with torch.no_grad():
-              outputs = model(img_tensor)
-              probs = torch.nn.functional.softmax(outputs, dim=1)
-              _, predicted = torch.max(outputs, 1)
-          
-          classes = ['indoor', 'outdoor']
-          result = {
-              'class': classes[predicted.item()],
-              'confidence': probs[0][predicted.item()].item() * 100
-          }
-          
-          print(f"Class: {result['class']}")
-          print(f"Confidence: {result['confidence']:.2f}%")
-          return result
-          
-      except Exception as e:
-          print(f"Error: {e}")
-          return None
-  
-  if __name__ == "__main__":
-      import sys
-      
-      if len(sys.argv) > 1:
-          image_path = sys.argv[1]
-      else:
-          image_path = input("Enter path to image: ")
-      
-      classify_image_huggingface(image_path)
+Example usage code has been provided in example_usage.py under model files.