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more example images+requirements.txt added
Browse files- .gitattributes +1 -0
- app.py +1 -1
- detect.py +0 -10
- examples/angry.jpg +0 -0
- examples/sad.jpg +0 -0
- requirements.txt +3 -0
.gitattributes
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@@ -33,3 +33,4 @@ 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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*tfevents* 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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*tfevents* filter=lfs diff=lfs merge=lfs -text
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eff_quantized.onnx filter=lfs diff=lfs merge=lfs -text
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app.py
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@@ -18,7 +18,7 @@ def predict(img):
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title = "Human Emotion Detection 😭🤣🥹"
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description = "An EfficientNet ONNX quantized feature extractor computer vision model to classify images and detect the emotion of the person in it.(Uploaded image should be of a single person)"
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article = "Full Source code from scratch can be found in the huggingface Space
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# Create examples list from "examples/" directory
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example_list = [["examples/" + example] for example in os.listdir("examples")]
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title = "Human Emotion Detection 😭🤣🥹"
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description = "An EfficientNet ONNX quantized feature extractor computer vision model to classify images and detect the emotion of the person in it.(Uploaded image should be of a single person)"
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article = "Full Source code from scratch can be found in the huggingface Space: https://huggingface.co/spaces/Victorano/human_emotion_detection"
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# Create examples list from "examples/" directory
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example_list = [["examples/" + example] for example in os.listdir("examples")]
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detect.py
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from PIL import Image
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from io import BytesIO
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import numpy as np
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def emo_router(im):
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print(f"the Image: {im}")
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image = np.array(im)
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return image
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examples/angry.jpg
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examples/sad.jpg
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requirements.txt
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numpy==1.26.3
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onnxruntime==1.18.0
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gradio==4.44.0
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