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Browse files- app.py +71 -0
- requirements.txt +117 -0
app.py
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import gradio as gr
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import cv2
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import numpy as np
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import insightface
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from insightface.app import FaceAnalysis
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import datetime
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import os
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from PIL import Image
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def faceswapper(user_image, result_image, username="test"):
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output_folder = 'outputs'
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# Convert PIL images to NumPy arrays for processing
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guest_img = np.array(user_image)
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result_img = np.array(result_image)
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# Convert RGB (PIL) to BGR (OpenCV)
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guest_img = guest_img[:, :, ::-1]
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result_img = result_img[:, :, ::-1]
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# Initialize the FaceAnalysis app
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app = FaceAnalysis(name='buffalo_l')
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app.prepare(ctx_id=0, det_size=(640, 640))
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# Initialize the face swapper model
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swapper = insightface.model_zoo.get_model('inswapper_128.onnx', download=False, download_zip=False)
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# Detect face in the guest image
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guest_faces = app.get(guest_img)
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guest_face = guest_faces[0]
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# Detect faces in the result image
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faces = app.get(result_img)
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# Perform face swapping
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for face in faces:
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result_img = swapper.get(result_img, face, guest_face, paste_back=True)
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# Save the result in the specified output folder
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if not os.path.exists(output_folder):
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os.makedirs(output_folder)
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current_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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output_path = os.path.join(output_folder, f'{username}_swapped_face_{current_time}.jpg')
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cv2.imwrite(output_path, result_img)
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# Convert the final image from BGR to RGB before returning
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result_img = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)
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# Convert back to PIL image
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result_img_pil = Image.fromarray(result_img)
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original_size = result_image.size
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result_img_pil = result_img_pil.resize(original_size, Image.Resampling.LANCZOS)
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return result_img_pil
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column():
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name = gr.Textbox(label="이름(파일저장용)")
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with gr.Row():
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user_image_input = gr.Image(type="pil", label="유저사진(얼굴추출)", width=300, height=300)
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result_image_input = gr.Image(type="pil", label="결과물 사진", width=300, height=300)
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swap_btn = gr.Button("Swap Faces")
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with gr.Column():
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output_image = gr.Image(label="합성 후 사진")
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swap_btn.click(fn=faceswapper, inputs=[user_image_input, result_image_input, name], outputs=output_image)
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demo.launch(debug=True)
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requirements.txt
ADDED
@@ -0,0 +1,117 @@
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aiofiles==23.2.1
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albumentations==1.3.1
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altair==5.2.0
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annotated-types==0.6.0
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anyio==4.2.0
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appnope @ file:///home/conda/feedstock_root/build_artifacts/appnope_1649077682618/work
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asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1698341106958/work
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attrs==23.2.0
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certifi==2023.11.17
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charset-normalizer==3.3.2
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click==8.1.7
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colorama==0.4.6
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coloredlogs==15.0.1
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comm @ file:///home/conda/feedstock_root/build_artifacts/comm_1704278392174/work
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contourpy==1.2.0
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cycler==0.12.1
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Cython==3.0.7
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debugpy @ file:///Users/runner/miniforge3/conda-bld/debugpy_1695534448458/work
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decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work
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easydict==1.11
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exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1700579780973/work
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executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1698579936712/work
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fastapi==0.108.0
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ffmpy==0.3.1
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filelock==3.13.1
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flatbuffers==23.5.26
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fonttools==4.47.0
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fsspec==2023.12.2
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gradio==4.13.0
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gradio_client==0.8.0
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h11==0.14.0
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httpcore==1.0.2
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httpx==0.26.0
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huggingface-hub==0.20.2
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humanfriendly==10.0
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idna==3.6
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imageio==2.33.1
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importlib-metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1703269254275/work
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importlib-resources==6.1.1
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insightface==0.7.3
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ipykernel @ file:///Users/runner/miniforge3/conda-bld/ipykernel_1703631823913/work
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42 |
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ipython @ file:///home/conda/feedstock_root/build_artifacts/ipython_1703263236586/work
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43 |
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jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1696326070614/work
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44 |
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Jinja2==3.1.2
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joblib==1.3.2
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jsonschema==4.20.0
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jsonschema-specifications==2023.12.1
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48 |
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jupyter_client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1699283905679/work
|
49 |
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jupyter_core @ file:///Users/runner/miniforge3/conda-bld/jupyter_core_1704321426011/work
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kiwisolver==1.4.5
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51 |
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lazy_loader==0.3
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markdown-it-py==3.0.0
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53 |
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MarkupSafe==2.1.3
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54 |
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matplotlib==3.8.2
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55 |
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matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1660814786464/work
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56 |
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mdurl==0.1.2
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57 |
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mpmath==1.3.0
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58 |
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nest-asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1697083700168/work
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59 |
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networkx==3.2.1
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numpy==1.26.3
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onnx==1.15.0
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onnxruntime==1.16.3
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63 |
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opencv-python-headless==4.9.0.80
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64 |
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orjson==3.9.10
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65 |
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packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1696202382185/work
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66 |
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pandas==2.1.4
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parso @ file:///home/conda/feedstock_root/build_artifacts/parso_1638334955874/work
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68 |
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pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1667297516076/work
|
69 |
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pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1602536217715/work
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70 |
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pillow==10.2.0
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71 |
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platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1701708255999/work
|
72 |
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prettytable==3.9.0
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73 |
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prompt-toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1702399386289/work
|
74 |
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protobuf==4.25.1
|
75 |
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psutil @ file:///Users/runner/miniforge3/conda-bld/psutil_1702833169958/work
|
76 |
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ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl
|
77 |
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pure-eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1642875951954/work
|
78 |
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pydantic==2.5.3
|
79 |
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pydantic_core==2.14.6
|
80 |
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pydub==0.25.1
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81 |
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Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1700607939962/work
|
82 |
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pyparsing==3.1.1
|
83 |
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python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1626286286081/work
|
84 |
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python-multipart==0.0.6
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85 |
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pytz==2023.3.post1
|
86 |
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PyYAML==6.0.1
|
87 |
+
pyzmq @ file:///Users/runner/miniforge3/conda-bld/pyzmq_1701783254483/work
|
88 |
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qudida==0.0.4
|
89 |
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referencing==0.32.1
|
90 |
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requests==2.31.0
|
91 |
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rich==13.7.0
|
92 |
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rpds-py==0.16.2
|
93 |
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scikit-image==0.22.0
|
94 |
+
scikit-learn==1.3.2
|
95 |
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scipy==1.11.4
|
96 |
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semantic-version==2.10.0
|
97 |
+
shellingham==1.5.4
|
98 |
+
six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work
|
99 |
+
sniffio==1.3.0
|
100 |
+
stack-data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1669632077133/work
|
101 |
+
starlette==0.32.0.post1
|
102 |
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sympy==1.12
|
103 |
+
threadpoolctl==3.2.0
|
104 |
+
tifffile==2023.12.9
|
105 |
+
tomlkit==0.12.0
|
106 |
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toolz==0.12.0
|
107 |
+
tornado @ file:///Users/runner/miniforge3/conda-bld/tornado_1695373570914/work
|
108 |
+
tqdm==4.66.1
|
109 |
+
traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1704212992681/work
|
110 |
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typer==0.9.0
|
111 |
+
typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1702176139754/work
|
112 |
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tzdata==2023.4
|
113 |
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urllib3==2.1.0
|
114 |
+
uvicorn==0.25.0
|
115 |
+
wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1700607916581/work
|
116 |
+
websockets==11.0.3
|
117 |
+
zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1695255097490/work
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