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add examples samples
Browse files
app.py
CHANGED
@@ -1,29 +1,49 @@
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import gradio as gr
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from transformers import pipeline
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from PIL import Image
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# Define the image classification function
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def classify_image(image):
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try:
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# Convert the Gradio image input (which is a NumPy array) to a PIL image
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image = Image.fromarray(image)
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# Create the image classification pipeline
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img_class = pipeline(
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"image-classification", model="AMfeta99/vit-base-oxford-brain-tumor"
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)
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# Perform image classification
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results = img_class(image)
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# Find the result with the highest score
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max_score_result = max(results, key=lambda x: x['score'])
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# Extract the predicted label
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predictions = max_score_result['label']
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except Exception as e:
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# Handle any errors that occur during classification
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return f"Error: {str(e)}"
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@@ -34,7 +54,14 @@ label = gr.Label(num_top_classes=1)
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title = "Brain Tumor X-ray Classification"
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description = "Worried about whether your brain scan is normal or not? Upload your x-ray and the algorithm will give you an expert opinion. Check out [the original algorithm](https://huggingface.co/AMfeta99/vit-base-oxford-brain-tumor) that this demo is based off of."
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article = "<p style='text-align: center'>Image Classification | Demo Model</p>"
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# Launch the Gradio interface
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demo.launch(
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import gradio as gr
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from transformers import pipeline
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from PIL import Image
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import os
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def load_images_from_current_directory():
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images = []
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current_directory = os.getcwd()
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for filename in os.listdir(current_directory):
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if filename.endswith(".jpg") or filename.endswith(".png"):
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img_path = os.path.join(current_directory, filename)
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img = Image.open(img_path)
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if img is not None:
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images.append(img)
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return images
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# Example: Load images from the current directory
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example_images = load_images_from_current_directory()
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# Define the image classification function
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def classify_image(image):
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try:
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# Convert the Gradio image input (which is a NumPy array) to a PIL image
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image = Image.fromarray(image)
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# Create the image classification pipeline
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img_class = pipeline(
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"image-classification", model="AMfeta99/vit-base-oxford-brain-tumor"
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)
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# Perform image classification
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results = img_class(image)
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# Find the result with the highest score
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max_score_result = max(results, key=lambda x: x['score'])
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# Extract the predicted label
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predictions = max_score_result['label']
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if predictions==1:
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text_pred='Tumor'
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else:
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text_pred='Normal'
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return text_pred
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except Exception as e:
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# Handle any errors that occur during classification
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return f"Error: {str(e)}"
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title = "Brain Tumor X-ray Classification"
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description = "Worried about whether your brain scan is normal or not? Upload your x-ray and the algorithm will give you an expert opinion. Check out [the original algorithm](https://huggingface.co/AMfeta99/vit-base-oxford-brain-tumor) that this demo is based off of."
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article = "<p style='text-align: center'>Image Classification | Demo Model</p>"
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# Prepare examples with loaded images
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examples = []
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for img in example_images:
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examples.append([np.array(img), os.path.basename(os.path.splitext(img.filename)[0])])
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demo = gr.Interface(fn=classify_image, inputs=image, outputs=label, description=description, article=article, title=title, examples=examples)
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# Launch the Gradio interface
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demo.launch()
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