Update app.py

app.py

@@ -1,39 +1,47 @@
 import gradio as gr
-from transformers import SamModel, SamProcessor
+from transformers import AutoProcessor, SamModel
 from PIL import Image
 import torch
+import numpy as np
+import traceback
 
-# Kleineres Modell (läuft stabiler auf CPU)
+# CPU-freundliches Modell
 model_id = "facebook/sam-vit-base"
-processor = SamProcessor.from_pretrained(model_id)
+processor = AutoProcessor.from_pretrained(model_id)
 model = SamModel.from_pretrained(model_id)
 
 def segment_image(image):
-    # Sicherstellen, dass CPU verwendet wird
-    device = torch.device("cpu")
-    model.to(device)
-    inputs = processor(images=image, return_tensors="pt").to(device)
+    try:
+        device = torch.device("cpu")
+        model.to(device)
+        inputs = processor(images=image, return_tensors="pt").to(device)
 
-    with torch.no_grad():
-        outputs = model(**inputs)
+        with torch.no_grad():
+            outputs = model(**inputs)
 
-    # Masken ausgeben
-    masks = processor.post_process_masks(
-        outputs,
-        original_sizes=[image.size[::-1]],
-        reshaped_input_sizes=[image.size[::-1]]
-    )[0]
+        masks = processor.post_process_masks(
+            outputs=outputs,
+            original_sizes=[image.size[::-1]],
+            reshaped_input_sizes=[image.size[::-1]]
+        )
 
-    mask_array = (masks[0][0].cpu().numpy() * 255).astype("uint8")
-    mask_image = Image.fromarray(mask_array)
-    return mask_image
+        if len(masks) == 0 or "masks" not in masks[0]:
+            return "Keine Maske erkannt."
+
+        mask_array = masks[0]["masks"][0][0].cpu().numpy()
+        mask_image = Image.fromarray((mask_array * 255).astype(np.uint8))
+        return mask_image
+
+    except Exception as e:
+        # Volle Fehlerausgabe anzeigen
+        return f"Fehler:\n{traceback.format_exc()}"
 
 demo = gr.Interface(
     fn=segment_image,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="pil"),
-    title="FishBoost Segment Anything (Meta SAM 2 Demo)",
-    description="Upload an image and get the segmented result using Meta’s SAM model (CPU compatible)."
+    inputs=gr.Image(type="pil", label="Upload your fish image"),
+    outputs="text",
+    title="FishBoost Segment Anything (Meta SAM CPU Debug)",
+    description="Debug-Version: Zeigt genaue Fehlermeldung bei Problemen mit SAM."
 )
 
 demo.launch()