app.py

import gradio as gr
import torch
import os
import cv2
import urllib.request
from model.pred_func import load_genconvit, df_face, pred_vid, real_or_fake
from model.config import load_config

# --- Model Download ---
def download_models():
    """
    Downloads the pre-trained model weights if they don't exist.
    """
    weight_dir = 'weight'
    ed_url = 'https://huggingface.co/Deressa/GenConViT/resolve/main/genconvit_ed_inference.pth'
    vae_url = 'https://huggingface.co/Deressa/GenConViT/resolve/main/genconvit_vae_inference.pth'
    ed_path = os.path.join(weight_dir, 'genconvit_ed_inference.pth')
    vae_path = os.path.join(weight_dir, 'genconvit_vae_inference.pth')

    if not os.path.exists(weight_dir):
        os.makedirs(weight_dir)

    if not os.path.exists(ed_path):
        print("Downloading ED model weights...")
        urllib.request.urlretrieve(ed_url, ed_path)
        print("Download complete.")

    if not os.path.exists(vae_path):
        print("Downloading VAE model weights...")
        urllib.request.urlretrieve(vae_url, vae_path)
        print("Download complete.")

# --- Global Variables ---
config = load_config()
model = None

def load_model_once():
    """
    Loads the model into memory. This function is called once at the start.
    """
    global model
    if model is None:
        download_models()
        print("Loading GenConViT model...")
        ed_weight = 'genconvit_ed_inference'
        vae_weight = 'genconvit_vae_inference'
        # Set net='genconvit' to use both ED and VAE as per prediction.py logic for best results
        model = load_genconvit(config, net='genconvit', ed_weight=ed_weight, vae_weight=vae_weight, fp16=False)
        print("Model loaded successfully.")

def get_video_duration(video_path):
    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        return 0

    fps = cap.get(cv2.CAP_PROP_FPS)
    frame_count = cap.get(cv2.CAP_PROP_FRAME_COUNT)
    cap.release()

    if fps == 0:
        return 0

    return frame_count / fps

# --- Prediction Function ---
def detect_deepfake(video_path, model_type, num_frames):
    if video_path is None:
        return "❌ Please upload a video file."

    # ===== VALIDASI DURASI VIDEO =====
    duration = get_video_duration(video_path)
    if duration > 60:
        return "❌ Video terlalu besar. Durasi maksimal adalah 1 menit (60 detik)."

    try:
        print(f"Processing video: {video_path} with model: {model_type}")
        
        # Map model_type to internal net identifier
        net_mapping = {
            "GenConViT": "genconvit",
            "AE": "ed",
            "VAE": "vae"
        }
        net_val = net_mapping.get(model_type, "genconvit")

        # Extract faces from the video
        faces = df_face(video_path, num_frames)

        if len(faces) == 0:
            return "No faces were detected in the video. Please try another video."

        # Make prediction
        y, y_val = pred_vid(faces, model, net=net_val)
        
        # Get the label (REAL or FAKE)
        label = real_or_fake(y)

        # The confidence score y_val is a bit complex in the original code.
        # For simplicity, we'll show the raw score associated with the prediction.
        # A lower score generally means more likely to be REAL, higher means more likely to be FAKE.
        
        confidence = y_val if label == 'FAKE' else 1 - y_val
        
        return { "FAKE": confidence, "REAL": 1 - confidence }

    except Exception as e:
        print(f"An error occurred: {e}")
        return "An error occurred during processing. The video might be corrupted or in an unsupported format."

# --- Gradio Interface ---
title = "GenConViT: Deepfake Video Detection"
description = """
Upload a video file to detect if it's a deepfake. This application uses the Generative Convolutional Vision Transformer (GenConViT)
to analyze the video. The model achieves an average accuracy of 95.8% and an AUC of 99.3% across multiple datasets.
"""

# Load the model once when the app starts
load_model_once()

iface = gr.Interface(
    fn=detect_deepfake,
    inputs=[
        gr.Video(label="Upload Video"),
        gr.Radio(["GenConViT", "AE", "VAE"], label="Pilih Model", value="GenConViT"),
        gr.Slider(1, 200, value=15, step=1, label="Number of Frames")
    ],
    outputs=gr.Label(num_top_classes=2, label="Prediction Result"),
    title=title,
    description=description,
    flagging_mode="never",
    examples=[
        ["sample_prediction_data/aajsqyyjni.mp4", "GenConViT", 15],
        ["sample_prediction_data/anndvqgoko.mp4", "GenConViT", 15],
        ["sample_prediction_data/0017_fake.mp4.mp4", "GenConViT", 15],
        ["sample_prediction_data/0048_fake.mp4.mp4", "GenConViT", 15]
    ]
)

if __name__ == "__main__":
    iface.queue().launch()