app.py

import gradio as gr
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import cv2
import os
import tempfile
from nudenet import NudeDetector

# --- Konstanten ---
DETECTION_MAX_DIM = 768
PIXELS_PER_CM_ESTIMATE = 15
MIN_CONFIDENCE = 0.45

detector = NudeDetector(inference_resolution=640)

def resize_for_detection(img_pil, max_dim):
    if max(img_pil.width, img_pil.height) <= max_dim:
        return img_pil, 1.0
    ratio = max_dim / max(img_pil.width, img_pil.height)
    new_size = (int(img_pil.width * ratio), int(img_pil.height * ratio))
    resized = img_pil.resize(new_size, Image.Resampling.LANCZOS)
    scale = 1 / ratio
    return resized, scale

def describe_breast_precise(crop_pil):
    w, h = crop_pil.size
    if w * h == 0:
        return "Fehler: leeres Crop"
    gray = cv2.cvtColor(np.array(crop_pil), cv2.COLOR_RGB2GRAY)
    _, thresh = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    nipple_detected = any(
        40 < cv2.contourArea(c) < (w * h / 4)
        and (p := cv2.arcLength(c, True)) > 0
        and (4 * np.pi * cv2.contourArea(c) / (p * p)) > 0.55
        for c in contours
    )
    ratio = w / h
    shape = "Breit" if ratio > 1.15 else "Hoch" if ratio < 0.85 else "Rund"
    size = "klein" if w * h < 28000 else "mittel" if w * h < 75000 else "groß" if w * h < 140000 else "sehr groß"
    w_cm = round(w / PIXELS_PER_CM_ESTIMATE, 1)
    h_cm = round(h / PIXELS_PER_CM_ESTIMATE, 1)
    return f"Brust: {shape}, {size}, Nippel: {'Ja' if nipple_detected else 'Nein'}, {w_cm}x{h_cm}cm"

def describe_vagina_precise(crop_pil):
    w, h = crop_pil.size
    if w * h == 0:
        return "Fehler: leeres Crop"
    gray = cv2.cvtColor(np.array(crop_pil), cv2.COLOR_RGB2GRAY)
    hair_ratio = np.sum(cv2.inRange(gray, 35, 145) > 0) / (w * h)
    shaved = "rasiert" if hair_ratio < 0.04 else "minimal" if hair_ratio < 0.13 else "Brazilian" if hair_ratio < 0.36 else "behaart"
    ratio = w / h
    area = w * h
    if area < 18000:
        form_desc = "Innie"
    elif area > 65000 and ratio > 1.45:
        form_desc = "Outie (Puff)"
    elif ratio > 1.45:
        form_desc = "Outie"
    else:
        form_desc = "Innie/Outie"
    size = "winzig" if area < 18000 else "klein" if area < 38000 else "mittel" if area < 65000 else "groß"
    w_cm = round(w / PIXELS_PER_CM_ESTIMATE, 1)
    h_cm = round(h / PIXELS_PER_CM_ESTIMATE, 1)
    return f"Vagina: {form_desc}, {size}, {shaved}, {w_cm}x{h_cm}cm"

def process_image(image_path):
    try:
        original_pil = Image.open(image_path).convert("RGB")
        detection_pil, scale = resize_for_detection(original_pil, DETECTION_MAX_DIM)
        detections = detector.detect(np.array(detection_pil))

        draw = ImageDraw.Draw(original_pil)
        try:
            font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 20)
        except Exception:
            font = ImageFont.load_default()

        results_text = []

        for det in detections:
            label = det["class"]
            score = det.get("score", 0)
            if score < MIN_CONFIDENCE:
                continue

            if label not in ["FEMALE_BREAST_EXPOSED", "FEMALE_GENITALIA_EXPOSED"]:
                continue

            x, y, w, h = [int(v * scale) for v in det["box"]]
            crop_pil = original_pil.crop((x, y, x + w, y + h))

            if label == "FEMALE_BREAST_EXPOSED":
                desc = describe_breast_precise(crop_pil)
                color = (255, 46, 130)
            else:
                desc = describe_vagina_precise(crop_pil)
                color = (138, 43, 226)

            draw.rectangle([x, y, x + w, y + h], outline=color, width=4)
            text_pos = (x, y - 25 if y > 25 else y + h)
            draw.text(text_pos, desc, fill=color, font=font)
            results_text.append(desc)

        if not results_text:
            draw.text((10, 10), "Keine relevanten Bereiche erkannt.", fill=(255, 0, 0), font=font)

        return original_pil

    except Exception as e:
        print(f"Fehler: {e}")
        return None

def analyze_all(files):
    if not files:
        return [], []

    processed_images = []
    output_files = []

    output_dir = os.path.join(tempfile.gettempdir(), "gradio_analyzer_outputs")
    os.makedirs(output_dir, exist_ok=True)

    for f in files:
        res = process_image(f.name)
        if res is None:
            continue

        processed_images.append(res)

        base_name = os.path.splitext(os.path.basename(f.name))[0]
        out_path = os.path.join(output_dir, f"{base_name}_analyzed.png")
        res.save(out_path)
        output_files.append(out_path)

    return processed_images, output_files

custom_css = """
body { background: #0f0f1a; color: #e0e0ff; }
.gradio-container { max-width: 1000px !important; margin: auto; }
h1 { color: #ff2e82; text-align: center; }
"""

with gr.Blocks() as demo:
    gr.Markdown("# 👙 Automatischer Nackt-Analyzer")
    gr.Markdown("Lade Bilder hoch für eine automatische Analyse. Die Ergebnisse werden im Bild angezeigt und zusätzlich als Dateien bereitgestellt.")

    with gr.Row():
        input_files = gr.File(file_count="multiple", label="Bilder hochladen")

    with gr.Row():
        output_gallery = gr.Gallery(
            label="Analyse-Ergebnisse",
            columns=2,
            height="auto"
        )

    with gr.Row():
        output_downloads = gr.File(
            label="Analysierte Bilder herunterladen",
            file_count="multiple"
        )

    input_files.change(
        fn=analyze_all,
        inputs=input_files,
        outputs=[output_gallery, output_downloads]
    )

if __name__ == "__main__":
    demo.launch(
        css=custom_css,
        theme=gr.themes.Soft(primary_hue="pink")
    )