Update app.py

app.py

@@ -18,7 +18,7 @@ def process_image(image, prompts):
     preds = outputs.logits.unsqueeze(1)
   
   filename = f"mask.png"
-  plt.imsave(filename,torch.sigmoid(preds[1][0]))
+  plt.imsave(filename,torch.sigmoid(preds[0][0]))
   
   img2 = cv2.imread(filename)
   gray_image = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
@@ -34,7 +34,7 @@ title = "Interactive demo: zero-shot image segmentation with CLIPSeg"
 description = "Demo for using CLIPSeg, a CLIP-based model for zero- and one-shot image segmentation. To use it, simply upload an image and add a text to mask (identify in the image), or use one of the examples below and click 'submit'. Results will show up in a few seconds."
 article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.10003'>CLIPSeg: Image Segmentation Using Text and Image Prompts</a> | <a href='https://huggingface.co/docs/transformers/main/en/model_doc/clipseg'>HuggingFace docs</a></p>"
 
-examples = [["example_image.png", "a glass", "something to fill", "wood", "a jar"]]
+examples = [["example_image.png", "wood"]]
    
 interface = gr.Interface(fn=process_image, 
                      inputs=[gr.Image(type="pil"), gr.Textbox(label="What do you want to identify (separated by comma)?")],