update

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
+import torch
+from PIL import Image
+import numpy as np
+
+def process(input_image, prompt):
+    inputs = processor(text=prompt, images=input_image, padding="max_length", return_tensors="pt")
+    # predict
+    with torch.no_grad():
+        outputs = model(**inputs)
+    preds = torch.sigmoid(outputs.logits).squeeze().detach().cpu().numpy()
+    preds = np.where(preds > 0.5, 255, 0).astype(np.uint8)
+    preds = Image.fromarray(preds.astype(np.uint8))
+    preds = np.array(preds.resize((input_image.width, input_image.height)))
+    print(preds)
+    return preds
+    
+if __name__ == '__main__':
+    processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
+    model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
+    input_image = gr.inputs.Image(label='image', type='pil')
+    prompt = gr.Textbox(label='Prompt')
+    ips = [
+            input_image, prompt
+        ]
+    outputs = "image"
+    input_size = (256, 256)
+    output_size = (256, 256)
+    iface = gr.Interface(fn=process,
+                         inputs=ips,
+                         outputs=outputs,
+                         input_size=input_size, 
+                         output_size=output_size)
+    iface.launch()