Create fake_image.py

fake_image.py

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+# -*- coding: utf-8 -*-
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+import torchvision.models as models
+from PIL import Image
+import os
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+# --- 🚨 MASTER FIX FOR PYTORCH 2.6 SECURITY ---
+import torch.serialization
+try:
+    torch.serialization.add_safe_globals([np.core.multiarray.scalar])
+except:
+    pass
+
+_original_load = torch.load
+def _patched_load(*args, **kwargs):
+    kwargs['weights_only'] = False
+    return _original_load(*args, **kwargs)
+torch.load = _patched_load
+# ----------------------------------------------
+
+# 1. Device Configuration
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# 2. Model Architecture (SOTA Hybrid Network)
+class Deepfake_Hybrid_Network(nn.Module):
+    def __init__(self, num_classes=2):
+        super(Deepfake_Hybrid_Network, self).__init__()
+        resnet = models.resnet18(weights=None)
+        self.cnn_extractor = nn.Sequential(*list(resnet.children())[:-2]) 
+        
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=512, nhead=8, dim_feedforward=1024, dropout=0.3, batch_first=True
+        )
+        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=2)
+        
+        self.classifier = nn.Sequential(
+            nn.Linear(512, 128),
+            nn.BatchNorm1d(128),
+            nn.ReLU(),
+            nn.Dropout(0.4),
+            nn.Linear(128, num_classes)
+        )
+
+    def forward(self, x):
+        x = self.cnn_extractor(x) 
+        b, c, h, w = x.shape
+        x = x.view(b, c, h * w).permute(0, 2, 1)  
+        x = self.transformer_encoder(x)
+        x = x.mean(dim=1) 
+        return self.classifier(x)
+
+# 3. Load Model from Hugging Face Hub
+print("Loading Image Hybrid SOTA Model...")
+model = Deepfake_Hybrid_Network(num_classes=2).to(device)
+
+try:
+    # Model HF repo se download ho raha hy
+    model_path = hf_hub_download(repo_id="aneela-pervez/My-Deepfake-Models", filename="Deepfake_Hybrid_SOTA.pth")
+    state_dict = torch.load(model_path, map_location=device)
+    
+    if any(k.startswith('module.') for k in state_dict.keys()):
+        from collections import OrderedDict
+        new_state_dict = OrderedDict()
+        for k, v in state_dict.items():
+            new_state_dict[k.replace('module.', '')] = v
+        model.load_state_dict(new_state_dict)
+    else:
+        model.load_state_dict(state_dict)
+    model.eval()
+    print("✅ Image SOTA Model loaded successfully!")
+except Exception as e:
+    print(f"⚠️ Error loading image model weights: {e}")
+
+# 4. Image Preprocessing
+test_transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# 5. Prediction Function for Master Pipeline
+def predict_image(img):
+    if img is None:
+        return {"Error": 1.0}
+    
+    img_tensor = test_transform(img).unsqueeze(0).to(device)
+    
+    with torch.no_grad():
+        outputs = model(img_tensor)
+        probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
+        
+        results = {
+            "Fake (Deepfake)": float(probabilities[0]),
+            "Real Image": float(probabilities[1])
+        }
+    return results