Create app.py

app.py

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+from PIL import Image
+import numpy as np
+import gradio as gr
+import os
+import cv2
+from nudenet import NudeDetector
+import io
+import concurrent.futures
+
+# ─── Konstanten ────────────────────────────────────────
+DETECTION_MAX_DIM    = 768
+PIXELS_PER_CM_ESTIMATE = 15
+MIN_CONFIDENCE       = 0.45
+
+# ─── Hilfsfunktionen ───────────────────────────────────
+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)
+    return resized, 1 / ratio
+
+# Deine bestehenden describe-Funktionen (unverändert)
+def describe_breast_precise(crop_pil): 
+    # ... dein Code bleibt gleich ...
+    return "Form: Rund · Größe: mittel · Nippel: Sichtbar · 9.8×8.4 cm"   # Beispiel
+
+def describe_vagina_precise(crop_pil): 
+    # ... dein Code bleibt gleich ...
+    return "Form: Klassisches Outie · Größe: mittel · Prominenz: sichtbar · Behaart: minimal · 8.2×11.1 cm"
+
+detector = NudeDetector(inference_resolution=640)
+
+def process_single_image(mode: str, path: str):
+    try:
+        original_pil = Image.open(path).convert("RGB")
+        detection_pil, scale = resize_for_detection(original_pil, DETECTION_MAX_DIM)
+        
+        detections = detector.detect(np.array(detection_pil))
+        
+        target_class = "FEMALE_BREAST_EXPOSED" if mode == "Brüste" else "FEMALE_GENITALIA_EXPOSED"
+        relevant = [d for d in detections if d["class"] == target_class and d.get("score", 0) >= MIN_CONFIDENCE]
+        
+        filename = os.path.basename(path)
+        markdown = f"**{filename}** — {mode}\n\n"
+        
+        outputs = []
+        
+        if not relevant:
+            markdown += "❌ Keine relevanten Bereiche erkannt.\n"
+            outputs.append((None, markdown))
+            return outputs
+        
+        markdown += f"✅ **{len(relevant)}** {mode.lower()} gefunden\n\n"
+        
+        for i, det in enumerate(relevant, 1):
+            x, y, w, h = [int(v * scale) for v in det["box"]]
+            crop_pil = original_pil.crop((x, y, x + w, y + h))
+            
+            desc = describe_breast_precise(crop_pil) if mode == "Brüste" else describe_vagina_precise(crop_pil)
+            
+            # Crop als bytes für Gradio Image-Komponente
+            crop_bytes = io.BytesIO()
+            crop_pil.save(crop_bytes, format="PNG")
+            crop_bytes.seek(0)
+            
+            markdown_part = f"**{mode} {i}**  (Konfidenz: {det['score']:.2f})\n{desc}\n"
+            
+            outputs.append((crop_bytes, markdown_part))
+        
+        # Zusammenfassung am Ende
+        summary_md = "\n".join([md for _, md in outputs]) + f"\n\n**Gesamt: {len(relevant)} Funde**"
+        outputs.append((None, summary_md))
+        
+        return outputs
+    
+    except Exception as e:
+        return [(None, f"**{filename}** — Fehler: {str(e)}")]
+
+def analyze_images(mode: str, image_paths):
+    if not image_paths:
+        return [(None, "**Keine Bilder hochgeladen.**")]
+    
+    all_outputs = []
+    
+    max_workers = min(6, len(image_paths), os.cpu_count() or 4)
+    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+        futures = [executor.submit(process_single_image, mode, p) for p in image_paths]
+        for future in concurrent.futures.as_completed(futures):
+            all_outputs.extend(future.result())
+    
+    return all_outputs
+
+# ─── Gradio Interface ──────────────────────────────────
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="pink", secondary_hue="purple")) as demo:
+    gr.Markdown("# Nackt-Analyzer – mit Crops")
+    gr.Markdown("Lädt Bilder → erkennt Brüste / Vulva → zeigt **Text + Crop-Bilder**")
+    
+    with gr.Row():
+        mode = gr.Radio(choices=["Brüste", "Vagina"], value="Brüste", label="Modus")
+        upload = gr.File(file_count="multiple", file_types=["image"], label="Bilder hochladen")
+    
+    analyze_btn = gr.Button("Analysieren", variant="primary")
+    
+    output_gallery = gr.Gallery(
+        label="Ergebnisse (Crops + Beschreibung)",
+        columns=3,
+        height="auto",
+        object_fit="contain",
+        show_label=True,
+        elem_id="result-gallery"
+    )
+    
+    markdown_output = gr.Markdown(label="Zusammenfassung / Details")
+    
+    def on_analyze(mode, files):
+        if not files:
+            return [], "**Keine Dateien ausgewählt.**"
+        
+        paths = [f.name for f in files] if hasattr(files[0], 'name') else files
+        results = analyze_images(mode, paths)
+        
+        images = []
+        md_parts = []
+        
+        for img_bytes, text in results:
+            if img_bytes is not None:
+                images.append((img_bytes, text))   # (image, caption)
+            else:
+                md_parts.append(text)
+        
+        combined_md = "\n\n".join(md_parts) if md_parts else ""
+        
+        return images, combined_md
+    
+    analyze_btn.click(
+        fn=on_analyze,
+        inputs=[mode, upload],
+        outputs=[output_gallery, markdown_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)