Create app.py

app.py

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+from IPython import get_ipython
+from IPython.display import display
+from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
+from PIL import Image
+import requests
+import matplotlib.pyplot as plt
+import torch.nn as nn
+import torch
+from torchvision import transforms
+from transformers import SamModel, SamProcessor
+from diffusers import AutoPipelineForInpainting
+from diffusers.utils import load_image, make_image_grid
+from google.colab import drive
+drive.mount('/content/drive')
+
+
+def modify_image(image_url, prompt, mask_id=4):
+
+  processor = SegformerImageProcessor.from_pretrained("sayeed99/segformer_b3_clothes")
+  model = AutoModelForSemanticSegmentation.from_pretrained("sayeed99/segformer_b3_clothes")
+
+  image = Image.open(image_url)
+  inputs = processor(images=image, return_tensors="pt")
+
+  outputs = model(**inputs)
+  logits = outputs.logits.cpu()
+
+  upsampled_logits = nn.functional.interpolate(
+      logits,
+      size=image.size[::-1],
+      mode="bilinear",
+      align_corners=False,
+  )
+
+  pred_seg = upsampled_logits.argmax(dim=1)[0]
+
+  mask = (pred_seg == mask_id).numpy()
+  mask_image = Image.fromarray((mask * 255).astype('uint8'))
+
+  pipeline = AutoPipelineForInpainting.from_pretrained(
+      "redstonehero/ReV_Animated_Inpainting",
+      torch_dtype=torch.float16)
+
+  pipeline.enable_model_cpu_offload()
+
+  image1 = pipeline(prompt=prompt,
+                  num_inference_steps=24,
+                  image=image,
+                  mask_image=mask_image,
+                  guidance_scale=3,
+                  strength=1.0).images[0]
+
+  return make_image_grid([image1], rows = 1, cols = 1)
+
+
+
+import gradio as gr
+
+def gradio_wrapper(image, prompt, choice):
+  return modify_image(image, prompt, int(choice))
+
+demo = gr.Interface(
+    fn=gradio_wrapper,
+    inputs=[
+        gr.Image(type="filepath"),  # Change gr.inputs.Image to gr.Image
+        gr.Textbox(label="Prompt"),
+        gr.Radio(["4", "5", "6"], label="Mask ID")
+    ],
+    outputs=gr.Image()
+)
+
+demo.launch(inline=False)