Update app.py

app.py

@@ -17,7 +17,7 @@ CLIP_MODEL_NAME = "openai/clip-vit-base-patch32"
 
 # FastSAM
 # *Corrected* HuggingFace link for the weights
-FASTSAM_WEIGHTS_URL = "https://github.com/An-619/FastSAM/releases/download/v1.0/FastSAM-s.pt"
+FASTSAM_WEIGHTS_URL = "https://huggingface.co/spaces/An-619/FastSAM/resolve/6f76f474c656d2cb29599f49c296a8784b02d04b/weights/FastSAM-s.pt"
 FASTSAM_WEIGHTS_NAME = "FastSAM-s.pt"
 
 # Default FastSAM parameters
@@ -120,7 +120,7 @@ def process_image_fastsam(image, imgsz, conf, iou, retina_masks):
         # Check if results are valid
         if results is None or len(results) == 0 or results[0] is None:
           return None, "FastSAM did not return valid results. Try adjusting parameters or using a different image."
-            
+
         # Get detections
         detections = sv.Detections.from_ultralytics(results[0])
           # Check if detections are valid
@@ -155,7 +155,6 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
     This demo combines two powerful AI models:
     - **CLIP**: For zero-shot image classification
     - **FastSAM**: For automatic image segmentation
-
     Try uploading an image and use either of the tabs below!
     """)
 
@@ -187,7 +186,7 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
                 conf_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=DEFAULT_CONFIDENCE, label="Confidence Threshold")
                 iou_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=DEFAULT_IOU, label="IoU Threshold")
                 retina_checkbox = gr.Checkbox(label="Retina Masks", value=DEFAULT_RETINA_MASKS)
-            
+
         with gr.Row():
           image_output = gr.Image(label="Segmentation Result")
           error_output = gr.Textbox(label="Error Message", type="text") # Added for displaying errors
@@ -212,7 +211,6 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
     ### How to use:
     1. **CLIP Classification**: Upload an image and enter text to check if that concept exists in the image
     2. **FastSAM Segmentation**: Upload an image to get automatic segmentation with bounding boxes and masks
-
     ### Note:
     - The models run on CPU by default, so processing might take a few seconds. If you have a GPU, it will be used automatically.
     - For best results, use clear images with good lighting.