Update app.py

app.py

@@ -1,21 +1,4 @@
 import gradio as gr
-def segment(image):
-    pass  # Implement your image segmentation model here...
-from PIL import Image
-import requests
-
-from transformers import CLIPProcessor, CLIPModel
-
-model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
-processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-
-#url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-image = Image.open(requests.get(url, stream=True).raw)
-
-inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
-
-outputs = model(**inputs)
-logits_per_image = outputs.logits_per_image # this is the image-text similarity score
-probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
+#Salesforce/BLIP
 
 gr.Interface(fn=segment, inputs="image", outputs="image").launch()