app.py

import gradio as gr
print("Loading models...")

import cv2
import numpy as np
from PIL import Image
from rembg import remove
from sentence_transformers import SentenceTransformer
import urllib.request
import pathlib

print("Libraries loaded")

# Load CLIP Model
image_model = SentenceTransformer("clip-ViT-B-32")
print("CLIP loaded")


# Load Anime Face Cascade
def load_anime_model():
    url = "https://raw.githubusercontent.com/nagadomi/lbpcascade_animeface/master/lbpcascade_animeface.xml"
    path = pathlib.Path("lbpcascade_animeface.xml")

    if not path.exists():
        print("Downloading anime face model...")
        urllib.request.urlretrieve(url, path.as_posix())

    return cv2.CascadeClassifier(path.as_posix())


# Load Human Face Cascade
def load_human_model():
    path = pathlib.Path(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
    return cv2.CascadeClassifier(path.as_posix())


anime_detector = load_anime_model()
human_detector = load_human_model()

print("Anime + Human detectors loaded")


# Embedding Function
def get_image_embedding(image):
    emb = image_model.encode(image)
    return {"embedding": emb.tolist()}


# Face Crop + Background Remove
def process_image(input_image, mode):

    img = cv2.cvtColor(np.array(input_image), cv2.COLOR_RGB2BGR)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Choose detector
    if mode == "Anime":
        detector = anime_detector
    else:
        detector = human_detector

    faces = detector.detectMultiScale(
        gray,
        scaleFactor=1.1,
        minNeighbors=5,
        minSize=(24, 24)
    )

    if len(faces) == 0:
        print("direct to background removal")
        pil_image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
        output = remove(pil_image)
        output = output.resize((224, 224))
       
        return "Success ✅", output

    x, y, w, h = faces[0]

    height, width, _ = img.shape

    # Expand bounding box
    top_expand = 0.5
    side_expand = 0.3
    bottom_expand = 0.2

    x1 = int(max(0, x - w * side_expand))
    x2 = int(min(width, x + w + w * side_expand))
    y1 = int(max(0, y - h * top_expand))
    y2 = int(min(height, y + h + h * bottom_expand))

    cropped = img[y1:y2, x1:x2]

    pil_image = Image.fromarray(cv2.cvtColor(cropped, cv2.COLOR_BGR2RGB))

    # Background removal
    output = remove(pil_image)

    # Resize for CLIP
    output = output.resize((224, 224))

    return "Success ✅", output


# Gradio UI
with gr.Blocks() as demo:

    with gr.Tab("Full Pipeline"):
        mode_selector = gr.Dropdown(
            choices=["Anime", "Human"],
            value="Anime",
            label="Detection Mode"
        )

        img_input = gr.Image(type="pil")
        embedding_output = gr.JSON()

        run_btn = gr.Button("Run Pipeline")

        def run_pipeline(img, mode):
            status_msg, processed_img = process_image(img, mode)
            if status_msg != "Success ✅":
                return {"embedding": None}

            embedding = get_image_embedding(processed_img)
            return embedding

        run_btn.click(
            run_pipeline,
            inputs=[img_input, mode_selector],
            outputs=[embedding_output]
        )

    with gr.Tab("Embedding Only"):
        img_input2 = gr.Image(type="pil")
        embedding_output2 = gr.JSON()
        run_btn2 = gr.Button("Get Embedding")

        def get_embedding_only(img):
            embedding = get_image_embedding(img)
            return embedding

        run_btn2.click(
            get_embedding_only,
            inputs=img_input2,
            outputs=embedding_output2
        )


print("Launching demo...")
demo.queue(max_size=15).launch()