app.py

import base64
import json
import os, shutil
import re
import time
import uuid

import cv2

import numpy as np
import streamlit as st
from PIL import Image
# from extract_video import extract_method_single_video

import shlex
import subprocess
from file_picker import st_file_selector

import os

DEBUG = True

def main():
    st.markdown("###")    
    uploaded_file = st.file_uploader('Upload a picture', type=['mp4', 'jpg', 'jpeg', 'png'], accept_multiple_files=False)

    with st.spinner(f'Loading samples...'):
        while not os.path.isdir("sample_files"):
            time.sleep(1)
    st.markdown("### or")
    selected_file = st_file_selector(st, path='sample_files', key = 'selected', label = 'Choose a sample image/video')

    if uploaded_file: 
        random_id = uuid.uuid1()
        base_folder = "temps"
        filename = "{}.{}".format(random_id, uploaded_file.type.split("/")[-1])
        file_type = uploaded_file.type.split("/")[0]
        filepath = f"{base_folder}/{filename}"
        faces_folder = f"{base_folder}/images/{random_id}"

        if uploaded_file.type == 'video/mp4':
            with open(f"temps/{filename}", mode='wb') as f:
                f.write(uploaded_file.read())
            st.video(uploaded_file)
        else:
            img = Image.open(uploaded_file).convert('RGB')
            ext = uploaded_file.type.split("/")[-1]
            with open(f"temps/{filename}", mode='wb') as f:
                f.write(uploaded_file.getbuffer())  
            st.image(img)
    elif selected_file:
        base_folder = "sample_files"
        file_type = selected_file.split(".")[-1]
        filename = selected_file.split("/")[-1]
        filepath = f"{base_folder}/{selected_file}"
        faces_folder = f"{base_folder}/images/" + selected_file.split(".")[0]

        if file_type == 'mp4':
            video_file = open(filepath, 'rb')
            video_bytes = video_file.read()
            st.video(video_bytes)
        else:
            image_file = open(filepath, 'rb')
            image_bytes = image_file.read()
            st.image(image_bytes)
    else:
        return


    

    with st.spinner(f'Processing {file_type}...'):
        processing_stdout = subprocess.run(shlex.split(f"""python extract_video.py --device cpu --max_frames 50 --bs 2 --frame_interval 60 --confidence_threshold 0.997 --data_path "{filepath}" """), capture_output=True)
    st.text(f'1. Processing {file_type} ✅')
    with st.spinner(f'Analyzing {file_type}...'):
        analyze_stdout = subprocess.run(shlex.split(f"""python inference.py --weight weights/model_params_ffpp_c23.pickle --device cpu --image_folder "{faces_folder}" """), capture_output=True)
    st.text(f'2. Analyzing {file_type} ✅')

    if len(os.listdir(faces_folder)) < 1:
        st.text("No faces could be detected! 🚨")
        return
    
    try:
        fake_probability = float(analyze_stdout.stdout.decode('utf-8').split('Mean prediction: ')[-1])
        if fake_probability > 0.6:
            st.error('  FAKE! ', icon="🚨")
        else:
            st.success("  REAL FOOTAGE! ", icon="✅")
        st.text("fake probability {:.2f}".format(fake_probability))

        # os.remove(f"{base_folder}/{filename}")
        shutil.rmtree(faces_folder)
    except Exception as e:
        if DEBUG:
            st.text(processing_stdout.stdout.decode('utf-8'))
            st.text(analyze_stdout.stdout.decode('utf-8'))

            st.text("")
            st.text(processing_stdout)
            st.text(analyze_stdout)
            st.write(e)
        else:
            st.text("Encountered a problem while analyzing video/image 🚨")


def setup():
    if not os.path.isdir("temps"):
        os.makedirs("temps")

if __name__ == "__main__":
    st.set_page_config(
        page_title="Nodeflux Deepfake Detection", page_icon=":pencil2:"
    )
    st.title("Deepfake Detection")
    setup()
    main()