Update app.py

app.py

@@ -8,37 +8,123 @@ import numpy as np
 import matplotlib.pyplot as plt
 import os
 
-# ✅ Load Xception model trained on DFDC
-class XceptionModel(nn.Module):
-    def __init__(self):
+# ✅ Xception Block Definition
+class SeparableConv2d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0):
         super().__init__()
-        base = torch.hub.load('pytorch/vision:v0.10.0', 'xception', pretrained=False)
-        base.fc = nn.Linear(base.fc.in_features, 1)
-        self.base = base
-
+        self.depthwise = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding, groups=in_channels, bias=False)
+        self.pointwise = nn.Conv2d(in_channels, out_channels, 1, 1, 0, bias=False)
     def forward(self, x):
-        return self.base(x)
+        x = self.depthwise(x)
+        x = self.pointwise(x)
+        return x
 
-# Load model and weights
-model = XceptionModel()
-state_dict = torch.hub.load_state_dict_from_url(
+class Block(nn.Module):
+    def __init__(self, in_filters, out_filters, reps, stride=1, start_with_relu=True, grow_first=True):
+        super().__init__()
+        layers = []
+        filters = in_filters
+        if grow_first:
+            if start_with_relu:
+                layers.append(nn.ReLU(inplace=True))
+            layers.extend([
+                SeparableConv2d(in_filters, out_filters, 3, 1, 1),
+                nn.BatchNorm2d(out_filters)
+            ])
+            filters = out_filters
+        for _ in range(reps - 1):
+            layers.extend([
+                nn.ReLU(inplace=True),
+                SeparableConv2d(filters, filters, 3, 1, 1),
+                nn.BatchNorm2d(filters)
+            ])
+        if not grow_first:
+            layers.extend([
+                nn.ReLU(inplace=True),
+                SeparableConv2d(in_filters, out_filters, 3, 1, 1),
+                nn.BatchNorm2d(out_filters)
+            ])
+        if stride != 1:
+            layers.append(nn.MaxPool2d(3, stride, 1))
+        self.block = nn.Sequential(*layers)
+        self.skip = nn.Conv2d(in_filters, out_filters, 1, stride, bias=False)
+        self.skipbn = nn.BatchNorm2d(out_filters)
+    def forward(self, inp):
+        x = self.block(inp)
+        skip = self.skipbn(self.skip(inp))
+        x += skip
+        return x
+
+# ✅ Xception Architecture
+class Xception(nn.Module):
+    def __init__(self, num_classes=1):
+        super().__init__()
+        self.conv1 = nn.Conv2d(3, 32, 3, 2, 0, bias=False)
+        self.bn1 = nn.BatchNorm2d(32)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = nn.Conv2d(32, 64, 3, bias=False)
+        self.bn2 = nn.BatchNorm2d(64)
+        self.block1 = Block(64, 128, 2, 2, start_with_relu=False, grow_first=True)
+        self.block2 = Block(128, 256, 2, 2, grow_first=True)
+        self.block3 = Block(256, 728, 2, 2, grow_first=True)
+        self.block4 = Block(728, 728, 3)
+        self.block5 = Block(728, 728, 3)
+        self.block6 = Block(728, 728, 3)
+        self.block7 = Block(728, 728, 3)
+        self.block8 = Block(728, 728, 3)
+        self.block9 = Block(728, 728, 3)
+        self.block10 = Block(728, 728, 3)
+        self.block11 = Block(728, 728, 3)
+        self.block12 = Block(728, 1024, 2, 2, grow_first=False)
+        self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1)
+        self.bn3 = nn.BatchNorm2d(1536)
+        self.conv4 = SeparableConv2d(1536, 2048, 3, 1, 1)
+        self.bn4 = nn.BatchNorm2d(2048)
+        self.fc = nn.Linear(2048, num_classes)
+    def features(self, input):
+        x = self.relu(self.bn1(self.conv1(input)))
+        x = self.relu(self.bn2(self.conv2(x)))
+        x = self.block1(x)
+        x = self.block2(x)
+        x = self.block3(x)
+        x = self.block4(x)
+        x = self.block5(x)
+        x = self.block6(x)
+        x = self.block7(x)
+        x = self.block8(x)
+        x = self.block9(x)
+        x = self.block10(x)
+        x = self.block11(x)
+        x = self.block12(x)
+        x = self.relu(self.bn3(self.conv3(x)))
+        x = self.relu(self.bn4(self.conv4(x)))
+        return x
+    def forward(self, input):
+        x = self.features(input)
+        x = nn.AdaptiveAvgPool2d((1, 1))(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+
+# ✅ Load weights
+model = Xception()
+model.load_state_dict(torch.hub.load_state_dict_from_url(
     "https://huggingface.co/Selimsef/xception-cnn-df/resolve/main/xception-binary-weights.pt",
-    map_location=torch.device('cpu')
-)
-model.load_state_dict(state_dict)
+    map_location="cpu"
+))
 model.eval()
 
-# ✅ Define transforms
+# ✅ Transform
 transform = transforms.Compose([
     transforms.Resize((299, 299)),
     transforms.ToTensor(),
     transforms.Normalize([0.5]*3, [0.5]*3)
 ])
 
-# ✅ Inference function
-def predict_deepfake(video_path):
-    if video_path is None:
-        return "❌ Please upload a video.", None
+# ✅ Analyze function
+def analyze_deepfake(video_path):
+    if not video_path:
+        return "❌ No video uploaded", None
 
     cap = cv2.VideoCapture(video_path)
     preds = []
@@ -55,15 +141,14 @@ def predict_deepfake(video_path):
         y2 = int(h * 0.75)
         x1 = int(w * 0.25)
         x2 = int(w * 0.75)
-        face_crop = frame[y1:y2, x1:x2]  # Center crop
-
-        image = cv2.cvtColor(face_crop, cv2.COLOR_BGR2RGB)
-        pil_img = Image.fromarray(image)
-        input_tensor = transform(pil_img).unsqueeze(0)
+        crop = frame[y1:y2, x1:x2]
+        image = cv2.cvtColor(crop, cv2.COLOR_BGR2RGB)
+        image = Image.fromarray(image)
+        input_tensor = transform(image).unsqueeze(0)
 
         with torch.no_grad():
-            output = model(input_tensor)
-            prob = torch.sigmoid(output)[0].item()
+            out = model(input_tensor)
+            prob = torch.sigmoid(out)[0].item()
             preds.append(prob)
 
         count += 1
@@ -71,36 +156,31 @@ def predict_deepfake(video_path):
     cap.release()
 
     if not preds:
-        return "❌ No frames processed. Try a different video.", None
+        return "❌ No frames analyzed", None
 
     avg = np.mean(preds)
-    result = "✅ **REAL**" if avg < 0.5 else "❌ **FAKE**"
-    verdict = f"""
-    🎯 Final Verdict: {result}
-    📊 Confidence (avg): {avg:.2f}
-    """
-
-    # Plot histogram
-    fig, ax = plt.subplots(figsize=(6, 4))
+    label = "**FAKE**" if avg > 0.5 else "**REAL**"
+    result = f"🎯 Verdict: {label}\nConfidence: {avg:.2f}"
+
+    fig, ax = plt.subplots()
     ax.hist(preds, bins=10, color="red" if avg > 0.5 else "green", edgecolor="black")
-    ax.set_title("Confidence per Frame (0 = Real, 1 = Fake)")
+    ax.set_title("Confidence per Frame")
     ax.set_xlabel("Fake Probability")
-    ax.set_ylabel("Frame Count")
+    ax.set_ylabel("Frames")
     ax.grid(True)
 
-    return verdict, fig
+    return result, fig
 
-# ✅ Gradio Interface
+# ✅ Gradio App
 with gr.Blocks() as demo:
-    gr.Markdown("## 🎭 Real Deepfake Detection (Xception DFDC Model)")
-    gr.Markdown("Upload a short `.mp4` video and the app will classify it as **REAL** or **FAKE** using a pretrained deepfake detection model.")
-
-    video_input = gr.Video(label="Upload video")
-    result_output = gr.Markdown()
-    graph_output = gr.Plot()
+    gr.Markdown("# 🎭 Deepfake Detector with Xception (DFDC)")
+    gr.Markdown("Upload a `.mp4` video. The app will classify it as REAL or FAKE based on pretrained deepfake model.")
 
-    analyze_btn = gr.Button("🔍 Analyze")
+    video = gr.Video(label="Upload Video")
+    output_text = gr.Markdown()
+    output_plot = gr.Plot()
+    analyze = gr.Button("🔍 Analyze")
 
-    analyze_btn.click(fn=predict_deepfake, inputs=video_input, outputs=[result_output, graph_output])
+    analyze.click(fn=analyze_deepfake, inputs=video, outputs=[output_text, output_plot])
 
 demo.queue().launch()