Create app.py

app.py

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+from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
+import gradio as gr
+from PIL import Image
+import matplotlib.pyplot as plt
+import torch
+import cv2
+
+
+import os
+os.system("wget https://huggingface.co/akhaliq/lama/resolve/main/best.ckpt")
+import paddlehub as hub
+import gradio as gr
+import torch
+from PIL import Image, ImageOps
+import numpy as np
+import imageio
+os.mkdir("data")
+os.rename("best.ckpt", "models/best.ckpt")
+os.mkdir("dataout")
+
+# Load CLIPSeg model
+processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
+clipseg_model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
+
+# Load LAMA model
+lama_model = hub.Module(name='U2Net')
+
+def process_image(image, prompt):
+    # Generate mask with CLIPSeg
+    inputs = processor(text=prompt, images=image, padding="max_length", return_tensors="pt")
+    with torch.no_grad():
+        outputs = clipseg_model(**inputs)
+        preds = outputs.logits
+    plt.imsave("mask.png", torch.sigmoid(preds))
+    mask_image = Image.open("mask.png").convert("RGB")
+
+    # Convert image to BGR format
+    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    # Convert mask to grayscale format
+    mask_image = cv2.cvtColor(np.array(mask_image), cv2.COLOR_RGB2GRAY)
+
+    # Perform inpainting with LAMA
+    input_dict = {"image": image, "mask": mask_image}
+    inpainted_image = lama_model.inference(data=input_dict)["data"][0]
+    
+    inpainted_image = cv2.cvtColor(inpainted_image, cv2.COLOR_BGR2RGB)
+    inpainted_image = Image.fromarray(inpainted_image)
+
+    return mask_image, inpainted_image
+
+interface = gr.Interface(fn=process_image, 
+                     inputs=[gr.Image(type="pil"), gr.Textbox(label="Please describe what you want to identify")],
+                     outputs=[gr.Image(type="pil"), gr.Image(type="pil")],
+                     title="Interactive demo: zero-shot image segmentation with CLIPSeg and inpainting with LAMA",
+                     description="Demo for using CLIPSeg and LAMA to perform zero- and one-shot image segmentation and inpainting. 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.",
+                     examples=[["example_image.png", "wood"]])
+
+interface.launch(debug=True)