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import gradio as gr
from transformers import pipeline
from PIL import Image
import os
def load_images_from_current_directory():
images = []
current_directory = os.getcwd()
for filename in os.listdir(current_directory):
if filename.endswith(".jpg") or filename.endswith(".png"):
img_path = os.path.join(current_directory, filename)
img = Image.open(img_path)
if img is not None:
images.append(img)
return images
# Example: Load images from the current directory
example_images = load_images_from_current_directory()
# Define the image classification function
def classify_image(image):
try:
# Convert the Gradio image input (which is a NumPy array) to a PIL image
image = Image.fromarray(image)
# Create the image classification pipeline
img_class = pipeline(
"image-classification", model="AMfeta99/vit-base-oxford-brain-tumor"
)
# Perform image classification
results = img_class(image)
# Find the result with the highest score
max_score_result = max(results, key=lambda x: x['score'])
# Extract the predicted label
predictions = max_score_result['label']
if predictions==1:
text_pred='Tumor'
else:
text_pred='Normal'
return text_pred
except Exception as e:
# Handle any errors that occur during classification
return f"Error: {str(e)}"
# Define the Gradio interface
image = gr.Image()
label = gr.Label(num_top_classes=1)
title = "Brain Tumor X-ray Classification"
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."
article = "<p style='text-align: center'>Image Classification | Demo Model</p>"
# Prepare examples with loaded images
examples = []
for img in example_images:
examples.append([np.array(img), os.path.basename(os.path.splitext(img.filename)[0])])
demo = gr.Interface(fn=classify_image, inputs=image, outputs=label, description=description, article=article, title=title, examples=examples)
# Launch the Gradio interface
demo.launch() |