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 zipfile
import cv2
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
from pytorch_grad_cam.utils.image import show_cam_on_image
from transformers import pipeline
with zipfile.ZipFile("examples.zip","r") as zip_ref:
    zip_ref.extractall(".")

pipe = pipeline(model="SivaResearch/Fake_Detection",trust_remote_code=True)

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)
np.random.shuffle(examples) # shuffle

def predict(input_image:Image.Image, true_label:str):
    out = pipe.predict(input_image)
    confidences,face_with_mask = out["confidences"], out["face_with_mask"]
    return confidences, true_label, face_with_mask

interface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Image(label="Input Image", type="filepath"),
        "text"
    ],
    outputs=[
        gr.Label(label="Class"),
        "text",
        gr.Image(label="Face with Explainability")
    ],
    examples=[[examples[i]["path"], examples[i]["label"]] for i in range(10)]
).launch()