import os
import gradio as gr
from transformers import pipeline
import cv2

# Load Hugging Face Gender Classifier model (ProjectPersonal/GenderClassifier)
gender_classifier = pipeline("image-classification", model="ProjectPersonal/GenderClassifier")

# Load Hugging Face face swap model (DeepFaceLab by senhan007)
face_swap_model = pipeline("image-to-image", model="senhan007/DeepFaceLab")

def detect_gender(image_path):
    # Use the gender classifier to detect gender from the image
    result = gender_classifier(image_path)
    # Return the predicted gender (male or female)
    return result[0]['label'].lower()  # This will return "male" or "female"

def swap_faces(image, video, selected_gender):
    # Save uploaded files
    image_path = "uploaded_image.jpg"
    video_path = "uploaded_video.mp4"
    output_path = "swapped_video.mp4"
    image.save(image_path)
    video.save(video_path)

    # Detect gender of the reference image
    detected_gender = detect_gender(image_path)

    # If the detected gender matches the selected gender or if "all" is selected, proceed with the face swap
    if selected_gender == "all" or detected_gender == selected_gender:
        video_cap = cv2.VideoCapture(video_path)
        frame_rate = video_cap.get(cv2.CAP_PROP_FPS)
        width = int(video_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        output_video = cv2.VideoWriter(output_path, fourcc, frame_rate, (width, height))

        while True:
            ret, frame = video_cap.read()
            if not ret:
                break

            # Apply the face swap model (you need to insert the actual face swap model logic here)
            swapped_frame = frame  # Placeholder, replace with actual model inference

            output_video.write(swapped_frame)

        video_cap.release()
        output_video.release()
    else:
        # Return the original video without face swap if gender doesn't match
        output_path = video_path  # No face swap applied

    return output_path

# Gradio UI
iface = gr.Interface(
    fn=swap_faces,
    inputs=[
        gr.Image(type="pil", label="Upload Reference Image"),
        gr.Video(type="file", label="Upload Video"),
        gr.Dropdown(choices=["all", "male", "female"], label="Select Gender")
    ],
    outputs=gr.Video(label="Face Swapped Video"),
    title="Video Face Swap",
    description="Upload a reference image and a video to swap faces based on gender selection."
)

iface.launch()