annanau commited on
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a9f478d
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1 Parent(s): 5146242

Update app.py

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  1. app.py +16 -6
app.py CHANGED
@@ -4,12 +4,23 @@ from tensorflow.keras.preprocessing import image
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  import numpy as np
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  from PIL import Image
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  from keras import layers
 
 
 
 
 
 
 
 
 
 
 
 
 
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- # Load your trained Xception model
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- model = tf.keras.models.load_model("xception-head")
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  # Define the labels for your classification
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- class_labels = ['fresh', 'early decay', 'advanced decay','skeletonized'] # Replace with your actual class names
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  def classify_image(img):
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  # Preprocess the image to fit the model input shape
@@ -31,12 +42,11 @@ def classify_image(img):
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  # Gradio interface
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  demo = gr.Interface(
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  fn=classify_image,
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- title="Human Decomposition Image Classification",
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- description = "Predict the stage of decay (fresh, early decay, advanced decay, or skeletonized) of a head. This is a demo of one of our human decomposition image classification <a href=\"https://huggingface.co/icputrd/megyesi_decomposition_classification/blob/main/head/xception\">models</a>.",
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  inputs=gr.Image(type="pil"),
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  outputs=[gr.Label(num_top_classes=len(class_labels)), gr.Number()],
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  live=True,
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- article = "Author: <a href=\"https://www.linkedin.com/in/anna-maria-nau/\">Anna-Maria Nau</a>"
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  )
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  if __name__ == "__main__":
 
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  import numpy as np
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  from PIL import Image
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  from keras import layers
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+ from transformers import TFAutoModelForImageClassification
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+ from transformers import AutoImageProcessor
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+
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+ # Load model#'model = tf.keras.models.load_model("xception-head")
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+
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+ # Replace with your Hugging Face model repository name
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+ model_name = "icputrd/Inception-V3-Human-Bodypart-Classifier"
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+
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+ # Load the pre-trained TensorFlow model from Hugging Face
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+ model = TFAutoModelForImageClassification.from_pretrained(model_name)
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+
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+ # Load the associated image processor (for preprocessing input images)
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+ image_processor = AutoImageProcessor.from_pretrained(model_name)
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  # Define the labels for your classification
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+ class_labels = ['arm', 'hand', 'foot', 'legs','fullbody','head','backside', 'torso', 'stake', 'plastic'] # Replace with your actual class names
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  def classify_image(img):
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  # Preprocess the image to fit the model input shape
 
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  # Gradio interface
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  demo = gr.Interface(
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  fn=classify_image,
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+ title="Human Bodypart Image Classification",
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+ description = "Predict the bodypart of huma. This is a demo of our human bodypart image <a href=\"https://huggingface.co/icputrd/Inception-V3-Human-Bodypart-Classifier">classifier</a>.",
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  inputs=gr.Image(type="pil"),
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  outputs=[gr.Label(num_top_classes=len(class_labels)), gr.Number()],
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  live=True,
 
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  )
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  if __name__ == "__main__":