pkl
#2
by
MohammedAlakhras
- opened
- .gitattributes +0 -1
- 1.jpeg +0 -0
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- ChestXRay10Epoch.pkl +0 -3
- ChestXRayfine.pkl +0 -3
- README.md +2 -2
- app.py +4 -10
- requirements.txt +0 -4
.gitattributes
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@@ -32,4 +32,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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ChestXRay10Epoch.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:6859df004aded729aaf30181159885bc790a1db3017c63aca1318f2984880d79
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size 87605859
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ChestXRayfine.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:75b479e8458140da1d14a3032e4717e6c0b966942ec89e3e615a6c0af2fcd21e
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size 87585613
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README.md
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@@ -4,10 +4,10 @@ emoji: 🏃
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colorFrom: pink
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colorTo: green
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sdk: gradio
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sdk_version:
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app_file: app.py
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pinned: false
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license: apache-2.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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colorFrom: pink
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colorTo: green
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sdk: gradio
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sdk_version: 3.24.1
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app_file: app.py
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pinned: false
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license: apache-2.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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@@ -3,10 +3,6 @@ import pathlib
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plt = platform.system()
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pathlib.WindowsPath = pathlib.PosixPath
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import requests
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# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
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# %% auto 0
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import gradio as gr
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# %% app.ipynb 2
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learn = load_learner('
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# %% app.ipynb 3
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categories=('COVID19','Normal','Pneumonia','Turberculosis')
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# %% app.ipynb 4
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image=gr.Image()
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label=gr.Label()
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examples=['1.jpeg','
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'18.jpeg','19.jpeg','2.jpeg','20.jpeg','21.jpg','22.jpg','23.jpg','3.jpeg','4.jpeg','5.jpeg',
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'6.png','7.png','8.png','9.png']
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interface=gr.Interface(fn=classify_image, inputs=image ,outputs=label,examples=examples)
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plt = platform.system()
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pathlib.WindowsPath = pathlib.PosixPath
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# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
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# %% auto 0
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import gradio as gr
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# %% app.ipynb 2
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learn = load_learner('tumorClassifier.pkl')
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# %% app.ipynb 3
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categories=('COVID19','Normal','Pneumonia','Turberculosis')
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# %% app.ipynb 4
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image=gr.inputs.Image(shape=(512,512))
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label=gr.outputs.Label()
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examples=['1.jpeg', '2.jpg', '3.png','a.jpg','b.jpg','c.jpg','d.jpg','e.jpg','f.jpg']
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interface=gr.Interface(fn=classify_image, inputs=image ,outputs=label,examples=examples)
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requirements.txt
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fastai
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torch
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requests
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gradio==3.39.0
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