Update README.md

README.md

@@ -54,47 +54,8 @@ The model is designed to classify single images as either **REAL** or **FAKE**.
 - **Video Analysis:** While it can analyze individual frames, it does not currently leverage temporal coherence in videos (frame-by-frame analysis only).
 - **Audio Deepfakes:** This model is strictly for visual content.
 - **Legal Proof:** The model provides a probabilistic assessment and should not be the sole basis for legal judgments.
-
-## How to Get Started with the Model
-
-```python
-import torch
-import torch.nn as nn
-from torchvision import models
-from safetensors.torch import load_file
-import cv2
-
-# Define Model Architecture
-class DeepfakeDetector(nn.Module):
-    def __init__(self, pretrained=False):
-        super(DeepfakeDetector, self).__init__()
-        self.efficientnet = models.efficientnet_v2_s(weights='DEFAULT' if pretrained else None)
-        self.swin = models.swin_v2_t(weights='DEFAULT' if pretrained else None)
-        
-        self.efficientnet.classifier = nn.Identity()
-        self.swin.head = nn.Identity()
-        
-        self.classifier = nn.Sequential(
-            nn.Linear(1280 + 768, 512),
-            nn.BatchNorm1d(512),
-            nn.ReLU(),
-            nn.Dropout(0.4),
-            nn.Linear(512, 1)
-        )
-
-    def forward(self, x):
-        f1 = self.efficientnet(x)
-        f2 = self.swin(x)
-        combined = torch.cat((f1, f2), dim=1)
-        return self.classifier(combined)
-
-# Load Model
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model = DeepfakeDetector(pretrained=False).to(device)
-state_dict = load_file("best_model.safetensors")
-model.load_state_dict(state_dict)
-model.eval()
-```
+ 
+ 
 
 ## Training Details