app.py

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 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"
))
model.eval()

# ✅ Transform
transform = transforms.Compose([
    transforms.Resize((299, 299)),
    transforms.ToTensor(),
    transforms.Normalize([0.5]*3, [0.5]*3)
])

# ✅ Analyze function
def analyze_deepfake(video_path):
    if not video_path:
        return "❌ No video uploaded", None

    cap = cv2.VideoCapture(video_path)
    preds = []
    count = 0
    max_frames = 20

    while True:
        ret, frame = cap.read()
        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)

        count += 1

    cap.release()

    if not preds:
        return "❌ No frames analyzed", None

    avg = np.mean(preds)
    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")
    ax.set_xlabel("Fake Probability")
    ax.set_ylabel("Frames")
    ax.grid(True)

    return result, fig

# ✅ Gradio App
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.")

    video = gr.Video(label="Upload Video")
    output_text = gr.Markdown()
    output_plot = gr.Plot()
    analyze = gr.Button("🔍 Analyze")

    analyze.click(fn=analyze_deepfake, inputs=video, outputs=[output_text, output_plot])

demo.queue().launch()