import gradio as gr
import numpy as np
import cv2
import tensorflow as tf
from tensorflow.keras.models import load_model, Model
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.applications.xception import preprocess_input as xcp_pre
from tensorflow.keras.applications.efficientnet import preprocess_input as eff_pre
from PIL import Image
import matplotlib.pyplot as plt

from huggingface_hub import hf_hub_download

xcp_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", filename="xception_model.h5")
eff_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", filename="efficientnet_model.h5")

xcp_model = load_model(xcp_path)
eff_model = load_model(eff_path)

# Face detection
def detect_face_opencv(pil_image):
    cv_img = np.array(pil_image.convert("RGB"))
    cv_img = cv_img[:, :, ::-1]  # RGB to BGR
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    gray = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=4)
    if len(faces) == 0:
        return pil_image  # fallback
    (x, y, w, h) = max(faces, key=lambda b: b[2]*b[3])  # largest face
    return pil_image.crop((x, y, x+w, y+h))

# Grad-CAM
def grad_cam(model, img, size, preprocess_func):
    img_resized = img.resize(size)
    x = img_to_array(img_resized)
    x = np.expand_dims(x, axis=0)
    x = preprocess_func(x)
    x_tensor = tf.convert_to_tensor(x)

    grad_model = Model([model.inputs], [model.layers[-3].output, model.output])
    with tf.GradientTape() as tape:
        conv_outputs, predictions = grad_model(x_tensor)
        loss = predictions[:, 0]
    grads = tape.gradient(loss, conv_outputs)[0]
    cam = np.mean(grads, axis=-1)
    cam = np.maximum(cam, 0)
    cam /= cam.max() if cam.max() != 0 else 1
    heatmap = cv2.resize(cam.numpy(), (size[0], size[1]))
    heatmap = np.uint8(255 * heatmap)
    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)

    img_np = np.array(img_resized)
    if img_np.shape[-1] == 4:
        img_np = img_np[:, :, :3]
    superimposed = cv2.addWeighted(img_np, 0.6, heatmap, 0.4, 0)
    return Image.fromarray(cv2.cvtColor(superimposed, cv2.COLOR_BGR2RGB))

# Preprocessing
def preprocess(img, size, preprocess_func):
    img = img.resize(size)
    arr = img_to_array(img)
    arr = np.expand_dims(arr, axis=0)
    return preprocess_func(arr)

# Prediction logic
def predict(image):
    face = detect_face_opencv(image)

    xcp_input = preprocess(face, (299, 299), xcp_pre)
    eff_input = preprocess(face, (224, 224), eff_pre)

    xcp_pred = xcp_model.predict(xcp_input)[0][0]
    eff_pred = eff_model.predict(eff_input)[0][0]
    ensemble_prob = (xcp_pred + eff_pred) / 2
    label = "REAL" if ensemble_prob > 0.5 else "FAKE"

    cam_img = grad_cam(xcp_model, face, (299, 299), xcp_pre)

    return f"{label} ({ensemble_prob:.2%} confidence)", cam_img

# Gradio interface
gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=["text", "image"],
    title="Deepfake Image Detector (with Grad-CAM)",
    description="Upload an image. We detect the face, classify using an ensemble (Xception + EfficientNetB4), and explain the prediction with Grad-CAM."
).launch()