app.py

import gradio as gr
import torch
import torch.nn.functional as F
from facenet_pytorch import MTCNN, InceptionResnetV1
import os
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import pickle

DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
print(f'Running on device: {DEVICE.upper()}')

torch.load('resnetinceptionv1_final.pth',map_location='cpu')

mtcnn = MTCNN(
    select_largest=False,
    post_process=False,
    device=DEVICE
).to(DEVICE).eval()

model = InceptionResnetV1(
    pretrained="vggface2",
    classify=True,
    num_classes=1,
    device=DEVICE
)
model.load_state_dict(torch.load('resnetinceptionv1_final.pth',map_location='cpu'))
model.to(DEVICE)
model.eval()
print("MTCNN & Classfier models loaded")


# Abrimos el fichero pickle de ejemplos de imagenes

with open('file_examples.pkl','rb') as file:
    examples=pickle.load(file)

#EXAMPLES_FOLDER = 'examples'
#examples_names = os.listdir(EXAMPLES_FOLDER)
#examples = []
#for example_name in examples_names:
#    example_path = os.path.join(EXAMPLES_FOLDER, example_name)
#    label = example_name.split('_')[0]
#    example = {
#        'path': example_path,
#        'label': label
#    }
#    examples.append(example)
 
   
       
def predict(input_image:Image.Image):
    """Predict the label of the input_image"""
    face = mtcnn(input_image)
    if face is None:
        raise Exception('No face detected')
    face = face.unsqueeze(0) # add the batch dimension
    face = F.interpolate(face, size=(256, 256), mode='bilinear', align_corners=False)
    
    # convert the face into a numpy array to be able to plot it
    face_image_to_plot = face.squeeze(0).permute(1, 2, 0).cpu().detach().int().numpy()

    face = face.to(DEVICE)
    face = face.to(torch.float32)
    face = face / 255.0
    with torch.no_grad():
        output = torch.sigmoid(model(face).squeeze(0))
        prediction = "real" if output.item() < 0.5 else "fake"
        
        real_prediction = 1 - output.item()
        fake_prediction = output.item()
        
        confidences = {
            'real': real_prediction,
            'fake': fake_prediction
        }
    return confidences, face_image_to_plot 
    
for i in range(10):
    example = examples[8]
    #example_img = example['path']
    example_img='fake_frame_0.jpg'
    example_label = example['label']

    print(f"True label: {example_label}")

    example_img = Image.open(example_img)
    confidences, _ = predict(example_img)
    if confidences['real'] > 0.5:
        print("Predicted label: real")
    else:
        print("Predicted label: fake")

    print()     
    
title='Fake or not Fake? that is the question'
description='Modelo de deeplearning para detectar imagenes generadas falsas'
article='Proyecto Saturdays.AI DemoDay 11/06/2022'
   
         
interface = gr.Interface(
    fn=predict,
    inputs=gr.inputs.Image(label="Input Image", type="pil"),
    outputs=[
        gr.outputs.Label(label="Class"),
        gr.outputs.Image(label="Face")
    ],
    title=title,description=description, article=article,
    theme='peach',
    #examples=[examples[i]["path"] for i in range(8)] # fake examples
    examples=['fake_frame_0.jpg','fake_frame_1.jpg','fake_frame_2.jpg','fake_frame_3.jpg','real_frame_0.jpg','real_frame_1.jpg','real_frame_2.jpg','real_frame_3.jpg']
).launch()