Update app.py

app.py

@@ -1,130 +1,25 @@
+
 import gradio as gr
 import torch
-import torch.nn as nn
-import torchvision.transforms as transforms
-from PIL import Image
-import cv2
 import numpy as np
+import cv2
 import matplotlib.pyplot as plt
-import os
-
-# ✅ Xception Block Definition
-class SeparableConv2d(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0):
-        super().__init__()
-        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):
-        x = self.depthwise(x)
-        x = self.pointwise(x)
-        return x
-
-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="cpu"
-))
+from PIL import Image
+from transformers import AutoImageProcessor, SiglipForImageClassification
+
+# ✅ Load model from Hugging Face (no manual files)
+model_name = "prithivMLmods/deepfake-detector-model-v1"
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = SiglipForImageClassification.from_pretrained(model_name)
 model.eval()
 
-# ✅ Transform
-transform = transforms.Compose([
-    transforms.Resize((299, 299)),
-    transforms.ToTensor(),
-    transforms.Normalize([0.5]*3, [0.5]*3)
-])
+# ✅ Haar cascade for face detection
+face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
 
-# ✅ Analyze function
-def analyze_deepfake(video_path):
-    if not video_path:
-        return "❌ No video uploaded", None
+# ✅ Deepfake analysis logic
+def detect_deepfake(video_path):
+    if video_path is None:
+        return "❌ Please upload a valid .mp4 video", None
 
     cap = cv2.VideoCapture(video_path)
     preds = []
@@ -136,51 +31,59 @@ def analyze_deepfake(video_path):
         if not ret or count >= max_frames:
             break
 
-        h, w, _ = frame.shape
-        y1 = int(h * 0.25)
-        y2 = int(h * 0.75)
-        x1 = int(w * 0.25)
-        x2 = int(w * 0.75)
-        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():
-            out = model(input_tensor)
-            prob = torch.sigmoid(out)[0].item()
-            preds.append(prob)
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        faces = face_detector.detectMultiScale(gray, 1.1, 4)
+
+        if len(faces) > 0:
+            x, y, w, h = faces[0]  # Take first detected face
+            face = frame[y:y+h, x:x+w]
+            if face.size == 0:
+                continue
+
+            face_rgb = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
+            pil_img = Image.fromarray(face_rgb)
+            inputs = processor(images=pil_img, return_tensors="pt")
+
+            with torch.no_grad():
+                logits = model(**inputs).logits
+                prob = torch.softmax(logits, dim=-1)[0][1].item()
+                preds.append(prob)
 
         count += 1
 
     cap.release()
 
     if not preds:
-        return "❌ No frames analyzed", None
+        return "❌ No faces detected. Try a clearer video.", None
 
-    avg = np.mean(preds)
-    label = "**FAKE**" if avg > 0.5 else "**REAL**"
-    result = f"🎯 Verdict: {label}\nConfidence: {avg:.2f}"
+    avg_conf = np.mean(preds)
+    label = "**FAKE**" if avg_conf > 0.5 else "**REAL**"
+    result = f"""
+    🎯 **Result:** {label}  
+    🔢 Avg Confidence: {avg_conf:.2f}  
+    📊 Frames Analyzed: {len(preds)}
+    """
 
+    # ✅ Create histogram
     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")
-    ax.set_xlabel("Fake Probability")
+    ax.hist(preds, bins=10, color="red" if avg_conf > 0.5 else "green", edgecolor="black")
+    ax.set_title("Fake Confidence per Frame")
+    ax.set_xlabel("Fake Probability (0 = Real, 1 = Fake)")
     ax.set_ylabel("Frames")
     ax.grid(True)
 
     return result, fig
 
-# ✅ Gradio App
+# ✅ Gradio interface
 with gr.Blocks() as demo:
-    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.")
+    gr.Markdown("## 🎭 Deepfake Detector (Transformer-based)")
+    gr.Markdown("Upload a short `.mp4` video. The app will detect faces and classify the video as **REAL** or **FAKE**.")
 
-    video = gr.Video(label="Upload Video")
-    output_text = gr.Markdown()
-    output_plot = gr.Plot()
-    analyze = gr.Button("🔍 Analyze")
+    video_input = gr.Video(label="📤 Upload your video")
+    result_output = gr.Markdown()
+    graph_output = gr.Plot()
+    analyze_button = gr.Button("🔍 Analyze")
 
-    analyze.click(fn=analyze_deepfake, inputs=video, outputs=[output_text, output_plot])
+    analyze_button.click(fn=detect_deepfake, inputs=video_input, outputs=[result_output, graph_output])
 
 demo.queue().launch()