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README.md

@@ -14,15 +14,12 @@ metrics:
 - f1
 pipeline_tag: image-classification
 inference: false
-widgets:
-  - text: "Test the DeepGuard Model Live"
-    src: "https://harshasnade-deepfake-detection-system-v1.hf.space"
-  - text: "Deepfake Sample"
-    src: "https://harshasnade-deepfake-detection-system-v1.hf.space"
 ---
 
 # DeepGuard - Deepfake Detection System
 
+[![Open in Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-lg.svg)](https://huggingface.co/spaces/Harshasnade/Deepfake_Detection_System_V1)
+
 ## Model Details
 
 ### Model Description
@@ -54,8 +51,47 @@ 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