Delete app.py

import gradio as gr
from transformers import SamModel, SamProcessor
from PIL import Image
import torch

# Kleineres Modell (läuft stabiler auf CPU)
model_id = "facebook/sam-vit-base"
processor = SamProcessor.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)

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]

mask_array = (masks[0][0].cpu().numpy() * 255).astype("uint8")
mask_image = Image.fromarray(mask_array)
return mask_image

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)."
)

demo.launch()

app.py

@@ -1,27 +0,0 @@
-import gradio as gr
-from transformers import SamModel, SamProcessor
-from PIL import Image
-import torch
-
-# SAM 2 Modell laden (von Meta)
-model_id = "facebook/sam-vit-huge"
-processor = SamProcessor.from_pretrained(model_id)
-model = SamModel.from_pretrained(model_id)
-
-def segment_image(image):
-    inputs = processor(images=image, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model(**inputs)
-    masks = processor.post_process_masks(outputs, target_sizes=[image.size[::-1]])[0]
-    mask_image = Image.fromarray((masks[0][0].cpu().numpy() * 255).astype("uint8"))
-    return mask_image
-
-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 a segmented result using Meta’s SAM 2 model."
-)
-
-demo.launch()