import os
import gradio as gr
from deepface import DeepFace

# Path to the dataset folder
DATASET_PATH = "face_dataset"

# Load dataset images (only image files are included)
dataset_images = {
    os.path.splitext(filename)[0]: os.path.join(DATASET_PATH, filename)  # Remove file extension
    for filename in os.listdir(DATASET_PATH)
    if filename.lower().endswith(('.jpg', '.jpeg', '.png'))
}

def recognize_face(uploaded_image):
    """
    Compare the uploaded image with dataset images using DeepFace.
    Returns the matched model name as an Instagram link and its image if a verified match is found.
    """
    if uploaded_image is None:
        return "No image uploaded.", None

    # Loop through each image in the dataset
    for model_name, image_path in dataset_images.items():
        try:
            result = DeepFace.verify(
                uploaded_image,
                image_path,
                model_name="VGG-Face",
                enforce_detection=False
            )
            if result.get("verified"):
                # Create an Instagram link
                model_link = f'<a href="https://www.instagram.com/{model_name}/" target="_blank">{model_name}</a>'
                return model_link, image_path
        except Exception as e:
            print(f"Error processing {image_path}: {e}")

    return "No matching face found.", None

# Create a Gradio interface for the app
iface = gr.Interface(
    fn=recognize_face,
    inputs=gr.Image(type="numpy", label="Upload an Image"),
    outputs=[
        gr.HTML(label="Model Name"),  # Use HTML for clickable link
        gr.Image(label="Matched Model Image")
    ],
    title="Face Recognition App",
    description="Upload an image to find the most similar face from the dataset."
)

if __name__ == "__main__":
    iface.launch()