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Update app.py
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app.py
CHANGED
@@ -1,9 +1,6 @@
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# Yolo (before mod-spaces)
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from ultralytics import YOLO
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# UI and Application Framework
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import gradio as gr
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import spaces
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# Standard Libraries
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@@ -19,18 +16,23 @@ from PIL import Image
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import torch
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from transformers import pipeline
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from diffusers import AutoPipelineForInpainting
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# Text and Data Manipulation
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import difflib
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yoloModel = YOLO('yolov8x-seg.pt')
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sdxl = AutoPipelineForInpainting.from_pretrained(
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"diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
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torch_dtype=torch.
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)
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image_captioner = pipeline("image-to-text", model="Abdou/vit-swin-base-224-gpt2-image-captioning")
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def image_to_base64(image: Image.Image):
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@@ -54,7 +56,7 @@ def get_most_similar_string(target_string, string_array):
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# Yolo
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def getClasses(model, img1):
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results = model([np.array(img1)])
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out = []
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for r in results:
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im_array = r.plot()
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@@ -106,7 +108,6 @@ def getSegments(yoloModel, img1):
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return allMask
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# Gradio UI
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@spaces.GPU
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def captionMaker(base64_img):
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@@ -165,4 +166,4 @@ iface = gr.Interface(
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live=False
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)
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iface.launch()
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# UI and Application Framework
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import gradio as gr
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import spaces
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# Standard Libraries
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import torch
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from transformers import pipeline
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from diffusers import AutoPipelineForInpainting
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from ultralytics import YOLO
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# Text and Data Manipulation
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import difflib
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# Constants
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DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
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# Load
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DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
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yoloModel = YOLO('yolov8x-seg.pt')
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sdxl = AutoPipelineForInpainting.from_pretrained(
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"diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
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torch_dtype=torch.float32
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).to(DEVICE)
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image_captioner = pipeline("image-to-text", model="Abdou/vit-swin-base-224-gpt2-image-captioning", device=DEVICE)
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def image_to_base64(image: Image.Image):
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# Yolo
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def getClasses(model, img1):
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results = model([np.array(img1)]) # Изменение для передачи изображения как массива NumPy
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out = []
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for r in results:
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im_array = r.plot()
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return allMask
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# Gradio UI
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@spaces.GPU
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def captionMaker(base64_img):
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live=False
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)
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iface.launch()
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