Delete app.py

app.py

@@ -1,60 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-import cv2
-from PIL import Image
-import os
-import urllib.request
-
-# SAM importieren
-try:
-    from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
-except ImportError:
-    raise ImportError("Bitte stelle sicher, dass 'segment-anything' in requirements.txt steht.")
-
-# Modellpfad & Download-Link
-MODEL_URL = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth"
-MODEL_PATH = "sam_vit_b_01ec64.pth"
-
-# Modell automatisch laden, falls nicht vorhanden
-if not os.path.exists(MODEL_PATH):
-    print("Modell wird heruntergeladen...")
-    urllib.request.urlretrieve(MODEL_URL, MODEL_PATH)
-    print("Download abgeschlossen.")
-
-# SAM laden
-model_type = "vit_b"
-device = "cuda" if torch.cuda.is_available() else "cpu"
-sam = sam_model_registry[model_type](checkpoint=MODEL_PATH)
-sam.to(device=device)
-
-mask_generator = SamAutomaticMaskGenerator(sam)
-
-def segment_all_objects(image):
-    image_np = np.array(image)
-    masks = mask_generator.generate(image_np)
-
-    overlay = image_np.copy()
-    for i, mask in enumerate(masks):
-        m = mask["segmentation"]
-        color = np.random.randint(0, 255, size=(3,))
-        overlay[m] = overlay[m] * 0.3 + color * 0.7
-
-        # Text auf Maske
-        y, x = np.where(m)
-        if len(x) > 0 and len(y) > 0:
-            cx, cy = int(np.mean(x)), int(np.mean(y))
-            cv2.putText(overlay, f"Obj {i+1}", (cx, cy),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
-
-    return Image.fromarray(overlay.astype(np.uint8))
-
-demo = gr.Interface(
-    fn=segment_all_objects,
-    inputs=gr.Image(type="pil", label="Bild hochladen"),
-    outputs=gr.Image(type="pil", label="Segmentiertes Ergebnis"),
-    title="FishBoost SAM2 Segmentierung",
-    description="Automatische Segmentierung aller Objekte im Bild (Meta SAM). CPU-kompatibel."
-)
-
-demo.launch()