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import gradio as gr
from model.load_model import load_model
from utils.video_utils import extract_frames
from utils.face_utils import extract_faces
from predictor.predict import predict_faces
from utils.gradcam import get_gradcam, get_conv_layers
import numpy as np
from PIL import Image
from tqdm import tqdm
model = load_model()
conv_layer_names = get_conv_layers(model) # populate dropdown choices
def deepfake_app(video, selected_layer, progress=gr.Progress(track_tqdm=True)):
frames = extract_frames(video)
frames = list(frames)
faces = extract_faces(frames)
faces = list(progress.tqdm(faces, desc="Detecting faces"))
if not faces:
return "No face detected", None
predictions = predict_faces(model, faces)
predictions = list(progress.tqdm(predictions, desc="Running predictions"))
avg_score = np.mean(predictions)
label = "FAKE" if avg_score > 0.5 else "REAL"
max_idx = np.argmax(predictions)
cam_image = get_gradcam(model, faces[max_idx], selected_layer)
cam_image = Image.fromarray(cam_image)
return label, cam_image
gr.Interface(
fn=deepfake_app,
inputs=[gr.Video(label="Upload a Video"),
gr.Dropdown(choices=conv_layer_names, label="Grad-CAM Layer", value=conv_layer_names[-1])
],
outputs=["text", "image"],
title="Deepfake Detection with XceptionNet",
description="Upload a video, and the model will predict if it contains a deepfake with RAI explainability using GRADCAM."
).launch()
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