Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+from transformers import AutoProcessor, CLIPSegForImageSegmentation
+
+# Load the CLIPSeg model and processor
+processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
+model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
+
+def segment_image(input_image, text_prompt):
+    # Preprocess the image
+    inputs = processor(text=[text_prompt], images=[input_image], padding="max_length", return_tensors="pt")
+
+    # Perform segmentation
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # Get the predicted segmentation
+    preds = outputs.logits.squeeze().sigmoid()
+
+    # Convert the prediction to a PIL image
+    segmentation = (preds > 0.5).float()
+    segmentation_image = Image.fromarray((segmentation.numpy() * 255).astype(np.uint8))
+
+    return segmentation_image
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=segment_image,
+    inputs=[
+        gr.Image(type="pil", label="Input Image"),
+        gr.Textbox(label="Text Prompt", placeholder="Enter a description of what to segment...")
+    ],
+    outputs=gr.Image(type="pil", label="Segmentation Result"),
+    title="CLIPSeg Image Segmentation",
+    description="Upload an image and provide a text prompt to segment objects.",
+    examples=[
+        ["path/to/example_image1.jpg", "car"],
+        ["path/to/example_image2.jpg", "person"],
+    ]
+)
+
+# Launch the interface
+iface.launch()